NEWS & EVENTS

Data Needs for Cardiovascular Events, Management, and Outcomes

May 26 - 27 , 2005
Hilton Washington DC/Silver Spring
Silver Spring, MD

Description

Working Group Objectives

Healthy People 2010 goals include primary and secondary prevention through risk factor reduction; improved early recognition and response to out-of-hospital cardiac arrest, acute coronary syndromes (ACS), and stroke symptoms; increases in timely reperfusion treatment; and reduced mortality from coronary heart disease and stroke. Although cardiovascular disease (CVD) remains the country?s leading cause of death, health care dollars spent, and lost productivity, a nationally representative surveillance system to track progress toward these goals does not exist. Current data collection efforts to describe ACS case presentation, treatment practices, and outcomes are limited in their scope, data collection procedures, and generalizability. Ongoing community-based surveillance studies generally have small numbers of predominantly white communities, emphasize disease incidence, and include little longitudinal follow-up. In addition, they are often restricted to in-hospital events and do not capture the evolving detection and treatment of CVD in the outpatient setting. Industry-sponsored registries collect primarily inpatient data as well, from participant hospitals that are mostly urban and academic centers. Despite these limitations, the potential exists for using and linking existing data sources and to use existing systems as models for new and more comprehensive study designs. This workshop was convened to explore these opportunities.

Obtaining high quality nationally representative CVD surveillance and outcomes data is of interest to and ideally will entail cooperation among a broad range of governmental and non-governmental entities. Recognizing this, the planning group that met to explore possible designs for a workshop to address this topic included representatives from the NHLBI, the Centers for Disease Control?s National Center for Chronic Disease Prevention and Health Promotion, and the American Heart Association, in addition to other experts in the field of CVD surveillance. The workshop itself was co-sponsored by these three entities, and the workshop presenters represented the breadth of expertise and stakeholder interest in this topic.

The objectives of the workshop were:

  1. To describe the current knowledge base provided by ongoing registries, surveys, and research studies on the incidence/prevalence of acute cardiovascular events and the clinical management and patient outcomes following these events.
  2. To describe the current data needs concerning the incidence and prevalence of acute CV events and the short- and long-term medical practice patterns, quality of care, and health outcomes following these events.
  3. To consider the appropriate study designs (including potential for linking existing data-gathering infrastructures vs. starting de novo) to address the data needs.
Read more

Recap

Recommendations

The workshop participants called for the establishment of a national surveillance system for cardiovascular disease (CVD). To that end, they emphasized the importance of:

  • Providing unique individual health identifiers to allow linkage across databases and within databases for multiple entries per patient
  • Establishing and implementing uniform data standards, electronic medical record with data transfer capability, and data interoperability

The group recommended a national CVD surveillance system with state- and local-based sampling and data-reporting process that allows these entities to leverage resources to collect more data at the state and local levels for planning purposes. They also called for the establishment of sentinel surveillance research centers that would serve to validate the data collected, provide additional in-depth individual data collection, and generate novel ideas on methodological research in this area. In short, a framework under which this national CVD surveillance system could operate involves a three-tier approach:

  • Tier 1: Systematic nation-wide reporting of CVD
  • Tier 2: Validation and collection of a limited number of standardized core measures and outcomes at selected time points
  • Tier 3: Detailed hypothesis-driven studies of these patients in specific surveillance centers

It is recognized that the establishment of a comprehensive national CVD surveillance system will likely need to be implemented in a stepwise fashion. For example, similar to strategies initially employed in cancer surveillance, state and local efforts to collect more comprehensive data than at the national level are appropriate next steps. The ultimate goal would be the standardized nationwide collection of a core set of data with additional data collected at the state and local levels tailored to the specific needs of that jurisdiction.

The workshop participants also recommended specific short- and long-term steps that can be taken to improve existing data collection and analysis efforts as well as to move the field toward a coordinated nationwide surveillance program:

Short-term:

  • Solicit proposals to
    • Enhance/expand existing electronic medical record linkage
    • Conduct studies that propose/test creative efficient CVD data collection
    • Better track out-of-hospital CVD events and outpatient care
  • Investigate how HIPAA can facilitate efforts at national surveillance
  • Begin efforts to establish a nationwide individual health ID
  • Seek more uniformity across electronic medical record system platforms
  • Enhance and promote the use of existing data collection systems. Examples include
    • Adding National Committee for Quality Assurance (NCQA) performance indicators to National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS) to better understand delivery of care
    • Adding health-related quality of life (HRQL) markers to CMS? Minimum Data Set (MDS) and CDC?s National Nursing Home Survey (NNHS)

Long-term:

  • Expand existing community-based surveillance to increase geographic and race/ethnic diversity
  • Implement nationwide individual health identification numbers to allow health record linkage
  • Seek a mandate for CVD reporting (as currently exists for some infectious diseases)
  • Implement a nation-wide CVD event surveillance system

Workshop Design

The morning and afternoon sessions of the first day of the workshop were structured to address various aspects of the above objectives. Four overview presentations described current knowledge and data needs concerning the incidence and prevalence, medical practice patterns, quality of care, and patient outcomes for sudden cardiac arrest, acute coronary syndromes, stroke, and congestive heart failure. The presentations that followed addressed opportunities and limitations of relevant existing data collection systems including federally-funded registries, various health care delivery systems, quality improvement measurement systems, and other large surveillance systems or studies. Particular operational challenges in collecting such data were also considered.

Workshop participants met in one of three small working groups in the evening session to consider appropriate study designs to address the identified data needs and to develop recommendations from one of three perspectives: hospital surveillance, out-of-hospital surveillance, or assessment of quality of care and outcomes. The working group leaders presented their groups? conclusions and recommendations the following morning for consideration and discussion among all workshop participants. The last segment of the workshop focused on developing final conclusion and recommendations.

Recommendations of Small Working Groups

A. Out of Hospital Surveillance

This working group concluded that the U.S. lacks national data on CVD incidence and selected outcomes. They described the need to track data on sudden cardiac death, stroke, acute and chronic coronary syndromes (including myocardial infarction, unstable angina, and stable angina) and heart failure. The out-of-hospital settings where data-tracking would be important include emergency medical services (EMS), offices/clinics, emergency departments, and deaths that occur outside of the healthcare system.

The group highlighted several potential barriers that prevent the collection of national CVD incidence and outcomes data. First, the U.S. does not mandate the use of unique individual health identifiers, which is important in allowing individual health information to be tracked longitudinally across healthcare systems. Second, there are inadequate standards and no interoperable electronic systems that permit linking individual data for the entire U.S. population. Having such a system would allow seamless linkage from in-hospital to out-of-hospital settings, across federal agencies, and also among other institutions. Third, no public mandate exists to report CVD data. Finally, there is a need for a national agency to be designated to coordinate and collect such data.

In order to achieve the goal of obtaining national CVD incidence and outcomes data, the group indicated that new data strategies and strategies to optimize existing systems need to be implemented. Standards-based interoperable electronic medical record (EMR) infrastructures should be accelerated to become readily available for all healthcare providers. The group recognized that as medical records become increasingly electronic, it may also be more feasible for CVD to become a reportable disease. Ongoing efforts to standardize definitions for CV diseases and outcomes should be encouraged. Once data are gathered, they should be tested and validated to assure quality and accuracy. A unique health identifier should be provided for all individuals; as technology advances, individuals may also be given health information data cards to carry with them. The group encouraged learning from other models such as those from Canada and Scandinavian countries that have successfully implemented national health identification number systems.

The group discussed 10 year expectations. By then, a coordinated, cost-effective system of CVD surveillance should be established that has core data on the national level, becoming increasingly specific at the state levels, and collecting even more detailed information at the local levels. They also envision widespread use of uniform data definitions, EMR systems, and secure data exchange standards.

The group recommended some immediate steps that can be taken now:

  • Develop an inventory and map of all existing data collection systems, including those that collect outpatient data.
  • Hold a meeting with stakeholders to develop a vision document, such as that akin to the ?EMS Agenda of the Future.? Additional outcomes from the meeting should include establishing strategies to achieve the vision and determining who should lead the effort.
  • Continue to standardize definitions for classification of disease and associated outcomes, incorporating standard vocabulary amenable to electronic capture.
  • Link current existing CVD databases from federal, state, local, and private institutions.
  • Require institutional compliance with electronic data standards (e.g., Health Level 7, National Health Information Network, and Public Health Information Network) before providing federal funding to grantees/contractors.

The long-term steps recommended by the group include the following:

  • Provide a unique healthcare identifier to individuals in the U.S.
  • Have national uniform definitions for reporting clinical data on CVD.
  • Make CVD encounters reportable as electronic medical record systems become operational.

The group recognized that implementing these changes will require various resources, some of which are fairly expensive. They emphasized that it is crucial to convey the positive return on such an investment in documenting and improving the nation?s health.

B. Hospital Surveillance

With respect to surveillance of CVD in the hospital setting, this working group unanimously agreed that there is a gap in present knowledge on nation-wide incidence data on CVD, including MI, unstable angina, stroke, heart failure (HF) and atrial fibrillation. The collected CVD incidence data should reflect national demographics (e.g. in age, sex, ethnic/racial distributions). In prioritizing data needs in light of feasibility, the group decided to focus chiefly on MI and stroke (including stroke sub-types).

Possible approaches to capture CVD incidence and outcomes data were discussed. Capitalizing on the expanding use of the EMR is attractive but presently problematic given the incomplete penetration of the use of EMR, which may introduce an unknown degree of bias to the collected data. The following databases are potential data sources: Joint Commission on Accreditation of Healthcare Organizations (JCAHO); National Hospital Discharge Survey (NHDS); and National Health and Nutrition Examination Survey (NHANES). However, it is important to underscore that unique individual identifiers are needed to derive true incidence from these various data sources.

In addition to the initial case finding steps, validation procedures relying on uniform standardized criteria are needed, which could use a sampling approach. Part of the validation procedures could include a limited number of standardized core measures including, for example, atrial fibrillation (for stroke) and other risk factors.

Case identification and outcome measurements for MI and stroke:

  • For MI, the group recommends using laboratory results of biomarkers, with the understanding that this may be problematic given false positive rates, especially with troponin. However, biomarkers are preferred over reliance either on the electrocardiogram or on physician diagnosis, which is more subjective and more prone to bias (e.g. due to lack of adherence with guidelines or under-ascertainment of post-procedure MI).
  • For stroke, the group recommends using imaging data as part of the procedures for case finding.
  • As for events after MI or stroke, the group recommended measuring a) case-fatality rates at selected time intervals and b) recurrent MI or stroke. They felt the other non-fatal outcomes were presently not feasible as part of a nation-wide system. For mortality, there is a need to rely on mortality at a fixed point in time (not in-hospital mortality given temporal declines in duration of hospital stay and likely inter-site variations).

Other disease targets include unstable angina, heart failure, and atrial fibrillation. For unstable angina, the group saw a need to track the invasive procedures percutaneous coronary interventions (PCI) and coronary artery by-pass grafting (CABG). Although it is important to also track unstable angina, they pointed out the difficulty in case definition and that validation procedures are needed before accurate data can be obtained.

The group felt that surveillance of heart failure and atrial fibrillation present unresolved challenges with regards to standardized definitions and event ascertainment. For heart failure, a major challenge remains how to define it in a standardized fashion. One option is to track heart failure with low ejection fraction, though doing so will exclude diastolic heart failure. The group suggested that perhaps heart failure could be the focus of center-specific surveillance research efforts (e.g. Veterans Administration or Kaiser). For atrial fibrillation, the group suggested collecting these data while validating stroke; otherwise, the data may be better collected in the outpatient setting. For unstable angina, a major challenge remains the need to validate the events, as discharge codes are often unreliable.

The group felt that optimizing existing systems is an important intermediate step while progressing towards nationwide mandatory reporting. The current system can be optimized by adding sites to existing surveillance programs that would increase ethnic diversity, leading to the establishment of appropriately diverse surveillance networks. The Veterans Administration?s data system appears attractive for enhancing diversity of surveillance. The group also underscored that having uniform standardized criteria was essential.

The group recommended a 3-tier approach for gathering hospital CVD data nation-wide.

  • Tier 1: Systematic nation-wide reporting of MI and stroke
  • Tier 2: Validation and collection of a limited number of standardized core measures and outcomes at selected time points
  • Tier 3: Detailed hypothesis-driven studies of these patients in specific surveillance centers

The group also called for the establishment of heart disease centers that are modeled after the U.S. national cancer registries (SEER and NPCR).

C. Assessment of Quality of Care and Outcomes

The collection of data regarding quality of care and patient outcomes should be included in a CVD surveillance program. This is because these data can inform policy makers regarding the need for, and success of, quality improvement initiatives. The translation of science into practice faces many barriers, one of which is the lack of data regarding the success of that translation process. Measurement of quality of care and patient outcomes would provide impetus to quality improvement efforts and a basis for evaluation. Better healthcare delivery and improved patient outcomes would represent a substantial positive return on the investment in a CVD surveillance system.

The working group made specific recommendations to assess quality of care and outcomes in the U.S. by optimizing existing systems applicable to the out-patient and in-patient settings. As an underlying assumption, the working group affirmed that acute coronary syndromes, stroke and heart failure were of primary interest. Time constraints prevented the group from generating specific recommendations concerning sudden cardiac arrest; however, they recognized that surveillance of the quality of care provided to, and the outcomes of, patients served by the pre-hospital care system, including the emergency medical care system and disease management programs, is also of great interest (though substantial work is required in these areas to develop, test and implement systems capable of capturing these important data, the group recommended that such developmental work be conducted and supported).

To improve data collection on outpatient quality of care, the group suggests a) expanding and optimizing CDC?s National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS), and b) making use of CMS data when medication data become available. With NAMCS and NHAMCS, the sample sizes of these surveys could be increased to provide more reliable data regarding cardiovascular conditions. A validation method is needed to ensure accuracy of the CVD data. Performance indicators, such as those from the National Committee for Quality Assurance (NCQA), could be added to the surveys. The group felt that these expansions to NAMCS and NHAMCS, if endorsed by CDC and assuming adequate funding presently available, could be accomplished within a short-term timeline. Regarding CMS data, the group pointed out the need for capturing managed care claims as well as fee-for service claims. Similar to using NAMCS and NHAMCS, data validation and inclusion of performance indicators should be considered. A limitation to using CMS data is that information is predominantly limited to those 65 years or older. The group felt that enhancing CMS data in such a manner may require a long-term timeline.

To better measure outpatient health-related quality of life (HRQL), the group suggested a) expanding and optimizing the use of the Medicare Current Beneficiary Survey (MCBS) or b), alternatively, by expanding the scope of work of CMS Quality Improvement Organizations (QIOs). Either option limits data to mostly those 65 years or older. The group suggested that MCBS be expanded to cover more HRQL measures and be linked to claims data as well as medication use. The sample size may be increased to capture cardiovascular conditions. With acceptance from CMS and assuming availability of adequate funding, the group felt that these changes could be implemented in a short-term timeline. As for expanding the QIO?s role to include collection of outpatient HRQL data from persons recently hospitalized for cardiovascular disease, the group sensed that this process would first require pilot testing to assess long-term feasibility.

As for data on inpatient quality of care and HRQL, the group cautioned against relying on voluntary databases (e.g., National Cardiovascular Data Registry, National Registry of Myocardial Infarction, Get With the Guidelines, CRUSADE, ADHERE) in the long-term. The group recommended the expansion and optimization of a) the Nationwide Inpatient Sample (NIS) database from AHRQ and/or b) CDC?s NHDS. Such data would also need validation, as with other aforementioned databases. Incident versus recurrent events should be distinguished. Performance indicators such as those from JCAHO or ACC/AHA should also be measured. Data to assess the appropriateness of procedures ordered by clinicians should also be obtained if possible. The group felt that these changes, if agreed by AHRQ and CDC, could be accomplished over a medium to long-term timeline. They also felt that inpatient HRQL should be placed as a lower priority, except possibly as a baseline. The committee recognized that inpatient HRQL is generally expected to be poor at the time of discharge, and that the assessment of transitional or outpatient HRQL (e.g., 15-day, 30-day or 90-day post discharge) may be more relevant.

To improve measurement of quality of care and HRQL during the transitional period between hospital discharge and outpatient care, the group recommended a) expanding and optimizing MCBS and, alternatively, b) expanding the scope of work of the QIOs. Once again, the age limit of 65 years or greater applies here. For MCBS, the group noted that sample size could be optimized to provide more reliable estimates of measures for beneficiaries who recently experienced CVD events, a process for data validation is needed, incident versus recurrent events could be distinguished, performance indicators such as those from NCQA could be added, and HRQL could also be included. Unlike expanding the work of QIOs, which would require a longer time line to implement, the group felt that changes to MCBS, if acceptable to CMS and assuming availability of adequate funding could be implemented in a short-term timeline.

