Comparison of 30-Day Mortality Models for Profiling Hospital Performance in Acute Ischemic Stroke With vs Without Adjustment for Stroke Severity
Notice bibliographique
Résumé
CONTEXT: There is increasing interest in reporting risk-standardized outcomes for Medicare beneficiaries hospitalized with acute ischemic stroke, but whether it is necessary to include adjustment for initial stroke severity has not been well studied. OBJECTIVE: To evaluate the degree to which hospital outcome ratings and potential eligibility for financial incentives are altered after including initial stroke severity in a claims-based risk model for hospital 30-day mortality for acute ischemic stroke. DESIGN, SETTING, AND PATIENTS: Data were analyzed from 782 Get With The Guidelines-Stroke participating hospitals on 127,950 fee-for-service Medicare beneficiaries with ischemic stroke who had a score documented for the National Institutes of Health Stroke Scale (NIHSS, a 15-item neurological examination scale with scores from 0 to 42, with higher scores indicating more severe stroke) between April 2003 and December 2009. Performance of claims-based hospital mortality risk models with and without inclusion of NIHSS scores for 30-day mortality was evaluated and hospital rankings from both models were compared. MAIN OUTCOMES MEASURES: Model discrimination, hospital 30-day mortality outcome rankings, and value-based purchasing financial incentive categories. RESULTS: Across the study population, the mean (SD) NIHSS score was 8.23 (8.11) (median, 5; interquartile range, 2-12). There were 18,186 deaths (14.5%) within the first 30 days, including 7430 deaths (5.8%) during the index hospitalization. The hospital mortality model with NIHSS scores had significantly better discrimination than the model without (C statistic, 0.864; 95% CI, 0.861-0.867, vs 0.772; 95% CI, 0.769-0.776; P < .001). Among hospitals ranked in the top 20% or bottom 20% of performers by the claims model without NIHSS scores, 26.3% were ranked differently by the model with NIHSS scores. Of hospitals initially classified as having "worse than expected" mortality, 57.7% were reclassified to "as expected" by the model with NIHSS scores. The net reclassification improvement (93.1%; 95% CI, 91.6%-94.6%; P < .001) and integrated discrimination improvement (15.0%; 95% CI, 14.6%-15.3%; P < .001) indexes both demonstrated significant enhancement of model performance after the addition of NIHSS. Explained variance and model calibration was also improved with the addition of NIHSS scores. CONCLUSION: Adding stroke severity as measured by the NIHSS to a hospital 30-day risk model based on claims data for Medicare beneficiaries with acute ischemic stroke was associated with considerably improved model discrimination and change in mortality performance rankings for a substantial portion of hospitals.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; un appel candidat d’une seule tête enseignante, 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 ».