Machine learning in the prediction of cardiac surgery associated acute kidney injury with early postoperative biomarkers

Rui Fan; Wei Qin; Hao Zhang; Lichun Guan; Wuwei Wang; Jian Li; Wen Chen; Fuhua Huang; Hang Zhang; Xin Chen

doi:10.3389/fsurg.2023.1048431

Machine learning in the prediction of cardiac surgery associated acute kidney injury with early postoperative biomarkers

Front Surg. 2023 Feb 7:10:1048431. doi: 10.3389/fsurg.2023.1048431. eCollection 2023.

Authors

Rui Fan^{1

2}, Wei Qin², Hao Zhang³, Lichun Guan⁴, Wuwei Wang², Jian Li², Wen Chen², Fuhua Huang², Hang Zhang⁴, Xin Chen^{1

2}

Affiliations

¹ School of Medicine, Southeast University, Nanjing, China.
² Department of Thoracic and Cardiovascular Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
³ Department of Nephrology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
⁴ Department of Thoracic Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Abstract

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 vs. 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.

Keywords: acute kidney injury; biomarker; cardiac surgery; machine learning; nomogram; random forest.

Grants and funding

This work was supported by the National Natural Science Foundation of China (Number, 8217021245) and the Science and Technology Plan of Changzhou (Number, CE20205047).