Hip fracture outcomes, risk prediction, and hospital comparisons: a population-based study in Ontario Canada
Notice bibliographique
Résumé
INTRODUCTION: Hip fracture repair is one of the most common urgent procedures performed in hospitals. Having a high burden of mortality, hip fracture repair is frequently targeted for health system quality improvement and hospital performance monitoring. In the present study, we measure hospital variability and explore factors associated with 90-day mortality and the time from emergency department (ED) visit until surgery. METHODS: Patients were 50-105 years of age at the time of their hip fracture surgery between fiscal years 2015/16 and 2023/24 in Ontario Canada. Hospital variation was measured using random intercept models, risk-adjusted mortality rates, and funnel plots. Risk-adjusted mortality was computed as observed/expected (O/E) ratios multiplied by the population mortality rate. Expected mortality was estimated using logistic regression or CatBoost machine learning methods adjusted for age, sex, comorbidity, and other measures of healthcare utilization. Funnel plots were presented using crude and risk-adjusted mortality by hospital volume. Bootstrap sampling was used to compute 95 % confidence intervals. RESULTS: A total 12,607 deaths (12.1 %) occurred within 90 days of hip fracture repair (N = 103,887), 4488 (36 %) of which occurred in hospital. Hospitals only accounted for 0.6 % of the total variation in 90-day mortality. Other predictors of mortality included older age, male, higher comorbidity score, facility transfer, pre-operative anemia, home care, residence in long-term care, no prior receipt of anti-osteoarthritic medication, and no previous bone-mineral density scan (p < 0.0001 for all). Hospitals accounted for 9.2 % of the variability in the odds of receiving surgery within 48 h of ED visit. There was no clear cut-point of the time from ED arrival until surgery on the risk of 90-day mortality. There was no ecological association between hospital performance on timeliness (receipt of surgery within 48 h) and performance on 90-day mortality. CONCLUSION: There was little hospital variation in 90-day mortality. Using three different approaches, there were a few hospitals that consistently stood out as performing better/worse than expected. There was more substantial variation in the time until treatment across hospitals, but the relationship between the time until surgery and 90-day mortality was tenuous.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 ».