ArticleCirculation. Cardiovascular quality and outcomes2025
Dynamic Updating Strategies to Assess Hospital Performance of Surgical Aortic Valve Replacement.
Article in Circulation. Cardiovascular quality and outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Construction of performance score dynamic prediction system for clinical departments using explainable machine learning.Health information science and systems · 2026Article
- Dynamic evaluation system for improving hospital outsourcing service performance: a G1-Critic and LSTM+Dropout approach.BMC health services research · 2026Article
Corrections and comments
- Commented on by
Authors and funding
5 authors.
Funding
Abstract
backgroundPrediction 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.
methodsThis 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
resultsThe 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
conclusionsStatic 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.