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In The Annals of thoracic surgery ; h5-index 58.0

BACKGROUND : This study evaluated the predictive utility of a machine learning algorithm in estimating operative mortality risk in cardiac surgery.

METHODS : Index adult cardiac operations performed between 2011-2017 at a single institution were included. The primary outcome was operative mortality. Extreme gradient boosting (XGBoost) models were developed and evaluated using 10-fold cross validation with 1000-replication bootstrapping. Model performance was assessed using multiple measures including precision, recall, calibration plots, area under receiver operating characteristic curve (c-index), accuracy, and F1 score.

RESULTS : A total of 11,190 patients were included (7,048 isolated coronary artery bypass grafting [CABG], 2,507 isolated valves, and 1,635 CABG plus valves). The Society of Thoracic Surgeons predicted risk of mortality (STS-PROM) was 3.2% ± 5.0%. Actual operative mortality was 2.8%. There was moderate correlation (r=0.652) in predicted risk between XGBoost versus STS-PROM for the overall cohort and weak correlation (r=0.473) in predicted risk between the models specifically in patients with operative mortality. XGBoost demonstrated improvements in all measures of model performance when compared to STS-PROM in the validation cohorts: mean average precision (0.221 XGBoost versus 0.180 STS-PROM), c-index (0.808 XGBoost versus 0.795 STS-PROM), calibration (mean observed:expected mortality: XGBoost 0.993 versus 0.956 STS-PROM), accuracy (1-3% improvement across discriminatory thresholds of 3-10% risk), and F1 score (0.281 XGBoost versus 0.230 STS-PROM).

CONCLUSIONS : Machine learning algorithms such as XGBoost have promise in predictive analytics in cardiac surgery. The modest improvements in model performance demonstrated in the current study warrant further validation in larger cohorts of patients.

Kilic Arman, Goyal Anshul, Miller James K, Gjekmarkaj Eva, Tam Weng Lam, Gleason Thomas G, Sultan Ibrahim, Dubrawksi Artur

2019-Nov-07

aortic valve replacement, artificial intelligence, coronary artery bypass grafts (CABG), database, mitral valve, outcomes