Accuracy of Medical Claims for Identifying Cardiovascular and Bleeding Events After Myocardial Infarction
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,192 | 0,424 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».