Predicting the Risk of Unplanned Hospital Encounters in Hemodialysis Patients: A Comparison Between Clinicians and a Machine Learning Prediction Algorithm.

Authors

Samiksha Singh, Lesley Donovan, Meggie Debnath, Maitreyee Sidhaye, David Dai, Yang Zhu, Mitchell Hughes, Sunghoon Minn, Sanja Neskovic, Jamie Beverley, Tony Antoniou, Ivana Prce, Sebnem Kuzulugil, Michael Fralick, Derek Beaton, Muhammad Mamdani, Ron Wald

Published in

Kidney medicine. Volume 8. Issue 11. Pages 101500. Epub Aug 13, 2026.

Abstract

Dialysis patients have a higher risk of hospitalization than the age-matched general population. Unplanned hospital encounters (UHEs), including emergency department visits and hospitalizations, may be preventable but are difficult to anticipate. We aimed to stratify dialysis patients' risk of UHE, using a machine learning (ML)-based prediction model.
We created an ML-based prediction model to stratify hemodialysis (HD) patients' risk of UHE in the subsequent 7 and 30 days. We compared the performance of this prediction model with that of clinicians.
627 patients who received maintenance HD at an academic hospital from October 2015 to April 2022 for model development and 285 patients for a comparison with clinician predictions.
Demographic and clinical data as well as data derived from the dialysis treatment sessions, updated weekly.
Clinicians demonstrated a higher positive predictive value (PPV) for a UHE in the subsequent 7 days (0.14 for clinicians vs 0.07 for the ML model; P < 0.001) but lower sensitivity (0.25 for clinicians vs 0.63 for the ML model; P < 0.001). The PPV for the 30-day UHE was comparable between clinicians and the model (0.25 for clinicians vs 0.21 for the ML model; P = 0.54), but sensitivity was nearly three-fold higher for our ML model relative to clinicians (0.24 for clinicians vs 0.73 for the ML model; P < 0.001).
Our ML-based prediction model was only developed within a cohort comprising HD recipients at a single center, limiting external validation.
Using ML, we were able to anticipate UHE with greater sensitivity than clinician predictions with comparable PPV. Further research will determine if integration of our prediction tool into clinical care will lead to fewer UHEs.

PMID:
42894684
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.

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