The group also discussed long-term care settings. To measure quality of care and HRQL, the group recommends the expansion of CMS? Minimum Data Set (MDS) and CDC?s National Nursing Home Survey (NNHS). At least MDS, if not also NNHS, already captures some HRQL measurements. They suggested that these databases be optimized to include larger sample size to provide more reliable estimates among persons with CVD events, establish a process for data validation, and include performance indicators such as those from NCQA. If approved by such agencies and assuming adequate funding is available, the group felt that these systems could be optimized within a short time frame.

The group discussed other issues concerning data on CVD quality of care and HRQL. They concluded that high quality, representative data from a national perspective is currently lacking. They recommended that state-based sampling and data collection processes may enable states and local entities to leverage resources to collect more data at the state and local levels for planning purposes. They also suggested the establishment of sentinel surveillance research sites that would serve to a) validate the data collected from these national surveys or claims data, b) provide additional in-depth individual data collection, and c) generate novel ideas on methodological research in this area. They emphasized the importance of establishing and implementing uniform data standards, electronic medical record data transfer capability, and data interoperability. As for carrying out the process to obtain data on CVD quality of care or HRQL, the group also suggested that Request for Applications be created to establish various sites to a) analyze existing surveillance data, b) enhance/expand current systems, and c) develop new methods and systems. They highlighted the need for unique patient identifiers, which would allow linkage across databases. They also pointed out the need to link within the databases, in order to account for multiple entries per patient.

Additional comments provided later from members of the group who were unable to participate in the discussion include the following:
a) Utilization of the National Healthcare Quality Report and the National Healthcare Disparities Report, both of which are prepared annually by AHRQ, would be an excellent method for assuring that quality of care data on CVD are tracked annually, both overall and by race/ethnicity/socioeconomic status.
b) Further investigations are needed to assess how HIPAA can be used, and modified if necessary, to facilitate efforts at national surveillance of CVD.

Speaker List

Gregory L Burke, M.D., M.S. (Co-Chair)
Professor and Chair
Department of Public Health Sciences
Wake Forest University School of Medicine
Medical Center Blvd.
Winston-Salem, NC 27157
gburke@wfubmc.edu

Rose Marie Robertson, M.D. (Co-Chair)
Chief Science Officer
American Heart Association
7272 Greenville Avenue
Dallas TX 75231-4596
rosemarie.robertson@heart.org

Claire Broome, M.D.
Senior Advisor to the Director
Integrated Health Information Systems
Centers for Disease Control and Prevention
2960 Brandywine Road, Mailstop D68
Atlanta, GA 30341
cbroome@cdc.gov

Helen Burstin, M.D., M.P.H.
Director, Center for Primary Care,
Prevention and Clinical Partnerships
Agency for Healthcare Research and Quality
6th Floor, Room 6028
540 Gaither Road
Rockville, MD 20850
hburstin@ahrq.gov

David C. Goff, Jr., M.D., Ph.D.
Professor, Section on Epidemiology and Internal Medicine
Public Health Sciences and Internal Medicine
Wake Forest University Health Sciences
Medical Center Blvd.
Winston Salem, NC 27157-1063
dgoff@wfubmc.edu

Robert Goldberg, Ph.D.
Professor of Medicine and Epidemiology
Division of Cardiovascular Medicine
University of Massachusetts Medical School
55 Lake Avenue North
Worcester, MA 01655
goldberr@ummhc.org

Virginia J. Howard
Assistant Professor of Epidemiology
School of Public Health
University of Alabama at Birmingham
210F Ryals Public Health Building
1665 University Blvd.
Birmingham, AL 35294-0022
VHoward@ms.soph.uab.edu

Robert Jesse, M.D., Ph.D.
National Program Director for Cardiology
Veterans Health Administration
Cardiology 111J
1201 Broad Rock Blvd.
Richmond, VA 23249
Robert.Jesse@med.va.gov

Harlan Krumholz, M.D., M.S.
Professor of Medicine and Epidemiology and Public Health
Department of Internal Medicine
Yale University School of Medicine
333 Cedar Street
Room I-456 SHM
New Haven, CT 06510
harlan.krumholz@yale.edu

Russell V. Luepker, M.D.
Professor, Department of Epidemiology
School of Public Health
University of Minnesota
1300 South Second Street, Suite 300
Minneapolis, MN 55454
luepker@epi.umn.edu

Greg Mears, M.D.
Associate Professor
North Carolina EMS Medical Director
EMS Performance Improvement Center
Department of Emergency Medicine
University of North Carolina-Chapel Hill
10002 Main Street
Chapel Hill, North Carolina 27516
gdm@med.unc.edu

Laurie Morrison, M.D., M.Sc.
Director, Prehospital and Transport Medicine Research Program
Division of Emergency Medicine
Department of Medicine
Sunnybrook Health Science Centre
2075 Bayview Avenue, Suite BG-13
North York, Ontario
Canada, M4N 3M5
laurie.morrison@sw.ca

Elizabeth Ofili, M.D., M.P.H.
Chief of Cardiology
Morehouse School of Medicine
720 Westview Drive SW
Atlanta, GA 30310-1495
ofilie@msm.edu

Joseph Ornato, M.D.
Chairman, Emergency Medicine
Box 980525
Main Hospital G248
Virginia Commonwealth University
Richmond, VA 23284
jornato@mcvh-vcu.edu

Eric D. Peterson, M.D., M.P.H.
Associate Professor of Medicine
Associate Vice Chair for Quality
Duke University Medical Center
Director, CV Outcomes Research & Quality
Duke Clinical Research Institute
Box 17969
Durham, NC 27715
peter016@mc.duke.edu

Veronique L. Roger, M.D.
Professor of Medicine
Department of Internal Medicine
Mayo Clinic Rochester
200 First Street Southwest
Rochester, MN 55905
roger.veronique@mayo.edu

Wayne Rosamond, Ph.D.
Associate Professor
Department of Epidemiology
University of North Carolina
Bank of America, Suite 306
137 E. Franklin Street
Chapel Hill, NC 27514
wayne_rosamond@unc.edu

Marcel Salive, M.D., M.P.H.
Director, Division of Medical and Surgical Services
Coverage and Analysis Group
Centers for Medicare & Medicaid Services
7500 Security Boulevard ? C1-09-28
Baltimore, MD 21244
MSalive@cms.hhs.gov

Veikko Salomaa, M.D., Ph.D.
Chief Physician, Head of the Chronic Disease Epidemiology Unit
Department of Epidemiology and Health Promotion
National Public Health Institute
Mannerheimintie 166, FIN-00300
Helsinki, Finland
veikko.salomaa@ktl.fi

Eduardo J. Sanchez, M.D., M.P.H.
Commissioner
Texas Department of State Health Services
Mail code 1911
1100 West 49th Street, M-751
Austin, TX 78756-3199
(Assistant Emma Gomez receives email: emma.gomez@dshs.state.tx.us)

Joseph Selby, M.D., M.P.H.
Director, Division of Research
Kaiser Northern California
3505 Broadway
Oakland, CA 64611
jvs@dor.kaiser.org

Jane E. Sisk, Ph.D.
Director, Division of Health Care Statistics
National Center for Health Statistics
3311 Toledo Road
Hyattsville, MD 20782
hzs2@cdc.gov

Zhi-Jie Zheng, M.D., Ph.D.
Lead Epidemiologist/ CVD Registries Team Leader
Heart Disease and Stroke Prevention Program
Centers for Disease Control and Prevention
4770 Buford Hwy NE, Mailstop K-47
Atlanta, GA 30341-3717
zaz7@CDC.GOV

Additional Planning Group Members

Janet B. Croft, PhD
Lead Epidemiologist
Cardiovascular Health Branch,
Division of Adult and Community Health,
National Center for Chronic Disease Prevention and Health Promotion,
Centers for Disease Control and Prevention
Mailstop K-47, 4770 Buford Hwy NE
Atlanta GA 30341
jbc0@cdc.gov

Lawrence Fine, M.D.
Leader, Clinical Prevention and Translation Scientific Research Group
Division of Epidemiology and Clinical Applications
National Heart, Lung, and Blood Institute
National Institutes of Health
6701 Rockledge Drive, Room 8138
Bethesda, MD 20892-7936
lf128x@nih.gov

Mary M. Hand, R.N., M.S.P.H.
Coordinator, National Heart Attack Alert Program
Office of Prevention, Education, and Control
National Heart, Lung, and Blood Institute
National Institutes of Health
31 Center Drive, Room 4A16
Bethesda, MD 20892-2480
handm@nhlbi.nih.gov

Teri A. Manolio, M.D., Ph.D.
Director, Epidemiology and Biometry Program
Division of Epidemiology and Clinical Applications
National Heart, Lung, and Blood Institute
National Institutes of Health
6701 Rockledge Drive, Room 81603
Bethesda, MD 20892-7934
manoliot@nhlbi.nih.gov

Jean Olson, M.D., M.P.H.
Leader, Field Studies and Clinical Epidemiology Scientific Research Group
Division of Epidemiology and Clinical Applications
National Heart, Lung, and Blood Institute
National Institutes of Health
6701 Rockledge Drive, Room 8154
Bethesda, MD 20892-7934
olsonj@nhlbi.nih.gov

Gina S. Wei, M.D., M.P.H.
Medical Officer, Field Studies and Clinical Epidemiology
Scientific Research Group
Division of Epidemiology and Clinical Applications
National Heart, Lung, and Blood Institute
National Institutes of Health
6701 Rockledge Drive, Room 8150
Bethesda, MD 20892-7934
weig@nhlbi.nih.gov

Agenda

8:30 am - 8:45 am
-
Welcome

Welcome, introduction, charge to group

8:30 am - 5:00 pm
-
Question 1

Question 1: What are the current data needs concerning the incidence and prevalence of acute CV events and the short-and long-term medical practice patterns, quality of care, and health outcomes following these events?

8:45 am - 11:00 am
-
Early Morning Session

Early Morning Session: Presentations describing current knowledge about the incidence and prevalence, medical practice patterns, quality of care, and patient outcomes for each of the 4 proposed cardiovascular (CV) events (sudden cardiac arrest, acute coronary syndromes, stroke, and congestive heart failure) to include:

  • Public health burden of the disease
  • Existence of established standards of care (such as national practice guidelines, performance measures, and quality initiatives) for short- and long-term disease management
  • Current knowledge about the incidence and prevalence, medical practice patterns, quality of care, and patient outcomes of the 4 CV events
  • Existing surveillance systems and cohort studies that collect these data
    • What data do they provide?
    • What are their limitations?
  • Gaps in present knowledge

Moderator: Dr. Robertson

8:45 am
-
Sudden Cardiac Arrest - Dr. Joseph Ornato

About 400,000-460,000 deaths occur per year in the U. S. from sudden cardiac arrest, mostly due to ventricular fibrillation.  A majority of the events (79-84%) occur at home, and survival has been estimated to be as low as 5-8%.  Survival is highly dependent on the time interval between collapsing to receiving initial defibrillation.  Bystander CPR has also been found to be strongly associated with positive outcome.  Dr. Ornato pointed out that the emergency medical service (EMS) system configurations vary widely across North America.  Survival rate to hospital discharge from out of hospital cardiac arrest varied from 2-25% for all cardiac rhythms and 3-33% for ventricular fibrillation.  Much of the variation is due to differences in definitions (especially problematic when establishing the denominator). He then described the Utstein CARDIA Arrest Guidelines, which aim to develop consensus on uniform reporting of data.  The guidelines were recently revised to include updated variable definitions, revised cardiac arrest data collection form, more focus on collecting core time intervals, and emphasis on the need to have collection time synchronized.  Some experts feel that the Utstein guidelines are limited by failing to include neurological outcomes as an essential component of all outcome studies on sudden cardiac arrest; they believe that once this is accomplished, using the Utstein template should be demanded by every editor to whom an article on resuscitation is submitted.

Dr. Ornato described the National Registry of CPR, a multi?facility registry sponsored by the American Heart Association.  It uses an electronic database to capture resuscitation records, follows an in-hospital Utstein template and standardized data definitions, and thus allows aggregate data analysis across multiple centers (about 303 hospitals throughout the U.S. are participating).  Resuscitations that started outside the hospital are excluded.  He also showed a list of international surveillances.  He concluded by saying that an organized national surveillance in the U.S. is needed.

9:15 am
-
Acute Coronary Syndromes - Dr. Harlan Krumholz

Various data-collection systems for acute myocardial infarction (AMI) currently exist, including local surveillance projects, federal agency projects, and other large organizations. There is a generally decreasing trend in coronary heart disease deaths, as observed by the Framingham Heart Study, the Minnesota Heart Survey, the Worcester Heart Attack Study, and in Olmsted County. Dr. Krumholz showed trends in AMI incidence as per the Worcester Heart Attack Study; this study has also provided data on trends in case-fatality, drug treatment, and use of invasive procedures following AMI. He then described the Cooperative Cardiovascular Project (CCP), a CMS initiative to improve the quality of care for Medicare beneficiaries with AMI. Using the databases of Medicare beneficiaries with AMI in four states (AL, CT, IA, and WI), medical records of more than 200,000 Medicare hospitalizations for AMI were evaluated. During 1992-2001, there was an increasing percentage of older people, female, and skilled nursing facility residents who had AMI. The burden of comorbidities among AMI patients has also increased. Other interesting CCP data presented included the adjusted 1-year mortality and changes in clinical presentation of AMI.

Dr. Krumholz indicated that presently no national surveillance system exists for AMI. Even with the currently available AMI data several important pieces are missing, including health status/functional status information, treatment from the patient perspective, and a concerted national strategy for surveillance. Specific data needs for AMI include national incidence data, patient profiles, treatment patterns, safety information, and outcomes. He also raised the need for complementary data that will provide insight about the changing epidemiology and performance in promoting the safety, effectiveness, equity, efficiency, timeliness and patient-centeredness of the U.S. health care system. He discussed the importance of optimizing existing data as well as obtaining new data, supporting creative local efforts, and moving toward coordinated national projects. Finally, he posed some fundamental questions for the group to consider: What do we need to know, and how well do we need to know it? What percent of our total health care budget should be informing us about the health and outcomes of our population?

9:45 am
-
Break

Break

10:00 am
-
Stroke - Virginia J. Howard, University of Alabama at Birmingham

Stroke is the third leading cause of death in the United States and one of the leading causes of long-term disability. It is estimated that 700,000 Americans experience a new or new recurrent stroke each year, 500,000 first strokes, and 200,000 recurrent strokes. It is estimated that in 2002, there were 5.4 million stroke survivors (i.e., prevalence of previous stroke). The Framingham study has shown that 15-30% of stroke survivors are permanently disabled. 1 In addition to these ?clinically diagnosed? strokes, MRI and CT data from the Cardiovascular Health Study and the Atherosclerosis Risk in Communities Study suggest that there may be as many as 13 million ?silent strokes? without diagnosed clinical symptoms but with radiologic evidence of infarction.1 The presence of these ?silent strokes? is likely associated with clinically important but less identifiable symptoms including decreased cognitive functioning; the odds of having a clinically pronounced stroke has been estimated to be over three times greater among those with silent infarctions.2

Although declines in stroke mortality rates have occurred, approximately a 60% decline since the 1960?s, the public health burden of stroke remains high. With the ?graying of America? it is anticipated that the absolute number of strokes will more than double in the next 40 years.3 In addition, health disparities, including a substantial excess (RR of 3 to 4 at ages 55-65) among African Americans and geographic disparities (i.e., the ?Stroke Belt?), have persisted in the face of the overall declining mortality.

For 2005, the estimated direct and indirect cost of stroke is $56.8 billion. This figure includes health expenditures such as costs of physicians, hospital and nursing home services and lost productivity, but is likely an underestimate because it may not include informal care costs and costs of comorbidities.1,4

Guidelines for the prevention and management of first and recurrent stroke and rehabilitation have been developed and are updated periodically. 5-12 Programs such as the Joint Commission on Accreditation of Healthcare Organizations (JCAHO), the American Stroke Association?s Get With the Guidelines-Stroke (GWTG-Stroke) and recommendations from the Brain Attack Coalition are focused on improving stroke care.13-15 There are no representative data available at the national level to monitor medical practice patterns, quality of care and patient outcomes, according to these guidelines, however, the Centers for Medicare and Medicaid Services (CMS) collects data on several secondary prevention measures among hospitalized Medicare beneficiaries. The National Committee for Quality Assurance (NCQA) reports data from participating managed care organizations, but this is a voluntary system and not nationally representative. Most recently, the Paul Coverdell National Acute Stroke Registry was initiated to collect data on and track the quality of acute stroke care.16

Existing surveillance systems and cohort studies that collect these data include:

1. Community-based surveillance studies

  • Greater Cincinnati/Northern Kentucky Stroke Study (GCNKSS)17
  • Brain Attack Surveillance in Corpus Christi (BASIC) Project18
  • Atherosclerosis Risk in Communities ? Surveillance Systems (ARIC)19

2. Hospital-based surveillance studies

  • Paul Coverdell National Acute Stroke Registry16
  • Rochester Minnesota Epidemiologic Program20

3. Cohort studies

  • Framingham21
  • Atherosclerosis Risk in Communities ? Cohort (ARIC)19
  • Cardiovascular Health Study (CHS)22
  • Multi-Ethnic Study of Atherosclerosis (MESA)23
  • REasons for Geographic And Racial Differences in Stroke (REGARDS)24

The community-based programs have the substantial strength of providing the most unbiased estimates of incidence (including non-hospitalized stroke, etc), but have the major shortcoming of being based in specific communities hence failing to reflect the substantial geographic variations in stroke mortality. The hospital-based programs have the advantage of access to hospital data to quantify stroke subtype and severity, but require hospitalization for the stroke in order to detect events; these are also limited to specific communities. The cohort studies have the advantage of assessment of risk factors prior to the event and providing a clearly defined cohort to identify events (i.e., clearly defined denominator for event rates), but will provide less-precise estimates of incidence as a result of limitations of sample size. Stroke mortality rates vary across race-ethnicity and geographic regions and many of these studies do not include all race-ethnic groups, and those that do are limited in sample size, especially in younger ages.

