Key result
Dynamic updating strategies identify ~7 outlier hospitals for post-SAVR mortality missed by static models.
Why the study?
Prediction models for hospital performance are infrequently updated, which may reduce accuracy and misclassify performance over time, whereas dynamic models may improve accuracy and fairness.
Do dynamic updating strategies alter hospital rankings and outlier detection compared with a static model among surgical aortic valve replacement patients?
Cohort (n=43,197)
Yes
Do dynamic updating strategies alter hospital rankings and outlier detection compared with a static model among surgical aortic valve replacement patients?
Dynamic updating of prediction models for surgical aortic valve replacement outcomes significantly alters hospital rankings and outlier detection compared to static models, enabling fairer performance comparisons.
Dynamic updating may identify more SAVR mortality outliers than static models; leaves open whether it improves fair hospital profiling.
BACKGROUND: Prediction models determining expected outcomes are infrequently updated (ie, static), which may reduce accuracy and misclassify hospital performance over time. Dynamic models incorporate changes over time and may improve accuracy and fairness in hospital comparisons. This study evaluated whether dynamic updating, compared with a static model, altered hospital rankings and outlier detection among surgical aortic valve replacement patients. METHODS: This retrospective cohort study assessed performance across 53 hospitals using claims data from the Pennsylvania Health Care Cost Containment Council. A multivariable logistic regression model using clinical and demographic variables was developed on data from 1999 to 2006 to predict 30-day postoperative mortality, then applied to testing data from 2007 to 2018 to compare 4 strategies: (1) a static model with fixed parameters, (2) an annual correction factor based on The Society of Thoracic Surgeons methodology, (3) calibration regression for annual recalibration, and (4) dynamic logistic state space model to continuously update model coefficients. Performance was evaluated using observed-to-expected ratios and Z scores. Lower values indicate better-than-expected outcomes. RESULTS: The training sample included 14 070 patients (mean age 66.6; 43.1% women); the testing sample included 29 127 patients (mean age 67.4; 39.1% women). The static model had the widest Z score variability (range −6.97 to 1.38), compared with calibration regression (−3.04 to 2.85), correction factor (−2.87 to 3.24), and dynamic logistic state space model (−2.57 to 3.03). The static model labeled 15 hospitals as significantly better-than-expected; only 3 (20.0%) maintained this classification with the correction factor and dynamic logistic state space model, and 5 (33.3%) with calibration regression. No hospitals were classified as significantly worse-than-expected under the static model, whereas calibration regression identified 6, and both dynamic logistic state space model and the correction factor identified 7. CONCLUSIONS: Static models may misclassify hospital performance and rankings. Dynamic strategies influence outlier detection and change hospital rankings over time. Regular model updates may better reflect current performance, supporting fairer hospital comparisons.
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Pollack et al. (2025) conducted a cohort in Surgical aortic valve replacement (n=43,197). Dynamic updating strategies (correction factor, calibration regression, dynamic logistic state space model) vs. Static model with fixed parameters was evaluated on Hospital performance evaluation using observed-to-expected ratios and Z scores for 30-day postoperative mortality. Dynamic updating strategies for predicting 30-day mortality after surgical aortic valve replacement identified 6 to 7 worse-than-expected outlier hospitals, whereas a static model identified none.
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