Accuracy of Medical Claims for Identifying Cardiovascular and Bleeding Events After Myocardial Infarction
Bibliographic record
Abstract
Importance: Pragmatic clinical trial designs have proposed the use of medical claims data to ascertain clinical events; however, the accuracy of billed diagnoses in identifying potential events is unclear. Objectives: To compare the 1-year cumulative incidences of events when events were identified by medical claims vs by physician adjudication and to assess the accuracy of bill-identified events using physician adjudication as the criterion standard. Design, Setting, and Participants: This post hoc analysis of a clinical trial assessed the medical claims forms and records for all rehospitalizations at 233 US hospitals within 1 year of the index acute myocardial infarction (MI) of 12 365 patients enrolled in the Treatment With Adenosine Diphosphate Receptor Inhibitors: Longitudinal Assessment of Treatment Patterns and Events After Acute Coronary Syndrome (TRANSLATE-ACS) study between April 1, 2010, and October 31, 2012. Fourteen patients (0.1%) died during the index hospitalization and were excluded from analysis. Recurrent MI, stroke, and bleeding events were identified per the International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis and procedural codes in medical bills. These events were independently adjudicated by study physicians through medical record reviews using the prespecified criteria of recurrent MI and stroke and the bleeding definition by the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries (GUSTO) scale. Medical claims were reported on a Uniform Bill-04 claims form; claims were collected from all hospitals visited by patients enrolled in TRANSLATE-ACS. Agreement between medical claims-identified events and physician-adjudicated events over the 12 months after discharge was assessed with the κ statistic. Data were analyzed from January 30, 2015, to March 2, 2017. Main Outcomes and Measures: Event rates within 1 year after MI. Results: Among 12 365 patients with acute MI, 8890 (71.9%) were men and mean (SD) age was 60 (11.6) years. The cumulative 1-year incidence of events identified by medical claims was 4.3% for MI, 0.9% for stroke, and 5.0% for bleeding. Incidence rates based on physician adjudication were 4.7% for MI, 0.9% for stroke, and 5.4% for bleeding. Agreement between medical claims-identified and physician-adjudicated events was modest, with a κ of 0.76 (95% CI, 0.73 to 0.79) for MI and 0.55 (95% CI, 0.41 to 0.68) for stroke events. In contrast, agreement between medical claims-identified and physician-adjudicated bleeding events was poor, with a κ of 0.24 (95% CI, 0.19 to 0.30) for any hospitalized bleeding event and 0.15 (95% CI, 0.11 to 0.20) for moderate or severe bleeding on the GUSTO scale. Conclusions and Relevance: Event rates at 1 year after MI were lower for MI, stroke, and bleeding when medical claims were used to identify events than when adjudicated by physicians. Medical claims diagnoses were only modestly accurate in identifying MI and stroke admissions but had limited accuracy for bleeding events. An alternative approach may be needed to ensure good safety surveillance in cardiovascular studies. Trial Registration: clinicaltrials.gov Identifier: NCT01088503.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".