External Validation of a Clinical and Claims-Based Approach for Predicting 90-Day Post-Pulmonary Embolism Outcomes Among US Veterans
Notice bibliographique
Résumé
Abstract Background: Both the simplified Pulmonary Embolism Severity Index (sPESI) and the multivariable In-hospital Mortality for Pulmonary embolism using Claims daTa (IMPACT) rule classify patients' risk of early post-pulmonary embolism (PE) complications. Objective: To externally validate sPESI and IMPACT for predicting 90-day all-cause mortality and readmission rates among PE patients treated within the Veterans Health Administration (VHA). Methods: We used VHA data from 10/1/2010-9/30/2015 to identify adult patients with: (1) ≥1 inpatient diagnosis for acute PE (International Classification of Diseases-9th Revision-Clinical Modification codes=415.1x), (2) continuous medical and pharmacy enrollment for ≥12-months prior to the index PE (baseline period), (3) a minimum of 90-days of post-event follow-up or until death (whichever came first), and (4) ≥1 claim for an anticoagulant during the index PE stay. Patients were excluded if they had a claim for PE or an anticoagulant during the baseline period. We classified patients as low-risk for early post-PE complications if their sPESI score=0 or their absolute in-hospital mortality risk estimated by IMPACT was <1.5% (the latter calculated using the formula: 1/(1 + exp(-x); where x = −5.833 + [0.026*age] + [0.402*myocardial infarction] + [0.368*chronic lung disease] + [0.464*stroke] + [0.638*prior major bleeding] + [0.298*atrial fibrillation] + [1.06 1*cognitive impairment] + [0.554*heart failure] + [0.364*renal failure] + [0.484*liver disease] + [0.523*coagulopathy] + [1.068*cancer]). Sensitivity, specificity, negative and positive predictive value (NPV and PPV) for all-cause mortality, all-cause readmission, and readmission for recurrent venous thromboembolism (VTE) or major bleeding at 90-days were reported with 95% confidence intervals (CIs) for sPESI and IMPACT tools. Results: Of6,746 eligible PE patients, 851 (12.6%) died and 1,359 (20.1%) were readmitted for any reason within 90-days. Hospitalization for recurrent VTE and major bleeding occurred in 375 (5.6%) and 116 (1.7%), respectively.sPESI classified 1,918 (28.4%) as low-risk, while 1,024 (15.2%) were low-risk per IMPACT. Both tools displayed sensitivity >90% and NPVs >96% for all-cause 90-day mortality, but low specificity and PPVs (Table). IMPACT's sensitivity for all-cause readmission was numerically higher than sPESI, but both had comparable NPVs. Similar trends were observed for accuracy in predicting readmissions due to recurrent VTE or major bleeding. Conclusion: In this external validation study utilizing VHA data, IMPACT classified patients for 90-day post-PE outcomes with similar accuracy as sPESI. While not recommended for prospective clinical decision-making, IMPACT appears useful for identification of PE patients at low-risk for early mortality or readmission in retrospective claims-based studies. Table. Test characteristics for sPESI and IMPACT for 90-day post-pulmonary embolism outcomes CI= confidence interval; IMPACT=In-hospital Mortality for Pulmonary embolism using Claims data; NPV=negative predictive value; PPV=positive predictive value; sPESI=simplified Pulmonary Embolism Severity Index; VTE=venous thromboembolism Table. Test characteristics for sPESI and IMPACT for 90-day post-pulmonary embolism outcomes CI= confidence interval; IMPACT=In-hospital Mortality for Pulmonary embolism using Claims data; NPV=negative predictive value; PPV=positive predictive value; sPESI=simplified Pulmonary Embolism Severity Index; VTE=venous thromboembolism Disclosures Kumar: Johnson & Johnson: Employment. Wells:Itreas: Other: Served on a Writing Committee; Janssen Pharmaceuticals: Consultancy; Bayer Healthcare: Other: Speaker Fees and Advisory Board; BMS/Pfizer: Research Funding. Peacock:Comprehensive Research Associates LLC: Equity Ownership; Cardiorentis: Consultancy, Research Funding; The Medicine's Company: Consultancy, Research Funding; Banyan: Research Funding; Emergencies in Medicine LLC: Equity Ownership; Abbott: Research Funding; Alere: Consultancy, Research Funding; Prevencio: Consultancy; Janssen: Consultancy, Research Funding; Portola: Consultancy, Research Funding; Pfizer: Research Funding; Roche: Research Funding; ZS Pharma: Consultancy, Research Funding; Ischemia Care: Consultancy; Phillips: Consultancy. Fermann:Janssen Pharmaceuticals: Other: Advisory Board, Speakers Bureau; Pfizer: Research Funding. Wang:Janssen Pharmaceuticals: Research Funding. Baser:Janssen Pharmaceuticals: Research Funding. Schein:Johnson & Johnson: Employment, Equity Ownership, Other: Own in excess of $10,000 of J&J stock. Crivera:Johnson & Johnson: Employment, Equity Ownership, Other: Owns excess of $10,000 in stock. Coleman:Boehringer-Ingelheim Pharmaceuticals, inc.: Consultancy, Research Funding; Bayer Pharmaceuticals AG: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy, Research Funding.
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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 | 0,041 | 0,084 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».