Gaps in present knowledge:

  1. Much of what we know about the burden of stroke is from stroke mortality rather than stroke incidence, with little data on incidence.
  2. Mortality data do not traditionally (and reliably) capture data on stroke subtypes. The major subtypes are remarkably different diseases, also placing different burdens on the public health.
  3. With few exceptions (Rochester Minnesota Epidemiologic Project Twin Cities of Minneapolis, and most recently, the Greater Cincinnati/Northern Kentucky Stroke Study and Corpus Christi), there is a lack of data on incidence, case-fatality and trends over time.
  4. There are even less incidence and case fatality data on important, but relatively rare, stroke subtypes ? specifically, subarachnoid hemorrhage and intracerebral hemorrhage.
  5. National, representative data on compliance with prevention and treatment guidelines are not yet available.

References

  1. American Heart Association. Heart Disease and Stroke Statistics ? 2005 Update. Dallas, Texas: American Heart Association; 2005.
  2. Wong TY, Klein R, Sharrett AR, Couper DJ, Klein BE, Liao DP, Hubbard LD, Mosley TH. Atherosclerosis Risk in Communities Study. Cerebral white matter lesions, retinopathy, and incident clinical stroke. JAMA 2002;288:67-74.
  3. Howard G, Howard VJ. Stroke incidence, mortality and prevalence. In: Gorelick PB and Alter M., eds. Stroke Prevention.Parthenon Publishing, 2002.
  4. Evers SMAA, Struijs JN, Ament AJHA, van Genugten MLL, Jager JHC, van den Bos GAM. International comparisons of stroke cost studies. Stroke 2004;35:1209-1215.
  5. Goldstein LB, Adams R, Becker K, Furberg CD, Gorelick PB, Hademenos G, Hill M, Howard G, Howard VJ, Jacobs B, Levine SR, Mosca L, Sacco RL, Sherman DG, Wolf PA, del Zoppo, GJ. Primary prevention of ischemic stroke: A statement for healthcare professionals from the Stroke Council of the American Heart Association. Stroke 2001;32:280-299.
  6. Straus SE, Majumdar SR, McAlister FA. New evidence for stroke prevention: scientific review. JAMA 2002;288:1388-1395.
  7. Wolf PA, Clagett GP, Eaton JD, Goldstein LB, Gorelick PB, Kelly-Hayes M, Sacco RL, Whisnant JP. Preventing ischemic stroke in patients with prior stroke and transient ischemic attack. A statement for healthcare professionals from the Stroke Council of the American Heart Association. Stroke 1999;30:1991-1994.
  8. Gorelick PB, Sacco RL, Smith DB, Alberts M, Mustone-Alexaner L, Rader D, Ross JL, Raps E, Ozer MN, Brass LM, Malone ME, Goldberg S, Booss J, Hanley DF, Toole JF, Greengold NL, Rhew DC. Prevention of a first stroke: A review of guidelines and a multidisciplinary consensus statement from the National Stroke Association. JAMA 1999;281:1112-1120.
  9. Biller J, Feinberg WM, Castaldo JE et al. Guidelines for carotid endarterectomy: a statement for healthcare professionals from a Special Writing Group of the Stroke Council, American Heart Association. Circulation 1998;97:501-509.
  10. Department of Veterans Affairs and Department of Defense. VA/DoD Clinical Practice Guideline for the Management of Stroke Rehabilitation. Washington, D.C: Department of Veterans Affairs and Department of Defense, 2003.
  11. Mayberg MR, Batjer HH, Dacey R, et al. Guidelines for the management of aneurismal subarachnoid hemorrhage: A statement for healthcare professionals from a special writing group of the Stroke Council, American Heart Association. Circulation 1994;90:2592-2605.
  12. Schwamm LH, Pancioli A, Acker III JE, Goldstein LB, Zorowitz RD, Shephard TJ, Moyer P, Gorman M, Johnston SC, Duncan PW, Gorelick P, Frank J, Stranne SK, Smith R, Federspiel W, Horton KB, Magnis E, Adams RJ. Recommendations for the establishment of stroke systems of care: Recommendations from the American Stroke Association?s Task Force on the Development of Stroke Systems. Stroke 2005;36:690-703.
  13. Holloway RG, Vickery BG, Benesch C, Hinchey JA, Bieber J. National Expert Panel. Development of performance measures of acute ischemic stroke. Stroke 2001;32:2058-2074.
  14. Joint Commission on Accreditation of Healthcare Organizations (JCAHO). Primary Stroke Center Certification Program. Available at: http://www.strokeassociation.org/presenter.jhtml?identifier=3016808(link is external).
  15. American Stroke Association. Get with the guidelines?stroke. Available at: www.strokeassociation.org/presenter.jhtml?identifier=3002728(link is external). Accessed May 25, 2005.
  16. The Paul Coverdell Prototype Registries Writing Group. Acute stroke care in the US: Results from 4 pilot prototypes of the Paul Coverdell National Acute Stroke Registry. Stroke 2005;36:1232-1240.
  17. Broderick J, Brott T, Kothari R, Miller R, Khoury J, Pancioli A, Gebel J, Mills D, Minneci L, Shukla R: The Greater Cincinnati/Northern Kentucky Stroke Study : Preliminary first-ever and total incidence rates of stroke among blacks. Stroke 1998;29:415-421.
  18. Smith MA, Risser JM, Moye LA, Garcia N, Akiwumi O, Uchino K, Morgenstern LB. Designing multi-ethnic stroke studies: the Brain Attack Surveillance in Corpus Christi (BASIC) project. Eth Dis 2004:14:520-526.
  19. Anonymous. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am J Epid 1989;129:687-702
  20. Leibson CL, Ballard DJ, Whisnant JP, Melton LJ 3rd. The compression of morbidity hypothesis: promise and pitfalls of using record-linked data bases to assess secular trends in morbidity and mortality. Milbank Quarterly 1992;70:127-154
  21. Higgins MW. The Framingham Heart Study: review of epidemiological design and data, limitations and prospects. Prog Clin Biologic Res 1984;147:51-64
  22. Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA, Kuller LH, Manolio TA, Mittelmark MB. Newman A. et al. The Cardiovascular Health Study: design and rationale. Ann Epid 1991:1:263-276, 1991
  23. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, Greenland P, Jacobs Jr. DR, Kronmal R, Liu K, Nelson JC, O?Leary D, Saas MF, Shea, Szklo M, Tracy RP. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol 2002;156:871-881.
  24. Howard VJ, Cushman M, Pulley L, Gomez C, Go R, Prineas RJ, Graham A, Moy CS, Howard G. The Reasons for Geographic And Racial Differences in Stroke (REGARDS) Study: Objectives and Design. Neuroepidemiology 2005: in press.

10:30 am
-
Heart Failure - Dr. Elizabeth Ofili

Heart failure poses significant health and financial burdens on patients and on the society as a whole.  The major cost-driver in heart failure is the high incidence of hospitalizations.  Patients with heart failure are at high risk for hospitalizations and mortality, which has been estimated to be about 50% at five years.   Sudden cardiac arrest is the primary mode of death in mild to moderate heart failure, and occurs at 6 to 9 times the rate of the general population.  Several guidelines have been developed on the management of heart failure, including those established by the American College of Cardiology and the American Heart Association.   Nevertheless, marked gaps and variations exist in the quality of care for heart failure; thus, many opportunities exist to improve care for patients with heart failure.  Despite overwhelming clinical-trial evidence, expert opinion, national guidelines, and a vast array of educational conferences, evidence-based, life-saving drug and device therapies continue to be underutilized.  Recommendations for medication and device therapies are rapidly evolving, therapy is more complex, and collaboration among physicians (primary care physicians, cardiologists, heart-failure specialists, and electrophysiologists) can be challenging.  New approaches to improving the use of proven, guideline-recommended, life-saving therapies are clearly needed. 

Dr. Ofili also described gaps in present knowledge concerning long-term care of heart failure. Specifically, more long-term data are needed for the following: physicians? use of evidence-based medications; patient adherence to prescribed medications; how comorbidities affect care; use of adequate dietary counseling and patient adherence to dietary regimen; early care with escalating symptoms; adequacy of discharge planning, outpatient follow-up, and outpatient monitoring; patient social support systems; patient and care-giver needs, and disparities in the care of minorities.  She emphasized the need for heart-failure surveillance, particularly one in which outpatient heart failure as well as ethnic disparities in care can be clearly assessed.

11:00 am - 12:15 pm
-
Late Morning Session

Late Morning Session: Presentations on federally-funded registries/ surveillance/ quality improvement measurement systems focusing on:

  • Design/Infrastructure
  • Strengths and limitations
  • Lessons learned
  • Opportunity to partner or piggyback to address data needs in CV incidence/prevalence, care and outcomes
  • Value as a model for new studies to address data needs in CV incidence/prevalence, care and outcomes

Moderator: Dr. Robertson

11:00 am
-
Public Health Information Network (PHIN) and National Electronic Disease Surveillance System (NEDSS) - Dr. Claire Broome

What is PHIN (Public Health Information Network)?

  • It is a multi-organizational business and technical architecture (www.cdc.gov/phin).
    • Technical standards
    • Data standards
    • Specifications to do work
  • It is also a process.
    • Commitment to the use of standards
    • Commitment to participating in development and implementation of specifications

Moving from conceptual to real

  • Many state and local health departments will have different stages of electronic systems and IT capacities
  • Each state/jurisdiction will need to develop specific plans
    • Which systems to integrate
    • What funding streams available
  • Central concept of PHIN is implementation of standards based interoperable systems so all
    • Maximize reuse of tools
    • Efficient use of technical expertise
    • Plan for extensibility

PHIN Components

  • Applications
    • National Electronic Disease Surveillance System (NEDSS) ? disease surveillance for 140 notifiable diseases, electronic laboratory reporting
    • BioSense ? early event detection
    • Laboratory Response Network (LRN) ? diagnostic capacity and information delivery

PHIN Tools available from CDC

  • Software for industry standards based bi-directional inter-institutional messaging transport (PHIN MS)
    • ebXML ?handshake,? PKI encryption and security
  • Technical assistance & direct assistance available for public health partners (eg security IVV)
  • PHIN Vocabulary Access and Distribution Services, including web accessible Standard Reference Tables
  • Implementation Guides that specify data standards, message format

Surveillance Funding History

  • 50 states, 6 cities funded for NEDSS: 43 started with Assessment & Planning phase in September 2000
  • NEDSS ELC awards FY 2001-2005
  • September 2002, 2003, 2004: Public Health and Social Services Emergency Fund provides >$1 billion for state and local public health preparedness capacity
    • Guidance from CDC and HRSA to use PHIN standards for IT investments
    • Guidance explicitly includes NEDSS as part of surveillance

How does NEDSS support public health surveillance functions?

  • More timely detection via electronic laboratory results reporting from clinical diagnostic laboratory information system
    • For pre-defined results of public health importance, electronic message to health department automatically sent
    • Message includes structured data including test, result, provider ID, patient age, sex
    • Multi-jurisdiction labs, public health labs, some local labs

How does NEDSS support public health surveillance functions?

  • Web data entry: case information available to local & state health departments immediately on entry (no paper, no mail)
  • Support case investigation by state and local health dept
  • Share lab results electronically between state public health lab & state surveillance
  • Send standardized data electronically to CDC
  • Same application for over 140 diseases, replace disease specific ?stovepipe? applications
  • Integrate with other PHIN components

NEDSS Base System:

  • NEDSS compatible system for state and local use developed by an experienced web software developer (Computer Sciences Corporation)
  • Also useful as a specific implementation of NEDSS -- e.g. standard messages, database model
  • Version 1.0 includes 93 notifiable diseases, and modules for vaccine preventable diseases, hepatitis, bacterial meningitis and pneumonia
  • Now at Version 1.1.3; includes over 140 notifiable diseases, expanded data entry capacity, reporting capacity, locally defined fields
  • Added additional contractor, SAIC, to accelerate Program Area Module Development

Status of NEDSS surveillance enhancements April 2005

Grantee capacity Impact on disease reports In daily use Develop or deploy Planning
Capacity for web data entry More timely 29* 12 15
Electronic Lab Reporting (excludes lead only) More timely
More cases		 28 14 12

*application in use: 1 commercial (3 states); NEDSS Base System (10 states); custom (16)

State NEDSS Surveillance Functions, April 2005

  • Surveillance using web-based data entry - 9
  • Electronic Laboratory Reporting - 8
  • Both - 18

Opportunities to accelerate Cardiovascular Event Surveillance

  • Collaboration on CV surveillance research
    • eg data sources, data entry options, EHR utility
  • Collaboration in accelerating standards based, interoperable electronic health records
    • eg FHA, JCAHO, NCQA, NQF
  • Collaboration on utilization of tools
    • eg CDC MOU with NCI
  • Collaboration on cardiovascular/stroke surveillance systems

What does NEDSS have to do with HIPAA?

  • HIPAA mandates national health care data standards and policies in four areas:
    • Transaction content; unique identifiers for providers, health plans; security; privacy
  • NEDSS architecture standards are HIPAA compliant:
    • supports ?dual use? for security, messaging elements
  • Approach to NEDSS data standards is HIPAA compliant:
    • Adopting HIPAA standards where relevant eg electronic laboratory reporting in NEDSS uses HIPAA claims attachment
    • Advocating inclusion of data elements relevant to public health with SDO?s

What does NEDSS have to do with HIPAA Privacy Rule?

  • Privacy Rule allows current practice of sharing data with public health
    • Rule permits health care providers to share individually identifiable information with legally authorized public health entities for public health activities
    • Public health activities include surveillance (NEDSS), investigation, intervention

11:15 am
-
Paul Coverdell National Acute Stroke Registry - Dr. Zhi-Jie Zheng

Zhi-Jie Zheng, MD, PhD, Cardiovascular Health Branch, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA

The Paul Coverdell National Acute Stroke Registry is a state-based program designed to measure, track, promote and improve quality of acute stroke in the United States. It is a major component of the Centers for Disease Control and Prevention (CDC)?s integrated National Heart Disease and Stroke Prevention Program. The registry was established in 2001 in honor of Senator Paul Coverdell (R, Georgia), who died of an acute stroke in 2000. Through consultations with national expert panel and after successful prototype development of eight state-based projects (GA, CA, IL, MI, MA, OH, NC, and OR) from 2001-2004, CDC funded four states (GA, IL, MA, and NC) in 2004 to implement

The scope of the Coverdell Stroke Registry program includes the process from onset of signs and symptoms through the emergency medical system or other transport to a hospital emergency department, diagnostic evaluation, use of thrombolytic therapy when indicated by diagnosis and timeliness, complication prophylaxis and management, other aspects of acute care; secondary prevention measures, and referral to rehabilitation services for surviving cases. All patients presented/transferred to the emergency department with initial signs and symptoms indicative of stroke are eligible to be enrolled in the registry initially, pending final diagnosis.

The registry utilizes web-based data collection systems that allow prospective case ascertainment with real-time data entry. A representative sample of stroke care facilities from each state are recruited to participate in the registry, and in each hospital, a minimum of 6 months of consecutive cases for a chosen timeframe are obtained. The data elements for the registry include demographic information, pre-hospital/EMS data, information on sign and symptom onset, imaging findings, thrombolytic therapy (e.g., time, complications, reasons for non-treatment), medical history, in hospital diagnostic procedures and treatment, other in-hospital complications: (e.g., DVT, pneumonia), and discharge information (e.g., ICD-9 codes, discharge destination, functional status, secondary prevention measures, and rehabilitation referral, etc).

The strengths of the Coverdell Stroke Registry include prototype-tested and standardized data elements, prospective case ascertainment, state flexibility in data system, and build-in intervention and data quality assurance. The registry, however, is not able to provide prevalence or incidence information in its current design, nor does it provide national representative sampling, and it has limited information on long-term outcomes after hospital discharge. Nevertheless, the experience learned from, and the model used for, the Coverdell Stroke Registry could be valuable for monitoring clinical management related to acute cardiac events, such as chest pain, acute myocardial infarction, acute coronary syndrome, and cardiac arrest.

