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Record W2619446037 · doi:10.1001/jamacardio.2017.1460

Accuracy of Medical Claims for Identifying Cardiovascular and Bleeding Events After Myocardial Infarction

2017· article· en· W2619446037 on OpenAlexfundno aff
Patrícia O. Guimarães, Arun Krishnamoorthy, Lisa A. Kaltenbach, Kevin J. Anstrom, Mark B. Effron, Daniel B. Mark, Patrick L. McCollam, Linda Davidson‐Ray, Eric D. Peterson, Tracy Y. Wang

Bibliographic record

VenueJAMA Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsnot available
FundersDuke Clinical Research InstituteNational Institutes of HealthGenentechValeant Pharmaceuticals InternationalMinistério da Ciência, Tecnologia e InovaçãoHeartWareRegeneron PharmaceuticalsBoston Scientific CorporationGilead SciencesGlaxoSmithKlineNational Heart, Lung, and Blood InstituteThoratec CorporationPfizerEli Lilly and CompanyBristol-Myers SquibbAstraZenecaDaiichi Sankyo EuropeSanofiAmerican Heart Association
KeywordsMedicineMyocardial infarctionCardiologyInternal medicineMEDLINEIntensive care medicine

Abstract

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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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.192
metaresearch head score (Gemma)0.424
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.997

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1920.424
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0050.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.377
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations61
Published2017
Admission routes1
Has abstractyes

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