Machine learning in the prediction of cardiac surgery associated acute kidney injury with early postoperative biomarkers - Fan, Rui; Qin, Wei; Zhang, Hao; Guan, Lichun; Wang, Wuwei; Li, Jian; Chen, Wen; Huang, Fuhua; Zhang, Hang; Chen, Xin (Frontiers in Surgery, 2023)
Purpose: To establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers. Patients and methods: This study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n=452, from November 2018 to June 2019) and a validation cohort (n=326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria. Results: Five biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third versus the first tertile: sST2, 3.55 [2.34-5.49], NT-proBNP, 5.50 [3.54-8.71], H-FABP, 6.64 [4.11-11.06], LDH, 7.47 [4.54-12.64], and UA, 8.93 [5.46-15.06]). Conclusion: Our study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.