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Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction.

ABSTRACT: Background This study evaluated the role of supplementing Society of Thoracic Surgeons (STS) risk models for surgical aortic valve replacement with machine learning (ML). Methods and Results Adults undergoing isolated surgical aortic valve replacement in the STS National Database between 2007 and 2017 were included. ML models for operative mortality and major morbidity were previously developed using extreme gradient boosting. Concordance and discordance in predicted risk between ML and STS models were defined using equal-size tertile-based thresholds of risk. Calibration metrics and discriminatory capability were compared between concordant and discordant patients. A total of 243 142 patients were included. Nearly all calibration metrics were improved in cases of concordance. Similarly, concordance indices improved substantially in cases of concordance for all models with the exception of deep sternal wound infection. The greatest improvements in concordant versus discordant cases were in renal failure: ML model (concordance index, 0.660 [95% CI, 0.632-0.687] discordant versus 0.808 [95% CI, 0.794-0.822] concordant) and STS model (concordance index, 0.573 [95% CI, 0.549-0.576] discordant versus 0.797 [95% CI, 0.782-0.811] concordant) (each P<0.001). Excluding deep sternal wound infection, the concordance indices ranged from 0.549 to 0.660 for discordant cases and 0.674 to 0.808 for concordant cases. Conclusions Supplementing ML models with existing STS models for surgical aortic valve replacement may have an important role in risk prediction and should be explored further. In particular, for the roughly 25% to 50% of patients demonstrating discordance in estimated risk between ML and STS, there appears to be a substantial decline in predictive performance suggesting vulnerability of the existing models in these patient subsets.

SUBMITTER: Kilic A

PROVIDER: S-EPMC8751954 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceThe Centers for Medicare & Medicaid Services national coverage determination for transcatheter aortic valve replacement (TAVR) includes volume requirements for surgical aortic valve replacement (SAVR) for hospitals seeking to initiate or continue TAVR programs. Evidence regarding the association between SAVR volume and TAVR outcomes is limited.ObjectiveTo examine the association of hospital SAVR and combined SAVR and TAVR volumes with patient outcomes of TAVR procedures performed within 1 year, 2 years, and for the entire period after initiation of TAVR programs.Design, setting, and participantsThis observational cohort study included 60 538 TAVR procedures performed in 438 hospitals between October 1, 2011, and December 31, 2015, among Medicare beneficiaries.Main outcomes and measuresThe associations between SAVR volume, SAVR and TAVR volumes, and risks of death, death or stroke, and readmissions within 30 days were determined using a hierarchical logistic regression model adjusting for patient and hospital characteristics. The association between SAVR and SAVR and TAVR volumes and 1-year and 2-year mortality after TAVR procedures was determined using a multivariable proportional hazard model with a robust variance estimator. The associations for procedures performed within 1 year, 2 years, and for the entire period after initiation of TAVR programs were examined.ResultsAmong the 60 538 patients, 29 173 were women and 31 365 were men, with a mean (SD) age of 82.3 (8.0) years. Hospitals with high SAVR volume (mean annual volume, ≥97 per year) were more likely to adopt TAVR early and had a higher growth in TAVR volumes over time (median TAVR volume by hospitals with high SAVR volume and low SAVR volume: year 1, 32 vs 19; year 2, 48 vs 28; year 3, 82 vs 38; year 4, 118 vs 54; P < .001). In adjusted analysis, high hospital SAVR volume alone was not associated with better patient outcomes after TAVR. When hospital TAVR and SAVR volumes were jointly analyzed, patients treated in hospitals with high TAVR volume had lower 30-day mortality after TAVR (high TAVR and low SAVR vs low TAVR and low SAVR: odds ratio, 0.85; 95% CI, 0.72-0.99; high TAVR and high SAVR vs low TAVR and high SAVR: odds ratio, 0.81; 95% CI, 0.69-0.95), the effect of which was more pronounced when hospitals also had high SAVR volume. Patients treated in hospitals with high SAVR volume and high TAVR volume had the lowest 30-day mortality (vs hospitals with low SAVR volume and TAVR volume: odds ratio, 0.77; 95% CI, 0.66-0.89).Conclusions and relevanceHospitals with high SAVR volume are most likely to be fast adopters of TAVR. Hospital SAVR volume alone is not associated with better TAVR outcomes. Accumulating high volumes of TAVR is associated with lower mortality after TAVR, particularly when hospitals have high SAVR volumes. Hospitals with high caseloads of both SAVR and TAVR are likely to achieve the best outcomes.

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Supplementing Existing Societal Risk Models for Surgical Aortic Valve Replacement With Machine Learning for Improved Prediction.

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