11:30 am
-
FUNDING TRAINING & CAREER DEVELOPMENT DIVISION OF INTRAMURAL RESEARCH RESEARCH RESOURCES RESEARCH MEETING SUMMARIES TECHNOLOGY TRANSFER Quality Improvement Measures for Cardiovascular Disease - Dr. Helen Burstin

Mission Statement: AHRQ

  • The mission of the Agency for Healthcare Research and Quality (AHRQ) is to improve the quality, safety, efficiency, and effectiveness of health care for all Americans.

Overview

  • Focus on quality and cost measurement for CVD
    • The National Healthcare Quality Report (NHQR) and the National Healthcare Disparities Report (NHDR)
  • AHRQ's role in quality and cost measures for CVD
    • Healthcare Cost and Utilization Project (HCUP)
    • National CAHPS Benchmarking Database (NCBD)
    • Medical Expenditure Panel Survey (MEPS)
  • Role of Health IT in quality measurement

RAND Study: Quality of Health Care Often Not Optimal

  • Doctors provide appropriate health care only about half the time
  • E. McGlynn, S. Asch, J. Adams, et al., The Quality of Health Care Delivered to Adults in the United States, N Engl J Med, 2003

Congressional Mandate: Mandated by Congress in the Healthcare Research and Quality Act (PL. 106-129)

  • "National trends in the quality of health care provided to the American people"
  • "Prevailing disparities in health care delivery as it relates to racial factors and socioeconomic factors in priority populations"

2004 National Reports on Quality and Disparities -- http://www.innovations.ahrq.gov/innovations_qualitytools.aspx

  • Second annual reports focus on quality of and disparities in health care in America
  • Measurement Topics
    • Quality of Health Care
      • Effectiveness
        • Cancer, Diabetes, ESRD, Heart Disease, HIV/AIDS, Maternal & Child Health, Mental Disease, Respiratory Disease, Nursing Home and Home Health Care
      • Safety
      • Timeliness
      • Patient centeredness
    • Access to Health Care
      • Getting into the system
        • Insurance, Usual Source of Care, Perceptions of Need
      • Getting care within the system
      • Perceptions of care
        • Patient-provider communication, relationship
      • Health care use
  • Key Findings from the 2004 Reports
    • Disparities are pervasive.
    • Improvement is possible, but change takes time.
    • Gaps in information exist, especially for specific conditions and populations.
    • The gap between the best possible care and actual care remains large.
    • Quality is improving in many areas, but change takes time.
    • Further improvement in health care is possible.

CVD Prevention

  • Screening for high blood pressure (NHIS)
    • % blood pressure measured within preceding 2 years and can state whether their BP is normal or high
  • Lipid screening: % Adults (20+) (NHANES)
    • Ever had a cholesterol checked
    • Were told by a doctor that they had a high cholesterol
    • With high cholesterol taking cholesterol-lowering medication
    • With high cholesterol who have total cholesterol < 200
  • Counseling on Risk Factors (MEPS)
    • Percent of smokers receiving advice to quit smoking

CVD Management

  • Management of hypertension (NHANES)
    • Percent of people with hypertension who have blood pressure under control
  • Management of CHF (NHDS/HCUP SID)
    • Hospital admissions for CHF

AMI Measures (QIO)

  • AMI Measures:
    • % AMI patients administered aspirin w/in 24 hrs of admission
    • % AMI patients prescribed aspirin at discharge
    • % AMI patients administered beta blocker w/in 24 hrs of admission
    • % AMI patients prescribed beta blocker at discharge
    • % AMI patients with LV dysfunction prescribed ACE inhibitor at discharge
    • % AMI patients given smoking cessation counseling while hospitalized
    • Median time in minutes to thrombolysis
    • Median time in minutes to PTCA
  • Aggregate Measures technical advisory panel supported the development of a composite measure of the 8 QIO AMI measures for 2005

Heart Disease Treatment (HCUP-NIS)

  • Pediatric cardiac surgery mortality rate
  • Abdominal aortic aneurysm (AAA) repair mortality rate
  • CABG morality rate
  • PTCA mortality rate
  • AMI mortality rate
  • CHF mortality rate

Patient-Provider Communication

  • CAHPS Core Components (MEPS/NCBD)
    • How often their health providers listened to them?
    • How often their health providers explained things clearly?
    • How often their health providers showed respect for what they had to say?
    • How often their providers spent enough time with them?

National CAHPS® Benchmarking Database

  • National repository for CAHPS® data
  • Includes CAHPS® survey data and health plan descriptive data
    • Commercial, Medicare, Medicaid
    • Adult and child
  • Facilitates comparisons of CAHPS® results
  • Provides benchmarking information useful for evaluation and QI
  • Offers primary data for research purposes

The Healthcare Cost and Utilization Project (HCUP)

  • Federal, state, industry partnership
  • Has 90% of all inpatient discharges
  • Growing to include ED, ambulatory surgery, other
  • Includes charge, payer, clinical data
  • Extensive use by researchers and policy-makers
  • New methodology converts charges to cost
    • Friedman, Journal of Health Care Finance, 2002
  • Quality Indicators Usable with any discharge data

HCUP Has Five Databases

  • State Inpatient Databases (SID)
  • Nationwide Inpatient Sample (NIS)
  • Kids' Inpatient Database (KID)
  • State Emergency Department Databases (SEDD)
  • State Ambulatory Surgery Databases (SASD)

State Inpatient Databases (SID)

  • What is the SID?
    • Captures all inpatient visits in a state
    • In total, they encompass data from 90% of all inpatient visits in community hospitals
  • What is in the SID?
    • ~55,000 - 3.9 million SID records
    • Data found on inpatient bills (UB-92)
  • How can the SID be used?
    • Can be linked to AHA, ARF and other HCUP databases
    • Enumerate hospitals and discharges within market areas or a state
    • Compare of data from two or more states
    • Disparities and quality of care
    • State-specific trends in inpatient utilization, access, charges and outcomes
  • What states are in the SID and released to the public through the Central Distributor?
AZ MA NE UT
CO MD NJ WA
FL ME NY WI
IA MI OR WV
KY NC SC  

Nationwide Inpatient Sample (NIS)

  • Years of Data
    • 1988 - 2002; 2003 - Coming in Summer, 2005
  • Enables
    • National and regional estimates of all hospitalizations
  • Sample:
    • All discharges from a sample of short-term community hospitals from the SID
    • Hospitals sampled based on region, location, ownership, teaching, bed size
  • Description for 2003 NIS
    • 36 states and ~ 1,000 hospitals
    • ~ 7.8 million records (unwgt) = ~ 37.8 million records (wgt)
  • Price in Central Distributor
    • $160-$322 per year, depending on the year
    • $20 per year for a student/trainee

Kids' Inpatient Database (KID)

  • Years of Data
    • 1997 and 2000 available; 2003 -- Coming October, 2005
  • Enables
    • National and regional estimates of pediatric hospitalization
    • Studies of common and rare pediatric conditions
  • Sample:
    • 10% stratified sample of in-hospital births from the SID
    • 80% of other pediatric discharges from the SID
  • Description for 2003 KID
    • Data on hospitalizations for children < or = 20 years
    • 36 states and > 2,800 hospitals
    • ~ 2.5 million records (unwgt) = ~ 7.3 million records (wgt)
  • Price in Central Distributor
    • $200 per year
    • $20 per year for a student/trainee

State Emergency Department Databases (SEDD)

  • What is the SEDD?
    • Captures all ED visits in a state that do not result in admission
    • With the SID, captures all ED visits in the state
  • What is in the SEDD?
    • 190,000 - 2.7 million ED records
    • Includes data found on outpatient bills
  • How can the SEDD be used?
    • Can be linked to AHA, ARF, other HCUP databases
    • Injury surveillance
    • Trends in ED use
    • Ambulatory care sensitive conditions
    • Enumerate ED visits and re-visits
    • Disparities in ED utilization
  • What states are in the SEDD?
CT MA MN TN
GA MD* NE* UT*
HI ME* NH VT
IN MO SC  

*Available to the public through the Central Distributor

State Ambulatory Surgery Databases (SASD)

  • What is the SASD?
    • All hospital-based AS visits in a state
    • Includes some free-standing AS visits
  • What is in the SASD?
    • ~75,000 - 2.9 million AS records per state
    • Includes data found on outpatient bills
  • How can the SASD be used?
    • Can be linked to AHA, ARF, other HCUP databases
    • State-specific trends in ambulatory surgery utilization, access, charges, and outcomes
    • Compare IP and AS data
    • Examine complications of AS
  • What states are in the SASD?
CO MD* NC* UT*
CT ME* NJ* VT
FL* MN NY* WI*
GA MO PA  
IN NE* SC  
KY* NH TN  

*Available to the public through the Central Distributor

AHRQ Quality Indicators (QIs)

  • Developed through contract with UCSF-Stanford Evidence-based Practice Center
  • Use existing hospital discharge data, based on readily available data elements
  • Incorporate severity adjustment methods (APR-DRGs, comorbidity groupings) in Inpatient QIs
  • Current modules: Prevention QIs, Inpatient QIs, and Patient Safety Indicators

Overview of AHRQ QIs

  • Prevention Quality Indicators
    • Ambulatory care sensitive conditions
  • Inpatient Quality Indicators
    • Mortality following procedures
    • Mortality for medical conditions
    • Utilization of procedures
    • Volume of procedures
  • Patient Safety Indicators
    • Post-operative complications
    • Iatrogenic conditions

AHRQ Health IT Initiatives

  • Transforming Healthcare Quality through Information Technology (THQIT) Grant Program
  • The AHRQ National Resource Center for Health IT
  • State and Regional HIT Demonstrations ( 6 states)
  • CMS - AHRQ collaboration
  • Indian Health Service - EHR Project
  • Privacy and Legal Framework

Transforming Healthcare Quality through Information Technology

  • Promoting access to Health IT:
    • Over 100 grants to communities, hospitals, providers, and health care systems to help in all phases of the development and use of health information technology.
    • The grants are spread across 38 states
    • Special focus on small and rural hospitals and communities.
    • First year funding is $41 million and will total nearly $96 million over three years.

Medicare Prescription Drug, Improvement, and Modernization Act

  • Health IT Provisions
    • Electronic Prescription Program
    • Grants to Physicians - ePrescribing systems
    • Telemedicine Demonstrations Projects
    • Medicare Care Management Performance Demonstration
    • Council for Technology and Innovation
    • Commission on Systemic Interoperability

Practice-Based Research Networks (PBRNs)

  • 36 new PBRN grants awarded in 2002
  • 19 PBRN grants awarded in 2000

Contact Information: For additional questions, please contact Dr. Helen Burstin hburstin@ahrq.gov

11:45 am
-
Centers for Medicare & Medicaid Services (CMS) - Dr. Marcel Salive

Outline of Talk

  • Medicare Claims
    • The bills submitted by hospitals (Part A) and physicians (Part B) to CMS for reimbursement
  • Medicare Coverage Process
    • Ability to link to data collection
  • Quality Improvement
    • Expansion of public reporting
    • Implementation of pay-for-performance

Medicare is the national health insurance program for:

  • People age 65 or older
  • Some people under age 65 with disabilities
  • People with End-Stage Renal Disease
  • 34.9 million covered lives in 2004

Three General Categories of Available Data

  • Category 1: Medicare Eligibility & Enrollment Data
  • Category 2: Medicare Claims Data
  • Category 3: Medicaid Overview Data
  • The first category includes Medicare beneficiary eligibility and enrollment data. The second category of data includes the Medicare data associated with fee-for-service claims. The third category of data includes Medicaid eligibility, utilization, and demographics data.
  • (Category 1) The Medicare Eligibility and Enrollment category contains person-level entitlement information for Medicare beneficiaries. Each time a claim for services rendered is received for adjudication and payment, beneficiary entitlement status is verified using this information. The Enrollment Database (EDB) is the designated CMS repository of enrollment and entitlement data for persons who are or have ever been enrolled in Medicare.
  • (Category 2) Medicare claims data constitutes the second category. Processing claims for Medicare health insurance benefits is fundamental to the operation of the Medicare program. CMS ensures that payments are made for services that are medically appropriate, covered, and rendered to eligible beneficiaries by qualified providers. The detailed claims records provide a unique source of information on health care utilization and costs. From these records, analytic files are created to support program and policy development and evaluation, as well as health care analyses and research. The National Claims History (NCH) is the CMS designated repository for all claims and utilization data.
  • Data from both categories contributes to the beneficiary demographics information that is maintained in the Enrollment Database (EDB), including: name; temporary residence, & mailing addresses; FIPS state & county codes; SSA state & county codes; date of birth; sex; representative payee; and program service center.
  • (Category 3) The third category contains data that originates in the State Medicaid Claims Processing systems. The types of data found in this category include eligibility data, claims data, other encounter and utilization information, and provider data.

Advantages of Using Medicare Claims for Surveillance

  • Routinely collected
  • Virtually the entire population of patients and providers
  • Large numbers
  • Tied to reimbursement, so is complete
  • Fraud if not accurate
  • Unique identifiers allow episodes of care to be linked for complete follow-up

Part A (Hospital) Claims

  • Unique patient and hospital ID
  • Dates of admission and discharge
  • Admitting diagnosis and acuity
  • Procedures performed (ICD-9-CM)
  • Medical diagnoses (ICD-9-CM)
  • Discharge status
  • Discharge destination

Part B (Physician) Claims

  • Unique patient and physician ID
  • Surgical and diagnostic procedures (CPT)
  • Date of service
  • Diagnosis for which service performed

Medicare Coverage

  • Section 1862(a)(1)(A) of the Social Security Act
  • Coverage and payment limited to items and services
    • Found "reasonable and necessary"
    • For treatment of illness or injury...

Steps to Medicare Coverage Determination and Payment

  • Outside of CMS:
    • Congress determines benefit categories
    • FDA approves drugs/devices for market
  • Within CMS:
    • Coverage
    • Coding
    • Payment

What standards are used in an NCD

  • Evidence of improved health outcomes
  • Appropriate for Medicare population
  • Could be replicated in provider community

Medicare ICD expanded coverage

  • Effective 1/05
  • Based on the results of SCD-HeFT trial
  • Linked to submission of data to national ICD database
  • Can answer residual questions regarding safety & effectiveness in certain groups of patients & providers
  • Initial hypotheses included in Decision Memo

ICD Implant Data Form

  • One page printed form
  • Data elements include:
    • Demographics
    • Patient history & clinical characteristics
    • Medications
    • Provider information
    • Clinical indications
    • Complications

CMS Vision of Quality: "The right care for every person every time," where the "right care" corresponds to the 6 Institute of Medicine aims

  • Safety
  • Effectiveness
  • Efficiency
  • Patient-centeredness
  • Timeliness
  • Equity

CMS Strategies For Promoting The Quality Council Vision

  • Standards setting, regulation, enforcement
  • Public reporting
  • Payment policy including pay-for-performance
  • Technical assistance

Upcoming work to Support This Vision?

  • Launch the third phase of the QIO Program - promote the Quality Council vision through infrastructure for public reporting and p4p and the provision of assistance
  • Lay foundation for evidence-based improvement in drug safety/quality
  • Lay foundation for evidence-based improvement in efficiency of resource use

Quality Measures

  • Measures do not stay constant due to changes in care
  • Measure refinement and development of new measures is needed
  • Abstraction, electronic, and survey tools must be modified to support data collection
  • CMS will develop and implement data validation processes
  • Maintenance and upgrades to IT infrastructure necessary to support data collection, validation, and reporting.

Assistance Tools and Methodology

  • Tools/methodologies needed to help providers seeking improvement on new performance measures
  • CMS needs to refine the tools and methods we are implementing
    • New tools to support work on new measures
    • Refined tools for work on measures that have not shown substantial improvement

Single Measure Set, Multiple Uses...

