Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis

Cheng-Hong Yang; Yin-Syuan Chen; Sin-Hua Moi; Jin-Bor Chen; Lin Wang; Li-Yeh Chuang

doi:10.1177/20406223221119617

Machine learning approaches for the mortality risk assessment of patients undergoing hemodialysis

Ther Adv Chronic Dis. 2022 Aug 30:13:20406223221119617. doi: 10.1177/20406223221119617. eCollection 2022.

Authors

Cheng-Hong Yang^{1

2

3

4

5}, Yin-Syuan Chen², Sin-Hua Moi⁶, Jin-Bor Chen⁷, Lin Wang⁸, Li-Yeh Chuang⁹

Affiliations

¹ Department of Information Management, Tainan University of Technology, Tainan.
² Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung.
³ Biomedical Engineering, Kaohsiung Medical University, Kaohsiung.
⁴ School of Dentistry, Kaohsiung Medical University, Kaohsiung.
⁵ Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung.
⁶ Center of Cancer Program Development, E-Da Cancer Hospital, I-Shou University, Kaohsiung 82445.
⁷ Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301.
⁸ Department of Nephrology, Dalian University Affiliated Xinhua Hospital, Dalian, 116001, China.
⁹ Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung 84004.

Abstract

Introduction: Mortality is a major primary endpoint for long-term hemodialysis (HD) patients. The clinical status of HD patients generally relies on longitudinal clinical observations such as monthly laboratory examinations and physical examinations.

Methods: A total of 829 HD patients who met the inclusion criteria were analyzed. All patients were tracked from January 2009 to December 2013. Taken together, this study performed full-adjusted-Cox proportional hazards (CoxPH), stepwise-CoxPH, random survival forest (RSF)-CoxPH, and whale optimization algorithm (WOA)-CoxPH model for the all-cause mortality risk assessment in HD patients. The model performance between proposed selections of CoxPH models were evaluated using concordance index.

Results: The WOA-CoxPH model obtained the highest concordance index compared with RSF-CoxPH and typical selection CoxPH model. The eight significant parameters obtained from the WOA-CoxPH model, including age, diabetes mellitus (DM), hemoglobin (Hb), albumin, creatinine (Cr), potassium (K), Kt/V, and cardiothoracic ratio, have also showed significant survival difference between low- and high-risk characteristics in single-factor analysis. By integrating the risk characteristics of each single factor, patients who obtained seven or more risk characteristics of eight selected parameters were dichotomized as high-risk subgroup, and remaining is considered as low-risk subgroup. The integrated low- and high-risk subgroup showed greater discrepancy compared with each single risk factor selected by WOA-CoxPH model.

Conclusion: The study findings revealed WOA-CoxPH model could provide better risk assessment performance compared with RSF-CoxPH and typical selection CoxPH model in the HD patients. In summary, patients who had seven or more risk characteristics of eight selected parameters were at potentially increased risk of all-cause mortality in HD population.

Keywords: feature selection; hemodialysis; machine learning; risk assessment; survival analysis.