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

## TL;DR

This study develops a survival prediction model for hemodialysis patients using machine parameters and traditional indicators to improve risk stratification and quality control.

## Contribution

The novel contribution is integrating dialysis machine parameters with traditional indicators to enhance survival prediction in incident hemodialysis patients.

## Key findings

- Serum alkaline phosphatase at month 3 and bicarbonate conductivity at month 6 were independent predictors of adverse outcomes.
- A combined model using ALP, BC, and traditional indicators achieved an AUC of 0.82 for 1.5-year outcomes.
- Meeting ≥5 of 8 quality indicators was associated with significantly better patient outcomes.

## Abstract

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.

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.

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).

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.