  • CMS using all or subset of measures for:
    • Doctors office
    • Medicare care management project
    • Voluntary coordinated care improvement pilot
    • Physician Group Practice demonstration
  • External interest in common measure set...
    • Physician specialty boards
    • Health plans
    • Purchasers
    • Consumers

CMS Measurement Framework

  • Build from previous work
  • Use existing, accepted concepts or measures
    • HEDIS measures
  • Align with other measures
    • JCAHO hospital measures
  • Public comments/forums

CMS Basic Requirements

  • Must be scientific and clinical sound
    • Evidenced based in guidelines
  • Must be transparent (reproducible)
  • Should not add burden to provider
  • Should use existing data source when possible

Types of Measures

  • Process: Blood pressure checked? Flu shot given?
  • Outcomes: Mortality, morbidity, HgbA1c control
  • Structure: Staffing levels, IT infiltration

Measure Alignment*

  • AMA, NCQA and CMS worked to create a single set of measures for coronary artery disease (CAD), congestive heart failure (CHF), hypertension (HTN), diabetes mellitus (DM), and prevention
  • JCAHO & CMS have aligned the reported hospital measures
    * Alignment at the micro-specification level

Measure Development

  • Identify existing relevant measures
  • If none found or revisions required....
    • Extensive input from expert clinicians
    • Draft technical specifications & training manuals
    • Build data collection tool if none exists
    • Support warehouse construction, record layouts
    • Multiple rounds of testing and refinement
    • Validity testing (do I get the information I expected?)
    • Reliability testing (does someone else get the same answer?)
    • Send for endorsement

Risk Adjustment: Address risk adjustment and other data adjustment needs

  • Inclusions and exclusions into the numerators and denominators
  • Variety of other techniques available to adjust the data
  • Result: measures calculated so that one has an 'apples to apples' comparison
  • May not always need to be risk adjusted

Endorsement of Measures: National Quality Forum

  • Private non-profit entity to create standards for health care quality
  • Working in partnership with many, including CMS, to develop consensus around what measures are ready to be called standards
  • Final step in a long development process

Contact Information

Websites: http://www.cms.gov/Medicare/Coverage/CoverageGenInfo/index.html
1-800-MEDICARE
www.medicare.gov
Marcel Salive, MD, MPH
410/786-0297
Marcel.Salive@cms.hhs.gov

12:00 pm
-
National Ambulatory Medical Care Survey (NAMCS) - Dr. Jane Sisk

National Ambulatory Medical Care (NAMCS) Scope

  • Non-federal, office-based physicians in direct patient care
  • Physicians excluded: Anesthesiology, radiology, and pathology
  • Out-of-scope settings
     
    • Hospital outpatient clinics, emergency depts.
    • Ambulatory surgicenter
    • Institutional settings (schools, prisons)
    • Federally operated clinic

Multistage National Probability Sample

  • 3-stage sampling
    • 112 geographic areas
    • 15 strata of physician specialties
    • About 30 visits sampled during 1 week
  • 1,500 physicians participate (about 70%)

Physician Characteristics, 2002

  • 55% under age 50
  • 21% female
  • 20% general/family practice; 13% internal medicine; 4% cardiovascular diseases
  • 86% metropolitan statistical area
  • 36% south, 18% northeast, 23% Midwest, 22% west

Practice Characteristics, 2002

  • 34% solo
  • 41% single-specialty group
  • 73% physician owned
  • 17% use electronic medical records
  • 84% have managed care contracts
  • Average percentage revenue
     
    • 48% private insurance
    • 30% Medicare
    • 12% Medicaid

Patient Characteristics

  • Age; Gender; Ethnicity/race; Vital signs; Tobacco use; Reason for visit; ZIP
  • Expected payment; Continuity; Disease management; Diagnoses.

Medical Care

  • Diagnostic testing/screening: Physical examination; Blood; Imaging; EKG
  • Counseling
  • Procedures
  • Treatment ordered
  • Medications/immunizations
  • Providers
  • Disposition
  • Time with physician

How Are the Data Used?

  • Management of specific medical conditions
  • Quality assessment and monitoring
  • Access to care
  • Disparities among subgroups
  • Diffusion of health-care technologies
  • Effects of policy changes
  • Benchmarks for states
  • Capacity and responsiveness of system

Number (in millions) and rate (per 100 persons) of physician office visits for diabetes [Grant et al. Arch Intern Med 2004;164(10):1134-1139]:

Year 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Number 14.3 17.1 17.3 15.1 16.8 18.9 20.8 25.5 23.8 26.1 27 24.9
Rate 5.7 6.8 6.8 5.8 6.4 7.1 7.8 9.5 8.8 9.4 9.6 8.8

Visits by Hypertensive Patients in Past 12 Months, 2001 NAMCS and NHAMCS

Number of visits during past 12 months

Percent

1 visit or new patient

8.7

2-3 visits

21.6

4-6 visits

32.6

7+ visits

30.3

Unknown

6.9

Percent of Patients with Hypertension and Diabetes Prescribed an Antihypertensive, by Therapeutic Class, 2001

Antihypertensives

ACE Inhibitor

Calcium Channel Blocker

Beta Blocker

Diuretic

Other Antihypertensive Drug

% of Patients

58.9

38.4

26.3

31.2

29.9

Continuing Challenges

  • Cover changes in technology and delivery
  • Obtain richer clinical data
  • Track medical management and health outcomes over time
  • Cover range of specialties and organizations
  • Anticipate and respond to clinical and public policy concerns

12:15 pm
-
Lunch

Lunch

1:30 pm - 5:00 pm
-
Afternoon Session

Afternoon Session: Presentations on health care delivery systems and other large surveillance programs and data system focusing on:

  • Design/Infrastructure
  • Strengths and limitations
  • Lessons learned
  • Opportunity to partner or piggyback to address data needs in CV
  • Value as a model for new studies to address data needs in CV incidence/prevalence, care and outcomes

Moderator: Dr. Burke

1:30 pm
-
Health care delivery systems and/or databases
Veterans Health Administration - Dr. Robert Jesse

The Veterans Affairs (VA) Health Care System

  • Largest integrated health care system in U.S.

    • 21 regions
    • 163 hospitals (1,139 total facilities)
      • 75 hospitals with cardiac cath facilities
      • 44 hospitals with onsite PCI/CABG capability
  • System challenges include comprehensive and consistent regional cardiac care delivery

Veterans Health Administration (VHA): Computerized Patient Record System (CPRS)

  • Every medical center has the Computerized Patient Record System
  • 65% Medical Centers have "filmless" images

What Does CPRS Provide?

  • Legibility
  • Computerized physician order entry
  • Real-time clinical reminders
  • Recall - limited search functions

What Does CPRS Not Provide?

  • National aggregate of clinical data
  • Disease specific data sets
  • Certain defined data fields are lacking (i.e. LVEF)
  • Forced data entry
  • Robust search function

Optimizing the Electronic Medical Records (EMR) -- What Do We Need?

  • Forced entry for key data elements
  • Care coordination and disease management functionality
  • Comprehensive critical pathway support
  • Computerized decision support and other 'E-health' strategies
  • Audit and feedback with benchmarking

CART-CL (Cardiovascular Assessment Reporting and Tracking System for Cath Labs)

  • National VHA Cath Lab database, including software for data entry and report generation for all 75 VA Cath Labs
  • Features and Function
    • Core data elements and data standards
    • Integrated into regular clinical care
      • Fully integrated into existing electronic medical record
      • Creates standardized pre-cath, cath, & PCI reports
    • Centralized national data repository
      • Not '75 databases for 75 cath labs'
    • National QI program - feedback to sites with benchmarking
      • VA to join ACC-NCDR

Performance Measures -- Improving Outcomes in VHA

  • EPRP (External Peer Review Program)
    • Chart Abstraction (100% of AMI diagnosis)
    • Performance measures and supporting data
  • IHD-QUERI (Ischemic Heart Disease Quality Enhancing Research Initiative)
    • Chart Abstraction (100% of AMI diagnosis)
    • Performance measures and supporting data

The Matrix for Quality Assessment: Event capture and timelines

 

Primary Prevention

Secondary Prevention

Initial Presentation

Discharge

Follow-up

Assessment Risk

    

AMI

  

60 days

Evaluation Testing

    

ECG, Tn

    

Therapy

    

ASA, BB

BB, ACE,
ASA, Statin

  

Education Counseling

      

Smoking Cessation

  

Clinical Events

    

PCI, Lytics

    

The Matrix for Quality Assessment: Antecedent Care

 

Primary Prevention

Secondary Prevention

Initial Presentation

Discharge

Follow-up

Assessment Risk

Lipids, DM, HTN

  

ACS

    

Evaluation Testing

          

Therapy

          

Education Counseling

A Plan

        

Clinical Events

          

VHA Performance Measures

  • ECG within 10 minutes of arrival (or 15 mins prior)
  • Troponin value returned within 60 mins of order
  • Reperfusion therapy in all eligible STEMI patients
  • Primary PCI 'door to balloon' within 120 mins
  • Thrombolysis 'door to needle' within 30 mins
  • Cardiologist involvement within 24 hours (all AMI)
  • Cath prior to discharge (all moderate/high risk ACS)
  • Cardiologist f/u within 60 days (all ACS discharges)
  • BP <140/90 mmHg; LDL <100 mgDL; Tobacco counseling
  • LDL measured and at goal w/in 2 years prior to ACS event

What's on the Horizon?

  • Re-hosting of CPRS
    • Oracle-based
    • JAVA scripting
  • National Clinical Data Repository
    • A single clinical database
  • Broad use of CART-like functions
    • Disease/condition based data entry and clinical support to optimize care.
  • The true Evidence-Based Medical Record

1:30 pm
-
Health care delivery systems and/or databases
HMO Populations and Clinical Databases as a Source for Monitoring Trends in CVD Morbidity & Mortality - Dr. Joseph Selby

Key Points

  • Integrated HMOs are an increasingly rich source of longitudinal data on CVD events, risk factors and quality of care
  • Membership is quite stable
  • Large registries of persons with CVD diagnoses are being created
  • Many of these HMOs share the same electronic medical record, and data definitions are being harmonized across HMOs

HMO Research Network (HMORN) -- 13 million members

  • Fallon Health Care, Worcester, MA
  • Group Health Cooperative, Seattle, WA
  • Harvard Pilgrim Health Care, Boston, MA
  • HealthPartners Research Foundation, Minneapolis, MN
  • Henry Ford Health System, Detroit, MI
  • Kaiser Permanente Colorado, Denver, CO
  • Kaiser Permanente Georgia, Atlanta, GA
  • Kaiser Permanente Hawaii, Honolulu, HI
  • Kaiser Permanente Northwest, Portland, OR
  • Kaiser Permanente Northern CA, Oakland, CA
  • Kaiser Permanente Southern CA, Pasadena, CA
  • Lovelace Clinic Foundation, Albuquerque, NM
  • Scott and White Health System, Temple, Texas
  • United Healthcare, Minnetonka, MN

Major Collaborative Projects of the HMO Research Network (HMORN)

Project

Funder

Coordinating Center
Vaccine Safety Datalink - CDC CDC CDC
Cancer Research Network - National Cancer Institute NCI Group Health Cooperative
Center for Education and Research on Therapeutics (CERT) AHRQ Harvard Pilgrim
Coordinated Clinical Studies Network Roadmap/
NHLBI		 Group Health Cooperative

Data are Improving

  • Membership (denominators & demographics)
  • Complete hospital discharge data (endpoints, procedures, comorbidities)
  • Outpatient diagnoses & procedures (comorbidities and endpoints)
  • Pharmacy data (comorbidities, quality of care)
  • Laboratory results (risk factors)
  • Outpatient measurements (BP, smoking status, BMI)

Availability of CVD Risk Factor Data among 1.2 million persons, age 45 and above, 1/1/04; Kaiser Permanente Northern California

  Men (n=580,817) Women (n=663,661)
Blood Pressure 86% 92%
LDL-C 68% 69%
HDL-C 73% 75%
Fasting Glucose 63% 64%
Current Smoking Status 85% 92%
Creatinine 81% 84%
BMI 55% 60%
Race/ethnicity 70% 75%

Examples of Studies Conducted in Kaiser Permanente, Northern California

  • Joint Control of Three CVD Risk Factors in the KP Population, 2001 to 2003:
    The 3D Study -- Support: Pfizer, Inc.
  • Troponin Measurement and Trends in Acute Coronary Syndrome Hospital Discharge Diagnosis
     
    • To determine the role of troponin measurement in the changing distribution of ACS hospitalization discharge diagnoses in Kaiser Permanente Northern California (KPNC)
    • All data, including cardiac biomarkers, were obtained from KPNC electronic databases.
    • All hospital discharges for ACS from KPNC hospitals between 1994-2003 were determined (primary discharge codes 410.x, 411.1, 414.x primary with 411.1 secondary)
  • Kaiser Permanente Acute Coronary Syndrome Registry (KP-ACS)
    • Modeled after National Registry of Myocardial Infarction (NRMI)
    • Systematic chart review of all hospitalized acute myocardial infarction and troponin (+) unstable angina patients at 17 Kaiser facilities since 2002 (AMI) & 2004 (UA)
    • ~9,000-10,000 reviewed cases annually
  • Kaiser Permanente Chronic Heart Failure Registry (KP-CHF)
    • Chronic heart failure (CHF) identified from hospitalizations and ambulatory visit (outpatient, ED) databases
    • 96% of primary hospital discharge diagnoses and 85-90% of cases identified from outpatient diagnoses are verified using Framingham criteria at chart review
    • 1996-2002: 59,772 adults > or = 20 years met registry criteria for CHF in Kaiser No. Cal.
    • Registry is updated annually and linked to treatment & clinical outcomes

Conclusions

  • Data from integrated HMOs can provide a timely look at incidence, complications, mortality for a variety of CVD conditions
  • Rich clinical data allows adjustment for population differences in comorbidities and disease severity
  • Data definitions can be standardized across settings
  • Data may allow exploration of possible artifactual differences in observed patterns

Limitations

  • HMO patients do not represent the extremes of U.S. SES spectrum
  • Time trends, geographic variation, subject to differences and changes in clinical and coding practices
  • Data completeness is sometimes a question
  • Potentially, time trends may be affected by differing enrollment/departure over time

1:30 pm
-
Health care delivery systems and/or databases
Academic Medical Centers - Dr. Veronique Roger

Rochester Epidemiology Project (REP)

  • The REP is not a database, it is a records linkage system
  • REP studies are labor intensive
  • Information in multiple sources, paper, and electronic format. Formats vary across sources and over time. Increasing need for IT?
  • Access to over half a century of ~ complete IN and OUT patient data on a geographically-defined population

Olmsted County, MN, Laboratory for Epidemiological Studies

  • Geographically isolated, home of Mayo Clinic. Almost all care delivered by a few providers.
  • Since 1907, Mayo patients assigned a unique identifier, information in a unit medical record (hospitals, offices, ED)
  • Since 1945, diagnoses and surgical procedures indexed
  • REP expanded indexing and medical records linkage to non-Mayo care providers.
  • Lengthy follow-up for fatal/nonfatal outcomes. Follow-up for vital status ~ complete
  • Population-based denominators from decennial

Electronic Medical Record (EMR) and Research

  • For clinical / QA,QC / financial needs
  • Research second step
  • Mayo/IBM Life Sciences system (live July 2005) umbrella for:
     
    • Clinical notes (dictated)
    • Lab/imaging tests
    • Resources utilization/Billing data

Use of the EMR for surveillance

  • Examples
    • Active recruitment of Acute Coronary Syndrome (ACS) using lab data
    • Active recruitment of Heart Failure (HF) Using Natural Language Processing from clinical notes
  • Need to:
    • Identify population
    • Validate cases using definitions, manually
    • Conduct research-driven measurements
  • Future directions
    • Tracking of non-fatal outcomes
    • Tracking of heath care delivery patterns

Strengths and limitations

  • Strengths
    • Opportunity for efficiencies, timeliness
    • High quality detailed clinical documentation
    • Access to outpatient data
    • Active surveillance capabilities
    • The population
  • Limitations
    • Systems designed for clinical purposes
    • Research applications considered at best as an after thought, often not at all
    • Human interface still needed
    • Still labor intensive
    • The population

Lessons learned

  • EMR systems in academic medical centers differ from one another, are fragmented and at various stages of maturation
  • Sizable challenges for collaborative work
  • As study needs/designs differ, customization likely unavoidable

Opportunity to partner to address CV data needs

  • Clinical and research interfaces between: a) Clinical research - Drug/device trials and b) Epi/HSR research, pharmaco-epi, QA...
    • a) Clinical research - Drug/device trials
      • Study operations
      • Study data
      • Document management
    • b) Epi/HSR research, pharmaco-epi, QA...
      • Patient information environment
  • For surveillance, need denominators/defined populations

Value of the model for new studies to address CV data needs

  • Linkage system
  • Population denominators
  • Replication conceptually easier in electronic world
  • IT intensive

References

  • Bristol N, Lancet, 2005; 365: 1610-1611
  • Pakhomov SV, Journal of Biomedical Informatics; 2005 38:145-153
  • Academic Health Centers' Clinical Research Forum (http://ahcforum.org)
  • Association of American Medical Colleges (Recommendations from conference in IT enabling clinical research, 2002)

2:15 pm
-
Other Surveillance Systems/Registries:
National Emergency Medical Services Information System - Dr. Greg Mears

National EMS Information System(link is external)

N E M S I S
National EMS Information System
www.nemsis.org(link is external) image of external icon

What is NEMSIS? The National Association of State EMS Directors is working with the National Highway Traffic Safety Administration (NHTSA) and the Trauma/EMS Systems program of the Health Resources and Services Administration (HRSA) to develop a national EMS database. The uses of such a database include:

  • Developing nationwide EMS training curricula.
  • Evaluating patient and EMS system outcomes.
  • Facilitating research efforts.
  • Determining national fee schedules and reimbursement rates.
  • Addressing resources for disaster and domestic preparedness.
  • Providing valuable information on other issues or areas of need related to EMS care.

