Abstract 170: Effect of Hospital Case Volume, Number of Hospitals, and the Predictive Accuracy Of Risk-adjustment Models (c-statistic) on the Accuracy of Hospital Report Cards
Notice bibliographique
Résumé
Background: The generation of report cards to compare hospital outcomes is increasingly common. Many researchers use the c-statistic of the risk-adjusted logistic regression model as a measure of the accuracy of the report cards, while often ignoring factors such as hospital case volumes and the number of hospitals. Through the use of Monte-Carlo simulations, we assessed the relative importance of these 3 variables (hospital case volume, number of hospitals,and model c-statistic) on the accuracy of hospital report cards. Methods: Using a previously developed 30-day mortality prediction model, we used data from 31,183 patients hospitalized with AMI in 159 Ontario hospitals between 2008 and 2010 to construct a data-generating process that allowed us to generate simulated datasets in which the actual hospital rankings (based on 30-day risk-adjusted mortality) were known with certainty. The simulated datasets had the same variability in case mix as was observed in the 159 hospitals (intra-class correlation ICC= 0.037). Using Monte Carlo simulations we varied the 3 variables across plausible ranges i.e., number of patients per hospital (50, 100, 200), number of hospitals (50, 100, 200), c-statistic (25 values ranging from 0.53 - 0.96), producing 225 different scenarios (i.e., 3 x 3 x 25) and created 500 simulated datasets for each scenario. The observed rank order of hospitals in each simulation was determined by generating observed vs. expected (O-E) ratios using indirect standardization as well as predicted vs. expected (P-E) ratios using hierarchical regression models. We determined the influence of the 3 factors on the accuracy of the report cards by calculating the Spearman-rank correlation (r s ) between the known and observed (O-E and P-E) hospital rank orders. Results: Of the 3 factors examined, only the number of patients per hospital had a meaningful influence on the correlation between known and observed rank order. To illustrate, in one typical simulation, as the case volume increased from 50, to 100, and then to 200 patients, the correlation increased from 0.37, 0.50, and 0.62, respectively. In contrast, the model c-statistic had a very modest impact on the accuracy of hospital report cards; across the full range of the c-statistic (0.53-0.96) correlations increased by <5% in all scenarios. The number of hospitals included in the simulations had no meaningful effect (change in r s ≤1%). The fact that none of the simulations produced correlations >0.70 and many were <0.50, serves to highlight the substantial amount of error that can occur when attempting to profile hospitals, even in the favorable environment of simulated datasets. Conclusions: The c-statistic of a risk-adjustment model should not be used to assess the accuracy of hospital report cards, rather, more attention should be paid to hospital case volumes which directly impact the accuracy of hospital rankings.
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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 | 0,074 | 0,284 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».