A new survival prediction model and exploration of hemodialysis quality control indicators in incident hemodialysis patients

Huaiwen Chang, Xuehui Sun, Jing Qian, Li Ni, Ping Cheng, Jun Shi, Chuhan Lu, Xiaofeng Wang, Mengjing Wang and Jing Chen

PLOS ONE, 2026, vol. 21, issue 1, 1-15

Abstract: Objective: To develop and internally validate a Cox model predicting 1.5-year adverse outcomes (cardiovascular admission or all-cause mortality) in incident hemodialysis (HD) patients by integrating routinely recorded dialysis-machine parameters with traditional indicators. Methods: We retrospectively analyzed 74 incident end-stage renal disease (ESRD) patients who commenced thrice-weekly HD at Huashan Hospital, Fudan University, between 2012 and 2018. A total of 83 candidate variables, including demographics, traditional indicators (Kt/V, phosphorus, parathyroid hormone [PTH], albumin, hemoglobin, ultrafiltration volume), and dialysis machine parameters, were evaluated. Univariable and multivariable Cox regression identified predictors of 1.5-year outcomes. Results: The mean (± SD) age of the study population was 62 ± 14 years, and 55.4% were male. Independent predictors included serum alkaline phosphatase (ALP) measured at month 3 and machine-derived bicarbonate conductivity (BC) at month 6. A model combining ALP (month 3), bicarbonate conductivity (month 6), and traditional indicators (month 6) showed strong discrimination (AUC = 0.82). Achieving targets in ≥5 of 8 indicators—including ALP and BC—was associated with significantly better outcomes (log-rank p = 0.018). Conclusion: Integrating ALP and machine-derived BC into a Cox model significantly improves risk stratification in incident HD patients and facilitates the implementation of automated quality control.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0340994

DOI: 10.1371/journal.pone.0340994

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