The NEMSIS Project. The NEMSIS grant is an ongoing project with the following tasks:

  • Promote the NHTSA Uniform Pre-Hospital Dataset.
  • Promote the National EMS Information System Dataset.
  • Promote the National EMS Information System Business Implementation Model.
  • Design and implement a Pilot National EMS Database with web reporting capabilities as a proof of concept.
  • Support and promote state EMS data initiatives directed at implementing NEMSIS.

Participating States. 48 States NASEMSD states and 3 territory members have formally agreed to promote and support all EMS data initiatives within their states to conform to the NHTSA Version 2 dataset. These states will call upon vendors of EMS information system software to assist in implementing the National EMS Information System.

Components of NEMSIS

  • The NHTSA Uniform Pre-Hospital EMS Dataset, Version 2.
    • A comprehensive dataset which serves as a reference to local and state EMS systems as they develop and implement EMS data systems.
    • Captures the following components of EMS service and patient care delivery:
      • Dispatch Data
      • Incident Data
      • Patient Data
      • Demographics
      • Medical History
      •  Assessment
      • Medical Device Data
      • Treatments/Medications
      • Procedures
      • Disposition
      • Injury/Trauma Data
      • Cardiac Arrest Data
      • Financial Data
      • EMS System Demographic Data
      • EMS Personnel Demographic Data
      • Quality Management Indicators
      • Outcome Indicators
      • Domestic Terrorism Data
      • Linkage Data
  • National EMS Dataset
    • A small subset of the NHTSA Uniform Pre-Hospital dataset consisting of 68 key data elements.
    • This is the minimal dataset each state is recommended to collect and submit.
    • Each state may choose to collect any amount of data between the full NHTSA Uniform Pre-Hospital Dataset and the National EMS Dataset.
    • Used to establish the National EMS Database.
  • National EMS Database
    • The "warehouse" which will store the data collected from each state based on their chosen dataset.
    • Web-based reporting capability to better define and describe EMS systems, personnel, and services across the country.
  • XML standard
    • Allows new and existing EMS datasets to import or export data based on the version 2 NHTSA dataset standards.
  • Business Model
    • Used for the deployment of the National EMS Information System at the local, state, and national levels.

Completed Components

  • NHTSA Version 2 Uniform Pre-Hospital Dataset
  • NHTSA Version 2 XML Data Standard
  • National EMS Database Dataset (66 data-element subset of the NHTSA Version 2 Dataset)
  • NEMSIS Business Model and Implementation Plan

Organizational Participation

  • Professional
    • American Ambulance Association (AAA)
    • American College of Emergency Physicians (ACEP)
    • American College of Surgeons: Committee on Trauma (ACS-COT)
    • American Heart Association (AHA)
    • Emergency Medical Research Outcomes Project (EMSOP)
    • International Association of Fire Chiefs (IAFC)
    • International Association of Fire Fighters (IAFF)
    • National Academy of Emergency Medical Dispatch (NAEMD)
    • National Association of EMS Physicians (NAEMSP)
    • National Association of EMS Quality Professionals (NAEMSQP)
    • National Association of State EMS Directors (NASEMSD)
    • National Emergency Number Association (NENA)
  • Federal
    • Centers for Disease Control (CDC)
    • EMSC National Resource Center (NRC)
    • Federal Emergency Management Administration (FEMA)
    • HRSA's Emergency Medical Services for Children Program (EMSC)
    • HRSA's Office of Rural Health Policy (ORHP)
    • HRSA's Trauma/EMS Systems Program
    • National EMSC Data Analysis Resource Center (NEDARC)
    • National Highway Traffic Safety Administration (NHTSA)

Funding: This contract is funded by the Health Resources and Services Administration (HRSA) Trauma/EMS Systems Program and the National Highway Traffic Safety Administration (NHTSA).

Contact Information

Greg Mears, MD, FACEP
Principal Investigator
University of North Carolina-Chapel Hill
Phone: (919) 843-0201
E-mail: gdm@med.unc.edu

N. Clay Mann, PhD, MS
Co-Investigator
University of Utah School of Medicine
Intermountain Injury Control Research Center
Phone: (801) 581-6410 
E-mail: Clay.Mann@hsc.utah.edu

2:15 pm
-
Other Surveillance Systems/Registries:
State-wide Surveillance - Dr. Eduardo Sanchez

After noting that not all state health departments share the same set of responsibilities and activities, Dr. Sanchez described the breadth of relevant data collection efforts at the Texas State Department of Health, including those concerning emergency medical services (EMS), hospital care, vital statistics, and chronic disease measures.  The state has numerous data sources, though many are disparate.  These include mortality data (inpatient and outpatient); hospital discharge and cost data from more than 80% of the state?s hospitals; behavioral data and trends from the Behavioral Risk Factor Surveillance System, the Youth Tobacco Survey, and the Youth Risk Factor Surveillance System; Health Plan Employer Data and Information Set; the Texas EMS/Trauma Registry; Medicaid and Medicare data; and the national voluntary hospital reporting initiative data available from CMS.  Texas? quality improvement organization (QIO) has focused on the outpatient setting and has not looked at cardiovascular disease or stroke but these diseases could potentially be added. 

Dr. Sanchez identified several gaps and opportunities to improve the applicability of these databases for CVD research.  Integration of data collected from various sources is needed, as is application of collected data from such sources into information that can direct policy making and program development.  He suggested analyzing hospital discharge and cost data to determine the cost of achieving the decline in MI rates in recent years.   He also emphasized the importance of tying local data to national standards.  Our definition of health care should be broadened to include public health as well as health care delivery when collecting and interpreting data as well as when using the results to inform policies and programs. Outpatient health care delivery should include diverse community settings such as churches, the workplace, and lay health worker settings for data gathering as well as for health promotion.  Greater adoption of electronic medical records is expected to be very helpful in improving data collection and linking among databases.

2:15 pm
-
Other Surveillance Systems/Registries:
Privately Funded Cardiovascular Registries - Dr. Eric Peterson*

*Acknowledgment: Receive research support from Schering Plough, BMS, Sanofi, and Millennium Pharmaceuticals

NRMI (National Registry of Myocardial Infarction) Study Overview

  • Multicenter acute myocardial infarction (AMI) registry (Genentech sponsor)
  • Established in 1990 as FDA post market study
  • Data: Demographics, clinical (presenting symptoms, risk factors), in-hospital care (meds, revascularization) and clinical events.
  • Hospitals: 1600 peak, now 450-500
  • Site feedback: extensive reporting, benchmarks, trends, JCAHO vendor
  • Oversight: Company owns database 
     
    • National advisory board (oversees publications)

NRMI Strengths /Limitations

  • Strengths:
    • Research: >75 scientific papers
    • Epidemiology: Source for US MI care and outcomes in community practice.
    • Quality: Established concept of "door to rx"
  • Limitations:
    • Sponsors control database
    • Voluntary hospital participation
    • Data audits (limited validation in1990's)
    • In-hospital data only

CRUSADE (Can Rapid Risk Stratification of Unstable Angina Patients Suppress Adverse Outcomes with Early Implementation of the ACC/AHA Guidelines) Study Overview

  • Multicenter NSTE ACS registry (STEMI recently added)
  • Multi-sponsor: Millennium + Schering Plough, + BMS-Sanofi + Sanofi-Aventis + Merck-Schering Plough
  • Established in 2000 as Quality Improvement Initiative
  • Hospitals: 400+, mixed like NRMI (3/4th non-academic)
  • Data: In-hospital clinical, treatment, outcomes data
  • Site feedback: extensive reporting, benchmarks, trends, JCAHO vendor
  • Oversight: DCRI owns database: 
     
    • National advisory board oversees publications

CRUSADE Site Distribution

  • Total sites = 486 (409 active)
  • Total Patients = 140,000+

Baseline Characteristics: CRUSADE vs. ACS Clinical Trials*

Variable PURSUIT (n = 9461) CURE (n = 12,562) SYNERGY(n=9975) CRUSADE (n = 119,046)
Mean age +/- SD (Yrs) 63 ± 11 63 ± 12 67 ± 11 68 ± 14
Female sex (%) 36 39 34 40
Diabetes mellitus (%) 23 23 29 33
Prior MI (%) 32 25 28 30
Prior CHF (%) 11 8 9 18
Prior PCI (%) 13 18 20 21
Prior CABG (%) 12 18 17 20
ST depression (%) 50 42 55 37

*NEJM 1998;339:436-43; NEJM 2001;345:494-502; JAMA 2004:292:45-54; CRUSADE cumulative through September 30, 2004

CRUSADE: Beyond Registry

  • Quality Improvement
    • National/regional QI meetings
    • Educational / QI tools/newsletters
    • Quarterly Site Feedback Reports
    • 1 on 1 visit/calls to sites
  • Collaborations
    • VA: planed use of Crusade for benchmark
    • UHC: Premium hospitals (also NRMI, GWGH)
    • AHA GWTG: EDC cross-walk, joint program efforts
  • Other programs
    • NIA Grant: Bleeding in Elderly (pending)
    • Longitudinal compliance Study (3000+ pts)

CRUSADE Strengths /Limitations

  • Strengths:
    • Research: 50+ abstracts/papers
    • Epidemiology: Source for US NST ACS care and outcomes in community practice.
    • Quality: Established association b/t hospital guidelines adherences---outcomes
  • Limitations:
    • NSTE ACS (STE MI recently added)
    • Voluntary hospital participation
    • Data audits (limited validation, ongoing)
    • In-hospital data only

Other Sponsor Databases

  • Get With The Guidelines (AHA + sponsor supported)
    • CAD
    • Stroke
    • Heart failure
  • GRACE (International MI, limited US)
  • ADHERE (heart failure)

Opportunities for Partnership

  • Most/all programs willing to share data
  • Programs generally committed to new knowledge generation/translation
  • Efforts afoot to collaborate
     
    • Standardize data elements
    • Share data among?

Value as a Model

  • Sites are willing to collect high quality data for a good cause with minimal support
    • i.e., value of data feedback itself
  • Important insights gained from community based sponsor funded registries
  • Limited longitudinal data to date 
     
    • Greatest unmet need

2:15 pm
-
Other Surveillance Systems/Registries:
The Finnish Experience With a National CVD Event Register During 1991-2003 - Dr. Veikko Salomaa

There are two types of population-based cardiovascular disease registers in Finland. The FINAMI register (1,2) is based on detailed reviewing of hospital documents, death certificates and autopsy reports following the tradition of the WHO MONICA Project. It is operating in four geographical areas of Finland and aims to register every CHD event in monitored populations. The register is planned for scientific research and has standardized data collection and quality control procedures. Therefore, the data it produces can be considered as accurate and reliable. The limitation is that the register is rather laborious and expensive to maintain and can cover only a small fraction of the country. The other type of CVD register is based on record linkage of administrative data, i.e., the National Hospital Discharge Register and the National Causes-of-Death Register (3,4). These country-wide computerized registers cover every hospitalisation in Finland and all deaths of permanent residents of the country. They can be linked together on the basis of the personal ID code, unique to every resident of Finland. Data on CHD and stroke events during 1991-2003 based on the record linkage of these administrative registers have been placed in the Internet, where they are freely available. The database has a user-friendly interface, which allows easy examination of event rates, in-hospital and out-of-hospital case fatality as well as one-year prognosis by age, sex, and hospital district. The strength of the administrative registers is that they cover the whole country and all age groups and provide data on large numbers of CVD events at a modest cost. For example, our database for the period 1991-2003 includes 333 015 CHD and 304 863 stroke events. On the other hand, only a limited standardization and quality control are possible for the administrative data. However, the more detailed FINAMI register can be used as a validation instrument for the country-wide administrative CVD-register. Validation studies have shown that the sensitivity and positive predictive value of CVD diagnoses in the Finnish administrative registers are reasonably good (5). Similar national CVD registers based on administrative data exist also in Sweden and Denmark. In the framework of the NORDAMI Project we are currently establishing common definitions for CVD events in these registers. The goal is that in the near future reasonably comparable data on the incidence and case fatality of CVD events in these three Nordic countries become freely available at a common website. At the European Union level, the EUROCISS (=European Cardiovascular Indicators Surveillance Set) Project aims to identify a set of CVD indicators, for which monitoring is both needed and feasible in the whole EU (6).

Figure 1 demonstrates the trends in the incidence of first ever MI events in Finland during 1991-2003, based on the administrative data. The decline was steep until 1997 and somewhat slower after that, which is likely to be due to the widespread adoption of troponins as the markers of myocardial injury. We have analyzed the effects of changing diagnostic criteria on the event rate estimates of MI using data from the FINAMI register (2). The findings suggested that the estimates of hospitalised CHD events increased by 15% among men and 38% among women aged 35-74 years with the adoption of troponins and the latest diagnostic criteria (7). Somewhat surprisingly, the additional cases identified by troponins and the new criteria had worse prognosis than those cases, which were definite MIs also according to the WHO MONICA criteria based on enzymatic markers of myocardial injury.

In conclusion, a country-wide CVD register based on administrative data and a geographically limited but more rigorously standardized register are used in a complementary manner in Finland. Together they provide a fairly good picture on the occurrence, case fatality and prognosis of CVD events in the country.

Incidence of first MI 1991-2003 Finland and link to data table

Figure 1. The age-standardized incidence of first MI events in 1991-2003 in Finland. The annual average decline was 4.9% (95% CI -5.2% to -4.5%) from 1991 to 1997 and 2.1% (95% CI -2.5% to -1.8%) from 1998 to 2003 among men. The respective changes among women were -5.2% (95% CI -5.8% to -4.7%) and -2.1% (95%CI -2.7% to -1.5%).

 

References:

  1. Salomaa V, Ketonen M, Koukkunen H, Immonen-Räihä P, Jerkkola T, Kärjä-Koskenkari P, Mähönen M, Niemelä M, Kuulasmaa K, Palomäki P, Mustonen J, Arstila M, Vuorenmaa T, Lehtonen A, Lehto S, Miettinen H, Torppa J, Tuomilehto J, Kesäniemi YA, Pyöralä K. Decline in out-of-hospital coronary heart disease deaths has contributed the main part to the overall decline in coronary heart disease mortality rates among persons 35 to 64 years of age in Finland: the FINAMI study. Circulation 2003;108:691-696.
  2. Salomaa V, Koukkunen H, Ketonen M, Immonen-Räihä P, Kärjä-Koskenkari P, Mustonen J, Lehto S, Torppa J, Lehtonen A, Tuomilehto J, Kesäniemi A, Pyörälä K, for the FINAMI Study Group. A New Definition for Myocardial Infarction - What Difference Does it Make?, Eur Heart J, in press, doi:10.1093/eurheartj/ehi185.
  3. Pajunen P, Pääkkönen R, Hämäläinen H, Keskimäki I, Laatikainen T, Niemi M, Rintanen H, Salomaa V. Trends in fatal and non-fatal strokes among persons aged 35-85+ years during 1991-2002 in Finland. Stroke 2005;36:244-248.
  4. Pajunen P, Pääkkönen R, Juolevi A, Hämäläinen H, Keskimäki I, Laatikainen T. Moltchanov V, Niemi M, Rintanen H, Salomaa V. Trends in fatal and non-fatal coronary heart disease events in Finland during 1991-2001. Scand Cardiovasc J 2004;38:340-344.
  5. Pajunen P, Koukkunen H, Ketonen M, Jerkkola T, Immonen-Räihä P, Kärjä-Koskenkari P, Mähönen M, Niemelä M, Kuulasmaa K, Palomäki P, Mustonen J, Lehtonen A, Arstila M, Vuorenmaa T, Lehto S, Miettinen H, Torppa J, Tuomilehto J, Kesäniemi YA, Pyörälä K, Salomaa V. The validity of the Finnish Hospital Discharge Register and Causes of Death Register data on coronary heart Disease. Eur J Cardiovasc Prev and Rehabilit 2005;12:132-137.
  6. The EUROCISS Working Group. Coronary and cerebrovascular registers in Europe: Are morbidity indicators comparable? Results from the EUROCISS Project. Eur J Publ Health 2003;13(suppl 3):55-60.
  7. Luepker RV, Apple FS, Christenson RH, Crow RS, Fortman SP, Goff D, Goldberg RJ, Hand MM, Jaffe AS, Julian DG, Levy D, Manolio T, Mendis S, Mensah G, Pajak A, Prineas RJ, Reddy KS, Roger VL, Rosamond WD, Shahar E, Sharrett AR, Sorlie P, Tunstall-Pedoe H. Case definitions for acute coronary heart disease in epidemiology and clinical research studies: a statement from the AHA Council on Epidemiology and Prevention; AHA Statistics Committee; World Heart Federation Council on Epidemiology and Prevention; the European Society of Cardiology Working Group on Epidemiology and Prevention; Centers for Disease Control and Prevention; and the National Heart Blood and Lung Institute. Circulation 2003;108:311-319.

