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Study of Cardiovascular Health Outcomes in the Era of Claims Data: The Cardiovascular Health Study
Bruce M Psaty1, Joseph A Delaney2, Alice M Arnold2
1From Cardiovascular Health Research Unit, Department of Medicine (B.M.P.), Department of Epidemiology (B.M.P., J.A.D., S.R.H.), Department of Health Services (B.M.P.), Department of Biostatistics (A.M.A., B.M.), Department of Global Health (A.L.F.), Department of Family Medicine (A.L.F.), and Department of Neurology (W.T.L.), University of Washington, Seattle; Group Health Research Institute, Group Health Cooperative, Seattle, WA (B.M.P., S.R.H.); Department of Medicine, Duke University, Durham, NC (L.H.C.); Department of Epidemiology, University of Pittsburgh, PA (D.I., L.H.K.); and Department of Medicine, University of Maryland, Baltimore (J.S.G.). psaty@u.washington.edu.
Insights
Using diagnostic codes from administrative claims data to define clinical outcomes, particularly when limited to primary diagnoses, underestimates event rates. This method also includes non-event hospitalizations, creating a composite endpoint.
Area of Science:
- Cardiovascular epidemiology
- Health services research
- Biostatistics
Background:
- Administrative claims data are increasingly utilized for clinical outcome assessment.
- Diagnostic codes from claims data present a convenient but potentially inaccurate method for identifying health events.
- Validating claims-based outcomes against adjudicated events is crucial for reliable research.
Purpose of the Study:
- To compare the accuracy of claims-based diagnostic codes versus adjudicated events for defining cardiovascular outcomes.
- To assess event rates and risk factor associations using different methods of claims data analysis.
- To evaluate the impact of code position (primary vs. any) on outcome ascertainment.
Main Methods:
- Utilized data from the Cardiovascular Health Study (CHS).
- Defined myocardial infarction (MI), stroke, and heart failure using three methods: CHS adjudicated events (CHS[adj]), International Classification of Diseases, Ninth Edition codes in primary position (CMS[1st]), and codes in any position (CMS[any]).
- Compared event rates and cardiovascular disease risk factor associations across the three definitions.
Main Results:
- Claims-based methods demonstrated high positive predictive values but low sensitivities for identifying events.
- The CMS[1st] method significantly underestimated event rates compared to CHS[adj] (e.g., MI incidence: 8.6 vs. 14.9 per 1000 person-years).
- Cardiovascular disease risk factor associations were generally consistent across the three event definition methods.
Conclusions:
- Restricting diagnostic codes to primary positions in claims data leads to underestimation of cardiovascular event rates.
- Claims-based event data can function as a composite endpoint, including both true events and misclassified non-event hospitalizations.
- Careful consideration of methodology is required when using claims data for clinical outcome research.
Background:
Increasingly, the diagnostic codes from administrative claims data are being used as clinical outcomes.
Methods And Results:
Data from the Cardiovascular Health Study (CHS) were used to compare event rates and risk factor associations between adjudicated hospitalized cardiovascular events and claims-based methods of defining events. The outcomes of myocardial infarction (MI), stroke, and heart failure were defined in 3 ways: the CHS adjudicated event (CHS[adj]), selected International Classification of Diseases, Ninth Edition diagnostic codes only in the primary position for Medicare claims data from the Center for Medicare & Medicaid Services (CMS[1st]), and the same selected diagnostic codes in any position (CMS[any]). Conventional claims-based methods of defining events had high positive predictive values but low sensitivities. For instance, the positive predictive value of International Classification of Diseases, Ninth Edition code 410.x1 for a new acute MI in the first position was 90.6%, but this code identified only 53.8% of incident MIs. The observed event rates for CMS[1st] were low. For MI, the incidence was 14.9 events per 1000 person-years for CHS[adj] MI, 8.6 for CMS[1st] MI, and 12.2 for CMS[any] MI. In general, cardiovascular disease risk factor associations were similar across the 3 methods of defining events. Indeed, traditional cardiovascular disease risk factors were also associated with all first hospitalizations not resulting from an MI.
Conclusions:
The use of diagnostic codes from claims data as clinical events, especially when restricted to primary diagnoses, leads to an underestimation of event rates. Additionally, claims-based events data represent a composite end point that includes the outcome of interest and selected (misclassified) nonevent hospitalizations.
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