DOI: 10.6084/m9.figshare.31111737.v1
title: Mortality prediction in hemodialysis patients using heart rate variability and skin sympathetic nerve activity

description: Patients undergoing maintenance hemodialysis (HD) face a substantially elevated risk of all-cause mortality, yet robust tools for individualized risk stratification remain limited. This multicenter study developed a predictive model integrating dynamic autonomic nervous system (ANS) markers – heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) – with clinical factors to assess mortality risk. We enrolled 198 HD patients from two Chinese centers between 2021 and 2023, recording HRV/SKNA parameters at baseline, 30 min, and 240 min into dialysis. Over a median follow-up of 34 months, the all-cause mortality rate was 17.7%. Ninety-one baseline features were included in the LASSO-regression model. The final multivariable logistic regression model incorporated six variables (diabetes mellitus, DBP<sub>2h</sub>, RMSSD<sub>240</sub>, ΔNnmean<sub>30</sub>, ΔApEn<sub>30</sub> and ΔaSKNA<sub>240</sub>) into the nomogram. The AUC of the nomogram for predicting one-year, two-year, and three-year survival rates was 0.764, 0.749, and 0.805, respectively. The Kaplan–Meier curves for overall survival stratified by nomogram model showed a significant difference between high- and low- risk groups. Internal validation <i>via</i> bootstrap resampling confirmed model robustness, with optimism-corrected AUCs of 0.758, 0.736, and 0.788 for one-, two-, and three-year mortality, respectively. The model demonstrated superior predictive accuracy for cardiovascular mortality (C-index = 0.881) and consistent performance across age and sex subgroups. The proposed model has the potential to predict all-cause mortality in HD patients and may enable earlier intervention and personalized management. Patients undergoing maintenance HD face high mortality risks, yet existing risk stratification tools lack integration of dynamic ANS markers. This study developed a predictive model integrating intradialytic HRV/SKNA parameters with clinical factors to improve risk assessment.The LASSO-based nomogram identified six predictors (diabetes mellitus, DBP<sub>2h</sub>, RMSSD<sub>240</sub>, ΔNnmean<sub>30</sub>, ΔApEn<sub>30</sub> and ΔaSKNA<sub>240</sub>) and achieved AUCs of 0.764, 0.749, and 0.805 for one-year, two-year and three-year mortality prediction respectively, with significant stratification of high- and low-risk groups (<i>p</i> &lt; .0001).This model facilitates early identification of high-risk MHD patients, offering a practical tool to guide personalized interventions and potentially reduce mortality through targeted management of ANS dysfunction. Moreover, the findings highlight the critical role of dialysis-induced autonomic instability in long-term outcomes. Patients undergoing maintenance HD face high mortality risks, yet existing risk stratification tools lack integration of dynamic ANS markers. This study developed a predictive model integrating intradialytic HRV/SKNA parameters with clinical factors to improve risk assessment. The LASSO-based nomogram identified six predictors (diabetes mellitus, DBP<sub>2h</sub>, RMSSD<sub>240</sub>, ΔNnmean<sub>30</sub>, ΔApEn<sub>30</sub> and ΔaSKNA<sub>240</sub>) and achieved AUCs of 0.764, 0.749, and 0.805 for one-year, two-year and three-year mortality prediction respectively, with significant stratification of high- and low-risk groups (<i>p</i> &lt; .0001). This model facilitates early identification of high-risk MHD patients, offering a practical tool to guide personalized interventions and potentially reduce mortality through targeted management of ANS dysfunction. Moreover, the findings highlight the critical role of dialysis-induced autonomic instability in long-term outcomes.