A novel machine learning algorithm, Bayesian networks model, to predict the high‐risk patients with cardiac surgery‐associated acute kidney injury

BACKGROUND: Cardiac surgery‐associated acute kidney injury (CSA‐AKI) is a well‐recognized complication with an ominous outcome. HYPOTHESIS: Bayesian networks (BNs) not only can reveal the complex interrelationships between predictors and CSA‐AKI, but predict the individual risk of CSA‐AKI occurrence. METHODS: During 2013 and 2015, we recruited 5533 eligible participants who underwent cardiac surgery from a tertiary hospital in eastern China. Data on demographics, clinical and laboratory information were prospectively recorded in the electronic medical system and analyzed by gLASSO‐logistic regression and BNs. RESULTS: The incidences of CSA‐AKI and severe CSA‐AKI were 37.5% and 11.1%. BNs model revealed that gender, left ventricular ejection fractions (LVEF), serum creatinine (SCr), serum uric acid (SUA), platelet, and aortic cross‐clamp time (ACCT) were found as the parent nodes of CSA‐AKI, while ultrafiltration volume and postoperative central venous pressure (CVP) were connected with CSA‐AKI as children nodes. In the severe CSA‐AKI model, age, proteinuria, and SUA were directly linked to severe AKI; the new nodes of NYHA grade and direct bilirubin created relationships with severe AKI through was related to LVEF, surgery types, and SCr level. The internal AUCs for predicting CSA‐AKI and severe AKI were 0.755 and 0.845, which remained 0.736 and 0.816 in the external validation. Given the known variables, the risk for CSA‐AKI can be inferred at individual levels based on the established BNs model and prior information. CONCLUSION: BNs model has a high accuracy, good interpretability, and strong generalizability in predicting CSA‐AKI. It facilitates physicians to identify high‐risk patients and implement protective strategies to improve the prognosis.