3:15 pm
-
Break

Break

3:30 pm
-
Studies assessing clinical management patterns and/or patient outcome for:
Worcester Heart Attack Study - Dr. Robert Goldberg

The Worcester Heart Attack Study is an ongoing population-based investigation examining changing trends (1975-2003) in the incidence rates, hospital and post discharge death rates, occurrence of major clinical complications, and use of different management approaches in greater Worcester (MA) residents hospitalized with independently validated acute myocardial infarction (AMI) at all metropolitan Worcester hospitals. Secondary goals of this project are to examine changes over time in duration of prehospital delay following the onset of acute coronary symptoms and community mortality related to coronary heart disease (CHD) in the greater Worcester population. This study has been funded by the NHLBI on an ongoing basis since the mid-1980's.

To accomplish these and additional study objectives, the medical records of residents of the Worcester metropolitan area (2000 census estimate = 478,000) hospitalized with discharge diagnoses suggestive of AMI and CHD are reviewed. The primary ICD-9 codes we select for purposes of identifying cases of AMI include codes 410 (AMI), 411 (angina), 412-414 (ischemic heart disease and/or chronic coronary atherosclerosis), and 786.5 (chest pain). The study periods included to date are 1975, 1978, 1981, 1984, 1986, 1988, 1990, 1991, 1993, 1995, 1997, 1999, 2001, and 2003. The medical records of patients satisfying the study's diagnostic and geographic (resident of metropolitan Worcester) eligibility criteria are reviewed in a standardized manner by trained study physicians and nurses. To date, a total of 12,760 patients with independently confirmed AMI have been included in this population-based investigation.

Patients with possible AMI are identified through the use of passive (cold pursuit) disease surveillance. Computerized hospital printouts, restricted to residents of the Worcester metropolitan area, are reviewed several months after patients have been discharged from all greater Worcester hospitals (present n=11 which was formerly 16) with diagnoses suggestive of AMI. The study sample is selected from this available pool of geographically eligible patients.

Information is collected about patient's age, sex, race, insurance status, medical history (e.g., AMI, CHD, diabetes, heart failure, hypertension, stroke), presenting symptoms, duration of prehospital delay in seeking acute medical care, body mass index, laboratory (e.g., serum electrolytes, blood urea nitrogen, hematocrit, platelets, cholesterol) and physiologic measures (e.g., blood pressure, heart rate), acute clinical complications (e.g., atrial fibrillation, cardiogenic shock, heart failure), medications (e.g., ACE inhibitors, aspirin, beta blockers, calcium antagonists, lipid lowering agents, thrombolytics), diagnostic procedures (e.g., echocardiography, radionuclide scans, treadmill testing), and coronary interventions (e.g., PCI, coronary artery bypass surgery), hospital length of stay, and hospital survival status.

Patients discharged from all greater Worcester hospitals are followed up through a variety of sources (presently available through 2003) to ascertain patient's long-term survival status and possible changes in patient's post discharge survival over time.

With regards to the principal study findings, we observed initial increases, followed by declines, and then relative stabilization in the incidence rates of initial AMI (Figure 1). Patients hospitalized with AMI during more recent study years are increasingly older and present with a greater prevalence of comorbidities (Table 1). The crude and multivariable adjusted risk of dying during hospitalization has declined over the periods under study (Table 2). There have been marked increases over time in the use of various medical treatment approaches (Figure 2) and coronary reperfusion strategies (Figure 3).

Table 1
Changing Face of AMI: Worcester Heart Attack Study

Characteristic

1975/78

1986/88

2001/2003
Age (median, yrs)

66

69

74
Male (%)

62

60

56
Medical history (%)      
Angina

24

27

22
Diabetes

22

25

33
Hypertension

41

49

71
Heart Failure

14

14

24
Stroke

5

9

12

Table 2
Trends in Hospital Case-Fatality Rates (CFR): Worcester Heart Attack Study

Time Period of Hospitalization

n

CFR (%)

Multivariable
Adjusted Risk

95% CI

1975/78

1626

20.8

1.0

---

1981/84

1712

17.4

0.69

0.57, 0.82

1986/88

1424

17.3

0.67

0.56, 0.81

1990/91

1514

15.1

0.53

0.43, 0.64

1993/95

1794

13.2

0.44

0.37, 0.53

1999/01

2264

11.6

0.36

0.30, 0.43

Figure 1: Trends in Age Adjusted Incidence Rates of Initial AMI

Trends in AMI incidence, link to data tables

Figure 2: Trends in Use of Selected Medications

Trends in Med Use, link to data tables

Figure 3: Trends in Use of Coronary Reperfusion Strategies

Trends in Coronary Reperfusion, link to data table

 

A number of publications have resulted from the Worcester Heart Attack Study to date. Examples of these publications are provided in the following areas of hospital incidence rates, hospital and long-term case fatality-rates, trends in management approaches, and changing demographic, clinical, and medical care seeking profile.

Incidence and Case-Fatality Rates

  1. Goldberg RJ, Gore JM, Alpert JS, Dalen JE: Recent changes in the attack rates and survival rates of acute myocardial infarction (1975-1981); The Worcester Heart Attack Study. JAMA 255:2774-2779, 1986.
  2. Goldberg RJ, Yarzebski J, Lessard D, Gore JM. A two-decades (1975-1995) long experience in the incidence, in-hospital and long-term case-fatality rates of acute myocardial infarction: A community-wide perspective. J Am Coll Cardiol 33:1533-1539, 1999.
  3. Furman MI, Dauerman HL, Goldberg RH, Yarzebski J, Lessard D, Gore JM: Twenty-two year (1975 to 1997) trends in the incidence, in-hospital and long-term case-fatality rates from initial Q wave and non-Q wave myocardial infarction: A multi-hospital, community-wide perspective. J Am Coll Cardiol 37:1571-80, 2001.
  4. Spencer FA, Lessard D, Gore JM, Yarzebski J, Goldberg RJ. Declining length of hospital stay for acute myocardial infarction and post-discharge outcomes: A community-wide perspective. Arch Intern Med 164:733-40, 2004.
  5. Goldberg RJ, Spencer FA, Yarzebski J, Lessard D, Gore JM, Alpert JS, Dalen JE. A 25-year perspective into the changing landscape of patients hospitalized with acute myocardial infarction (the Worcester Heart Attack Study). Am J Cardiol 94:1373-1378, 2004.

Impact of, and Trends in, Clinical Complications of AMI

  1. Goldberg RJ, Samad NA, Yarzebski J, Gurwitz J, Bigelow C, Gore JM. Temporal trends (1975-1997) in the incidence and hospital death rates of cardiogenic shock complicating acute myocardial infarction (Worcester Heart Attack Study). N Engl J Med 340:1162-1168, 1999.
  2. Spencer FA, Meyer TE, Goldberg RJ, Yarzebski J, Hatton M, Lessard D, Gore JM: Twenty year trends (1975-1995) in the incidence, in-hospital and long-term death rates associated with heart failure complicating acute myocardial infarction. A community-wide perspective. J Am Coll Cardiol 34:1378-1387, 1999.
  3. Goldberg RJ, Yarzebski J, Lessard D, Wu J, Gore JM. Recent trends in the incidence rates of and death rates from atrial fibrillation complicating initial acute myocardial infarction: A community-wide perspective. Am Heart J 143:519-27, 2002.
  4. Spencer FA, Gore JM, Yarzebski J, Lessard D, Jackson EA, Goldberg RJ. Trends (1986-1999) in the incidence and outcomes of in-hospital stroke complicating acute myocardial infarction (The Worcester Heart Attack Study). Am J Cardiol 92:383-388, 2003.

Changing Treatment Practices Over Time

  1. Yarzebski J, Goldberg RJ, Gore JM, Alpert JS: Temporal trends and factors associated with pulmonary artery catheterization in patients with acute myocardial infarction. Chest 105:1003-08, 1994.
  2. Col NF, McLaughlin TJ, Soumerai SB, Hosmer Jr DW, Yarzebski J, Gurwit JH, Gore JM, Goldberg RJ: The impact of clinical trials on the use of medications for acute myocardial infarction: Results of a community-based study. Arch Intern Med 156:54-60, 1996.
  3. Spencer F, Scleparis G, Goldberg RJ, Yarzebski J, Lessard D, Gore JM. Decade long trends (1986 to1997) in the medical management of patients with acute myocardial infarction: a community-wide perspective. Am Heart J 142:594-603, 2001.
  4. Jackson EA, Sivasubramian R, Spencer FA, Yarzebski J, Lessard D, Gore JM, Goldberg RJ. Changes over time in the use of aspirin in patients hospitalized with acute myocardial infarction (1975 to 1997): A population-based perspective. Am Heart J 144:259-68, 2002.
  5. Silvet H, Spencer F, Yarzebski J, Lessard D, Gore JM, Goldberg RJ. Community-wide trends in the use and outcomes associated with beta blockers in patients with acute myocardial infarction (The Worcester Heart Attack Study). Arch Intern Med 163:2175-83, 2003. <

Delays in Seeking Acute Medical Care

  1. Goldberg RJ, Yarzebski JL, Lessard DM, Gore JM. Decade long trends and factors associated with time to hospital presentation in patients with acute myocardial infarction. The Worcester Heart Attack Study. Arch Intern Med 160:3217-23, 2000.

Gender Differences in AMI Risk or Treatment Practices

  1. Pagley PR, Yarzebski J, Goldberg RJ, Chen Z, Chirboga D, Dalen P, Gurwitz J, Alpert JS, Gore JM: Gender differences in the treatment of patients with acute myocardial infarction: A multi-hospital, community-based perspective. Arch Intern Med 153:625-29, 1993.
  2. Vaccarino V, Krumholz H, Yarzebski J, Gore JM, Goldberg RJ. Sex differences in long term mortality after myocardial infarction: effect modification due to age. Ann Intern Med 134:173-81, 2001.
  3. Harrold L, Esteban J, Lessard D, Yarzebski J, Gurwitz J, Gore JM, Goldberg RJ. Narrowing gender differences in procedural utilization in acute myocardial infarction: Insights from the Worcester Heart Attack Study. J Gen Intern Med 18:423-31, 2003.
  4. Crowley A, Menon V, Lessard D, Yarzebski J, Jackson E, Gore JM, Goldberg RJ. Sex differences in survival after acute myocardial infarction in patients with diabetes (Worcester Heart Attack Study). Am Heart J 146:824-31, 2003.
  5. Milner KA, Vaccarino V, Arnold AL, Funk M, Goldberg RJ. Gender and age differences in chief complaints of acute myocardial infarction (Worcester Heart Attack Study). Am J Cardiol 93:606-608, 2004. 

3:30 pm
-
Studies assessing clinical management patterns and/or patient outcome for:
Resuscitation Outcomes Consortium (ROC) - Dr. Laurie Morrison

Epistry Subcommittee

  • Graham Nichol (DCC)
  • Cliff Calloway (Pittsburgh)
  • Jim Christenson (BC/OPALS)
  • Diane Atkins (Iowa)
  • Craig Newgard (Portland)
  • Ron Pirallo (Milwaukee)
  • Laurie Morrison (Toronto)
  • Joe Minei (Dallas)
  • Tom Terndrup (Alabama)
  • Tom Rhea (Seattle)
  • Dan Davis (San Diego

EMS OPS members

  • Jonathan Larsen (Seattle)
  • Jamie Frank (Toronto)
  • Michael Hartley (Iowa)
  • Shannon Stevens (Alabama)

Experts
Jennifer Long Data Management (Toronto)
Berit Bardarson (DCC)
Gena Sears (DCC)
George Sopko (NIH)
Tracey Hoke (NIH)
Study Chairs
Joe Ornato, John Holcomb
Chair Mike Weisfeldt

 Resuscitation Outcomes Consortium (ROC)

Epistry Overview

  • Epidemiological Databank for ROC
  • Provide population based EMS and outcome data (field and in-hospital)
  • Baseline for all ROC interventional trials
  • Complementary to existing registries addressing the bias (missing data)
     
    • OHCA deaths (90% in field death rate),
    • EMS data
    • Non trauma centre outcomes

Vision

  • Shining a light makes a difference
  • In other words, a registry is an intervention that in and of itself can improve outcomes in participating sites

The Intervention

  • An internet based registry of standardized data pertaining to adults, infants and children with OHCA or life-threatening trauma for all ROC centers.

     

Inclusion Criteria

  • Individuals who experience out of hospital cardiac arrest in ROC communities evaluated by organized EMS personnel;
    • a) who receive external defibrillation administered by anyone, or
    • b) on whom EMS personnel perform chest compressions

Through

  • Data upload using a web based interface or
  • Download from existing data sources initially,
  • Work to standardize uniform data collection at the level of the paramedic,
  • Implement data quality initiatives and evaluate,
  • Facilitate direct electronic transfer

Potential Contributions to ROC Outcomes

  • Collect inhospital outcomes common to all ROC trials on all registry patients
  • Provide population based outcome estimates for CA and Trauma
  • Measure the crude pooled estimate of resuscitation success for the consortium- track overtime

Potential Contributions to ROC Population

  • Share best practices across ROC centers
  • Provide web based EMS operational reports and data quality management
  • Provide population estimates of program interventions i.e. bystander CPR

Potential Contributions to ROC Studies

  • Provide pilot data to define existing standards of care, sample size calculations, duration for protocol development
  • Track and define the characteristics of missed patients or excluded patients to report on generalizability

Unique Contributions to ROC Productivity

  • Quality Administrative Datasets
    • Answer research questions not amenable to randomized controlled trials
    • Evaluate policy through regional surveillance
    • Generate RO1/RO3 & CIHR grants
    • Epidemiological and surveillance manuscripts

Why Epistry is important to CV surveillance

  • Most CA generate an EMS response
  • Most die in the field
  • Most Efficacious interventions occur early
  • Population based without EMS data lacks validity

ROC Population

  • Population 26 million
    • Hospitals >101
    • EMS Systems at least 70
  • Per Annum
    • 1.8 million EMS transports
    • Trauma 46,000 (95%)
    • Cardiac Arrest 18,000 (5.2%)

Literature Search

  • MEDLINE (31)
  • EMBase (6)
  • Health Star (5)
  • Journal of Medical Internet Research 2001 (1)
  • CINAHL (8)
  • Dissertation abstracts (6)

Favorite Reference

  • Cardiac Arrest and Cardiopulmonary Resuscitation Outcome Reports: Update and Simplification of the Utstein Templates for Resuscitation Registries. Circulation. 110(21):3385-3397, Nov 23, 2004

Complementary Data - Linkable Partners

Web Sites - helpful

Minimum Data Set and Dictionary

  • 72 Minimum Data Points
    • Data variable names
    • Data response codes
    • Data dictionary
      • Definitions
      • Source
      • Intent
      • Data entry
      • Judgment

Current Linkage

  • Site Specific Survey
    • In-hospital datasets
    • Existing registries
    • Hand abstracted
    • Privacy and ethical issues

Probabilistic Matching

  • EMS linking resulted in > 90% capture with Sensitivity of 90% and Specificity of 100%.
  • OHCA; S. Waien AEM 1997, Trauma: C. Newgard AEM 2005

Peer Review

  • ReSS: Highest rated ROC protocol
  • Submission to External Agencies - AHA
  • Planned submission to NIH RO1

3:30 pm
-
Studies assessing clinical management patterns and/or patient outcome for:
Studies Assessing Clinical Management Patterns and/or Patient Outcomes for Heart Failure - Dr. Harlan Krumholz

Dr. Krumholz has studied extensively the management of heart failure. He is a clinical coordinator for the National Heart Failure (NHF) Project, a quality improvement effort launched in 1999 by the then Health Care Financing Administration (HCFA) ? now Centers for Medicare and Medicaid Services (CMS) ? to improve care for Medicare beneficiaries hospitalized with heart failure. A systematic national sample of fee-for-service Medicare patients hospitalized for heart failure in 1998-1999 was collected as part of this project. As a national inpatient program, its baseline quality indicator rates were focused on rates of ejection fraction documentation and angiotensin-converting enzyme inhibitor prescription. Data from the project have allowed him and his collaborators to assess various aspects of quality of care and patient outcomes concerning heart failure, including the treatment and outcome disparities that have been observed by gender and by race.

Dr. Krumholz pointed out that clinical management patterns of heart failure vary. For example, he speculated that it is likely that the criteria clinicians use to determine whether to treat a patient with acute exacerbation of heart failure as outpatient versus inpatient often differ among physicians and by location. Quality indicators of heart failure management are mostly limited to ejection fraction documentation and angiotensin converting-enzyme prescriptions ? for which rates are too low for both measures nationally. There do not appear to be many national quality improvement projects on the horizon that involve the collection of data that can be used for surveillance. There is a particular lack of data on outpatient management as well as outpatient self-monitoring of this disease. There is a great need to assess disparities. He expressed concern that as new and more invasive treatment technologies such as implantable cardiac defibrillators become available, the high costs of such treatment may inadvertently widen the gap of current disparities. To track the impact of marked anticipated changes in the care of patients with heart failure and the changing risk patterns of the population, there exists a great need for reliable surveillance efforts that focus on incidence, treatment, disparities, and outcome.

