Key result
Xgboost machine learning outperforms logistic regression for predicting AKI after cardiac surgery.
Why the study?
The study was conducted to derive and validate a machine learning prediction model of acute kidney injury for surveillance and management to improve clinical outcomes.
Does an Xgboost machine learning model improve the prediction of acute kidney injury after cardiac surgery compared to logistic regression?
Cohort (n=15,880)
No
Does an Xgboost machine learning model improve the prediction of acute kidney injury after cardiac surgery compared to logistic regression?
Effect estimate: AUROC 0.849 (95% CI 0.837-0.861)
Absolute Event Rate: 0.849% vs 0.803%
p-value: p=<0.001
An Xgboost machine learning model outperformed logistic regression in predicting acute kidney injury after cardiac surgery, with eGFR and creatinine on ICU arrival being the most important predictors.
XGBoost may enhance post-cardiac surgery AKI prediction over logistic regression in this cohort; leaves open need for prospective validation before clinical adoption.
Background: To derive and validate a machine learning (ML) prediction model of acute kidney injury (AKI) that could be used for AKI surveillance and management to improve clinical outcomes. Methods: This retrospective cohort study was conducted in Fuwai Hospital, including patients aged 18 years and above undergoing cardiac surgery admitted between January 1, 2017, and December 31, 2018. Seventy percent of the observations were randomly selected for training and the remaining 30% for testing. The demographics, comorbidities, laboratory examination parameters, and operation details were used to construct a prediction model for AKI by logistic regression and eXtreme gradient boosting (Xgboost). The discrimination of each model was assessed on the test cohort by the area under the receiver operator characteristic (AUROC) curve, while calibration was performed by the calibration plot. Results: A total of 15,880 patients were enrolled in this study, and 4845 (30.5%) had developed AKI. Xgboost model had the higher discriminative ability compared with logistic regression (AUROC, 0.849 [95% CI, 0.837– 0.861] vs 0.803[95% CI 0.790– 0.817], P < 0.001) in the test dataset. The estimated glomerular filtration (eGFR) and creatine on intensive care unit (ICU) arrival are the two most important prediction parameters. A SHAP summary plot was used to illustrate the effects of the top 15 features attributed to the Xgboost model. Conclusion: ML models can provide clinical decision support to determine which patients should focus on perioperative preventive treatment to preemptively reduce acute kidney injury by predicting which patients are not at risk.
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Gao et al. (2023) conducted a cohort in Acute kidney injury after cardiac surgery (n=15,880). eXtreme gradient boosting (Xgboost) model vs. Logistic regression model was evaluated on Discriminative ability (AUROC) for acute kidney injury (AUROC 0.849, 95% CI 0.837-0.861, p=<0.001). An Xgboost machine learning model demonstrated higher discriminative ability for predicting acute kidney injury after cardiac surgery compared to logistic regression (AUROC 0.849 vs 0.803; P<0.001).
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