Predicting Mortality Using Machine Learning Algorithms in Patients Who Require Renal Replacement Therapy in the Critical Care Unit

Hsin Hsiung Chang, Jung Hsien Chiang, Chi Shiang Wang, Ping Fang Chiu, Khaled Abdel-Kader, Huiwen Chen, Edward D. Siew, Jonathan Yabes, Raghavan Murugan, Gilles Clermont, Paul M. Palevsky, Manisha Jhamb

研究成果: Article同行評審

6 引文 斯高帕斯(Scopus)

摘要

Background: General severity of illness scores are not well calibrated to predict mortality among patients receiving renal replacement therapy (RRT) for acute kidney injury (AKI). We developed machine learning models to make mortality prediction and compared their performance to that of the Sequential Organ Failure Assessment (SOFA) and HEpatic failure, LactatE, NorepInephrine, medical Condition, and Creatinine (HELENICC) scores. Methods: We extracted routinely collected clinical data for AKI patients requiring RRT in the MIMIC and eICU databases. The development models were trained in 80% of the pooled dataset and tested in the rest of the pooled dataset. We compared the area under the receiver operating characteristic curves (AUCs) of four machine learning models (multilayer perceptron [MLP], logistic regression, XGBoost, and random forest [RF]) to that of the SOFA, nonrenal SOFA, and HELENICC scores and assessed calibration, sensitivity, specificity, positive (PPV) and negative (NPV) predicted values, and accuracy. Results: The mortality AUC of machine learning models was highest for XGBoost (0.823; 95% confidence interval [CI], 0.791–0.854) in the testing dataset, and it had the highest accuracy (0.758). The XGBoost model showed no evidence of lack of fit with the Hosmer–Lemeshow test (p > 0.05). Conclusion: XGBoost provided the highest performance of mortality prediction for patients with AKI requiring RRT compared with previous scoring systems.

原文English
文章編號5289
期刊Journal of Clinical Medicine
11
發行號18
DOIs
出版狀態Published - 2022 9月

All Science Journal Classification (ASJC) codes

  • 一般醫學

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