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Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods

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  • Berrak Itır Aylı
  • Çiğdem Ikhlef
  • Ebru Gök Oğuz
  • Nüket Paksoy

Abstract

Background: Diabetes mellitus is the leading cause of end-stage renal disease and confers excess mortality in patients on hemodialysis. However, which diabetes-specific clinical parameters best predict survival remains insufficiently explored. Methods: This retrospective cohort study followed 252 patients receiving maintenance hemodialysis (126 with diabetes, 126 without) from three centres in Ankara, Turkey, over 68 months (January 2020–September 2025; 66 deaths). Mortality predictors were assessed using Cox proportional hazards models, propensity score–weighted regression, and gradient-boosted machine-learning survival analysis. Eleven diabetes-specific parameters were examined within the diabetes subgroup. Results: Diabetes was associated with reduced survival (log-rank p = 0.038) and a 68% higher mortality hazard (HR 1.68, 95% CI 1.02–2.75). After multivariable adjustment, the association was attenuated but directionally consistent across all specifications (HR 1.52–1.66). Within the diabetes subgroup, HbA1c was the strongest modifiable predictor (adjusted HR 1.43 per 1%, 95% CI 1.17–1.76, p

Suggested Citation

  • Berrak Itır Aylı & Çiğdem Ikhlef & Ebru Gök Oğuz & Nüket Paksoy, 2026. "Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods," PLOS ONE, Public Library of Science, vol. 21(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0349639
    DOI: 10.1371/journal.pone.0349639
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