Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis

Abstract

Tzu-Hao Wang,1,2 Chih-Chin Kao,3– 5,* Tzu-Hao Chang2,6,* 1Division of General Medicine, Department of Medical Education, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan, Republic of China; 2Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, Republic of China; 3Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, Republic of China; 4Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, Republic of China; 5Taipei Medical University-Research Center of Urology and Kidney (TMU-RCUK), Taipei Medical University, Taipei, Taiwan, Republic of China; 6Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei City, Taiwan, Republic of China*These authors contributed equally to this workCorrespondence: Tzu-Hao Chang, Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, International Center for Health Information Technology, Taipei Medical University, 9F, Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Road, Zhonghe District, New Taipei City, 235603, Taiwan, Republic of China, Tel [phone] ext.10922, Email [email protected]: Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes.Patients and Methods: We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. Ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform.Results: The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our Ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor.Conclusion: Ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical decision-making and enhance patient care for the high-risk population.Keywords: AKI-D, dialysis prognosis, ensemble machine learning, prediction models, risk factors

Keywords

• aki-d
• dialysis prognosis
• ensemble machine learning
• prediction models
• risk factors