4:15 pm
-
Other operational issues to be considered
Contemporary Challenges to Population Studies of Cardiovascular Disease - Dr. Robert Goldberg

Disease Surveillance Questions - In designing surveillance systems suitable for global, national, or regional application:

  • What information is essential or indispensable?
  • What are the most valid, cost-efficient, and practical means for obtaining this information?
  • How can relevant data be obtained for potentially generalizable populations?
  • How can the system of surveillance become sustained?
  • How can this information continue to be made useful and of interest for different audiences?

Population-Based Approach to Surveillance

  • Broad-based perspective enhances generalizability and interpretation of findings
  • Ability to calculate incidence rates of disease and other pertinent health outcomes
  • Reflects more "real world" patients with disease, and their likely management practices, as compared to individuals studied in RCT's of more select patient samples with potentially narrow inclusion criteria

Non-Population Based Approach to Surveillance

  • Hospitals or clinics included for study may not be representative of centers from defined area
  • Patients hospitalized at select medical centers may have different characteristics from those seen in usual care settings
  • Management practices may not reflect those utilized in a community setting
  • Incidence rates of disease cannot be calculated with a "catch-can" ascertainment approach

"Cold" Pursuit Surveillance

  • Advantages
    • Complete case ascertainment
    • Cost efficiencies and minimal logistical complexities
  • Disadvantages
    • Cannot obtain supplemental data not available from medical or other records for research purposes
    • Need systematic lists and sampling frames for case selection

"Warm" Pursuit Surveillance

  • Advantages
    • Supplemental data not included in medical records can be obtained through direct patient or surrogate interviews
    • Can identify potential etiologic or prognostic factors in a more systematic and standardized manner
  • Disadvantages
    • High potential for incomplete case ascertainment
    • Greater logistical difficulties involved in identifying patients and ascertaining information
    • Increased personnel costs

Endpoints to be examined in CHD Surveillance Systems

  • New hospitalized events of AMI
  • Out-of-hospital deaths due to CHD
  • CFR's (hospital, 28 day, 1 year, longer)
  • Prehospital delay times
  • Use of EMS
  • Medical care (medications and procedures)

Obstacles to accessing and reviewing hospital and ambulatory care records

  • Ability to construct an appropriate sampling frame for sample selection
  • Reliability of ICD codes for case ascertainment
  • Selection of 1º or 2º discharge dx's of CHD for purposes of case ascertainment
  • Jumping into the "long and winding que" for accessing medical records (and importance of developing personal relationships)
  • Missing records
  • Incomplete records
  • Quality of information and lack of standardized questioning and recording
  • Quiet space for data abstractors

Data Abstraction Issues

  • Case definitions to be utilized
    • Standardized criteria
    • No upper age cap
    • Decision on transferred cases
  • Hospital data elements
  • Post discharge data elements
    • Mortality
    • Morbidity
    • QOL
    • Medication adherence

HIPAA, Consent, and Confidentiality Issues

  • Type of study to be conducted (mailed questionnaire, medical record review, phone survey)
  • Initial IRB approvals and dealing with medical care centers either without an IRB or who meet infrequently
  • Patient identifiers and matching criteria for follow-up information
  • Assuring patient confidentiality

Event Adjudication

  • Determination of which events that need continual review (e.g., UA, HF) and those which may not need further review and adjudication (e.g., receipt of CABG)
  • Need for standardized definitions
  • Need for experienced clinicians
  • Adequate "case" information to validate or rule out from further consideration
  • Maintaining group interest in review process and emphasizing importance of reviewers task

4:15 pm
-
Other operational issues to be considered
Data Management Issues - Dr. Wayne Rosamond

Some data management issues to consider

  • Infrastructure
    • What minimal structure needs to be in place to monitor case presentation, treatment patterns, and outcomes for 4 different event types (sudden cardiac arrest, acute coronary syndrome, stroke, heart failure)?
    • How do we balance needs for high quality data with practical (and budget) constraints on infrastructure?
    • How can current infrastructure be best incorporated with new initiatives?
  • Coordination
    • How best to oversee, organize, communicate, and otherwise coordinate data collection, validation, quality control, analysis, and data distribution?
    • How best to balance centralized versus local control and access to data?
  • Analysis
    • How best to manage the data to address various analytic issues we can expect to face?
  • Promotion
    • How can we promote the use of the data on a broad scale?
    • Howe can widespread use of data be balanced with need to maintain high analytic standards?
  • Ethics
    • What policies should be established for communicating clinical data (diagnostic) to subjects?

How does data management fit into workshop goals?

  • National, population-based data needs
  • Acute coronary syndrome, cardiac arrest, stroke, heart failure
  • Surveillance systems that are feasible and sustainable
  • Cardiovascular treatments and outcomes
  • Ascertainment of incidence and prevalence
  • Registries, quality improvement systems, research studies

Data management and flow -- can be a complex web of procedures and system, starting from data collection to ascertainment of outcomes and determination of incidence rates

Infrastructure/coordination issues

  • Data collection
    • Retrospective vs. prospective
    • Concurrent with care vs. chart-based
    • Real time vs. batch mode
    • Validation

Example of Patient ID and Data Collection System: NC Coverdell Acute Stroke Registry

NC Coverdell Acute Stroke Registry

Prehospital delay time for stroke patients as recorded from interview and medical records (Evenson E, Rosamond W, Vallee J, Morris D. Concordance of stroke symptom onset time. Ann Epidemiol 2001;11:202-207.):

  Interview Medical Record
Mean (hr) 9.8 8.9
Median (hr) 3.3 3.1
Interquartile range 1.3-9.1 1.2-9.0
Percent with onset time recorded 95% 60%

EMS Trip Sheet Can Capture (Rosamond W. et al. Calling emergency medical services for acute stroke. A study of 9-1-1 tapes. Prehospital Emergency Care 2005;9:1-5.):

  • Time arrived at scene
  • Time departed from scene
  • Time arrive at hospital
  • Response code
  • Transport code
  • Treatments provided

Example -- Validation of sudden cardiac death

  • Comparison (n) of Reynolds sudden cardiac death review and ARIC sudden death classification using 1 hours definition
 

Reynolds Sudden Cardiac Death Classification

ARIC Sudden Death Classification (1 hour definition)

Definite Sudden Death

Possible Sudden Death

Not Sudden Death

Total

Yes

129

21

32

182

No

125

38

143

306

Unknown

5

3

21

29

Total

259

62

196

517

Analysis issues

  • Sampling
    • Can increase efficiency
    • Straightforward methods exists
  • Local vs. central control
    • Quality improvement requires local access to analysis

Promoting the use of data

  • Public use datasets
  • Meta-analyses
  • Advertising
  • Symposium on how to use data
  • Local sites, real-time access to analysis
  • Fellowships and student involvement

Ethical issues: A framework (Principles and Practice of Public Health Surveillance. 2nd Edition. Teutsch and Churchil Eds. Oxford University Press, 2000 )

  • Respect for autonomy
  • Beneficence
  • Nonmaleficence
  • Justice

Ethical Framework applied to surveillance

  • Reasons for undertaking activity?
  • Benefits vs. harms vs. costs?
  • Resolution of similar ethical problems in the past?
     
    • Informing subject of test results?
      • Learn from past/current cohort studies
    • Under real time methods should you inform subjects at discharge that their care didn't meet guidelines?
      • Less experience in retrospective community surveillance
  • Community involvement?
  • Violation of rights?
  • Demonstratable virtues?

Summary

  • Attention to data management critical to insure quality data collection and analysis.
  • Reality of real-time data collection and analysis create new data management challenges.
  • Data management systems must balance practical, ethical, and data quality issues.
  • Data management systems must allow for broad access to and dissemination of analyses.

5:00 pm - 6:00 pm
-
Break

Break

6:00 pm - 9:00 pm
-
Question 2

Question 2: What are appropriate study designs (including potential for linking existing data-gathering infrastructures vs. starting de novo) to address the identified data needs?

6:00 pm - 9:00 pm
-
Evening Session (with working dinner)

Evening Session (with working dinner): Participants will divide into small working groups to address the above question from each of three perspectives:

  1. Out of hospital surveillance,
  2. Hospital surveillance, and
  3. Assessment of quality of care and outcomes.

6:00 pm
-
Small groups meet separately to prepare responses to Question 2

Small groups meet separately to prepare responses to Question 2

9:00 pm
-
Adjourn Day 1

Adjourn Day 1

8:00 am
-
Housekeeping/announcements

Housekeeping/announcements

8:15 am -11:20 am
-
Morning Session

Morning Session: The 3 small groups from Day 1 Evening Session will present their responses to Question 2, after which the full group will discuss and prepare its final recommendations.

Moderator: Dr. Luepker

8:15 am
-
Small Working Group: In hospital Surveillance - Dr. Veronique Roger, Group Leader

Gaps in knowledge

  • Nation-wide incidence data
    • Myocardial infarction (MI)
    • Stroke, including stroke subtypes
  • Track procedures: Percutaneous coronary interventions (PCI) and coronary artery by-pass grafting (CABG)
  • To track unstable angina (UA): need validation procedures
  • Heart failure (HF) and atrial fibrillation (AF): definitional and ascertainment challenges
  • Data should reflect national demographics (age, sex, ethnic/racial distributions)

Possible approaches

  • Capitalize on electronic medical records (EMR) attractive but problematic given incomplete penetration of the use of EMR, which may introduce an unknown degree of bias
  • Data sources 
     
    • JCAHO
    • National hospital discharge survey
    • NHANES
  • Need for systematic reporting of MI/stroke

Cases identification

  • MI: lab-based using biomarkers
    • Problematic given false positive rates particularly with Troponin, but reliance on MD diagnosis unsatisfactory (~adoption of criteria, under-ascertainment of post proc MI?)
  • Stroke: imaging
  • Need to develop use of other data sources 
     
    • JCAHO
    • National hospital discharge survey
    • NHANES

Validation

  • Sampling
  • Relying on uniform standardized criteria
  • As part of validation procedures, include a limited number of standardized core measures (risk factors, AF, others...)

Outcomes

  • Mortality
    • Need to rely on mortality at fixed point in time (not in-hospital mortality given temporal declines in duration of hospital stay and likely inter site variations)
  • Recurrent MI or stroke
    • Need for linkage with individual identifiers to measure true incidence
  • Other non-fatal outcomes
    • Presently not feasible as part of nation-wide system

Optimize existing systems

  • Important intermediate step while progress towards nationwide mandatory reporting
  • Adding sites to existing surveillance programs that increase ethnic diversity leading to the establishment of appropriately diverse surveillance networks
  • Use of same standardized criteria essential
  • Veterans Health Administration (VA) system attractive for enhancement of diversity of surveillance data

Other disease targets

  • HF: need in- and out-patient data
    • Challenge - standardized definition for HF
    • Option of tracking HF with low ejection fraction
    • Could be the focus of center-specific efforts (VA, Kaiser)
    • Use CMS/JCAHO
  • AF: collect while validating stroke, otherwise outpatient entity
  • UA: will need to validate, cannot rely on codes

Three-tier approach recommended:

  • Tier 1: Systematic nation-wide reporting of MI and stroke
  • Tier 2: Validation and collection of a limited number of standardized core measures
  • Tier 3: Detailed hypothesis-driven studies of these patients in specific centers

Heart disease centers, modeled from cancer registries

8:45 am
-
Small Working Group: Out-of-Hospital Surveillance - Dr. Joseph Ornato, Group Leader

What we need to track

  • Sudden cardiac death
  • Stroke
  • Acute & chronic coronary syndromes
     
    • Myocardial infarction
    • Unstable angina
    • Stable angina
  • Heart failure

Where we need to track CVD data

  • Emergency medical services (EMS)
  • Office/clinic setting
  • Emergency department
  • Observation visits
  • Deaths before entry into healthcare system

Where we want to be in 10 years

  • Coordinated, cost-effective system of CVD surveillance that has core data (i.e., counts) on national level, more detail at state & local levels
  • Provides a return on investment
  • Uniform data definitions & data transfer standards
  • Required, automatic population of data from electronic medical record (EMR) systems

Major Gaps

  • No one entity owns the problem
  • Paucity of electronic data & linkages
     
    • Out of hospital vs. in-hospital
    • Agency to agency, entity to entity
  • Paucity of CVD incidence & outcome rates
  • No uniform healthcare identifier
  • No public mandate to share the data

Optimizing Existing Data Systems

  • Look at other models as examples
  • Inventory & map existing data systems
  • Require compliance with data standards (e.g., HL7, PHIN) for federal funding
  • Need national coordinating entity
  • Need to test & validate existing data

Need new data strategies or optimize existing?

  • Combination of both
  • Need to accelerate EMR infrastructure
  • Need uniform healthcare identifier
  • Could benefit from new technologies such as health information data cards
  • Make CVD a reportable disease as records become electronic

Immediate Steps

  • Inventory & map existing data systems
  • Stakeholder meeting to develop a vision document
     
    • Similar to EMS Agenda of the Future
    • Establish who should lead the effort
    • Establish a strategy for achieving the vision
  • Continue to standardize disease and outcome definitions, incorporating standard vocabulary amenable to electronic capture
  • Link current databases from federal, state, local, and private institutions
  • Could require institutional compliance with electronic data standards before providing federal funding to grantees/contractors

Long Term Steps

  • National uniform healthcare identifier
  • National uniform definitions for reporting clinical data on CVD
  • Make CVD encounters reportable as electronic medical record systems become operational

Where we want to be in 10 years

  • Coordinated, cost-effective system of CVD surveillance that has core data (i.e., counts) on national level, more detail at state & local levels
    • Provides a return on investment
  • Uniform data definitions & data transfer standards
  • Required, automatic population of data from electronic medical record systems

9:15 am
-
Small Working Group: Quality of Care and Outcomes - Dr. David Goff, Group Leader

Out Patient Quality of Care

  • Expand/optimize National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS)
    • Increase sample size for CVD conditions
    • Validation?
    • Performance indicators (e.g., National Committee for Quality Assurance (NCQA))
    • Short-term timeline
  • Make use of Centers for Medicare & Medicaid Services (CMS) data when med data available
    • Need managed care claims as well as fee-for service claims
    • Validation?
    • Performance indicators (e.g., NCQA)
    • Limited to those 65 yrs or older
    • Long-term timeline

Out Patient Health-Related Quality of Life (HRQL)

  • Expand/optimize Medicare Current Beneficiary Survey (MCBS)
    • Measures HRQL
    • Links claims and collects med use
    • Increase sample size for CVD conditions
    • Limited to those 65 yrs or older
    • Short-term timeline
  • Alternative
    • Expand scope of work of CMS Quality Improvement Organizations (QIO)
      • Long-term testing feasibility needed
      • Limited to those 65 yrs or older

In-Patient Quality of Care & HRQL

  • Expand/optimize National Inpatient Sample (NIS) and National Hospital Discharge Survey (NHDS)
    • Validation
    • Classification (incident v. recurrent)
    • Performance indicators (e.g., JCAHO)
    • Procedure indications regarding appropriateness
  • Medium to long-term timeline
  • Don't count on sponsored databases long-term, e.g., GWTG, NRMI, GAP
  • In patient HRQL lower priority except as baseline
    • Generally expected to be poor at time of discharge
    • Transitional/out patient HRQL may be more relevant

Transitional Quality of Care & HRQL

  • Expand/optimize MCBS
    • Optimize sample size for CVD events
    • Validation?
    • Classification (incident v. recurrent)
    • Performance indicators (e.g., NCQA)
    • HRQL measured
    • Limited to those 65 yrs or older
    • Short-term timeline
  • Expand scope of work of QIO
    • Timeliness barriers
    • Limited to those 65 yrs or older
    • Long-term timeline

Long Term Care Quality of Care & HRQL

  • Expand/optimize Minimum Data Set (MDS) and National Nursing Home Survey (NNHS)
  • Optimize sample size for CVD events
  • Validation?
  • Performance indicators (e.g., NCQA)
  • HRQL measured (at least in MDS)
  • Short-term timeline

Other Issues

  • Thin national perspective
    • State-based sampling to enable state and local supplements
    • Sentinel surveillance research sites
      • Validation
      • In-depth data collection
      • Methodologic research
  • Data standards
  • Electronic medical record capacity
  • Interoperability
  • Disease management
  • Process
    • Request for Applications (RFAs) to establish sites to
      • Analyze existing surveillance data
      • Enhance/expand current systems
      • Develop new methods & systems
    • Unique patient identifier needed to link across databases
    • Need to link with-in databases to account for multiple entries per person

9:45 am
-
Break

Break

10:00 am
-
Full group to consider small group responses to Question 2 and prepare final recommendations

Full group to consider small group responses to Question 2 and prepare final
recommendations

11:00 am
-
Wrap-up/Final announcements/Next steps?

Wrap-up/Final announcements/Next steps?

11:20 am
-
Adjourn

Adjourn

11:20 am - 12:30 pm
-
Post-Adjournment Meeting

Co-chairs, Day 2 Morning Session Moderator, Small Group Leaders, and
NHLBI/ CDC representatives meet to address workshop report