acePredicting the Risk of Unplanned Hospital Encounters in Hemodialysis Patients: A Comparison Between Clinicians and a Machine Learning Prediction Algorithm
Rationale & objective: 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. Study design: We created an ML-based prediction model to stratify hemodialysis (HD) patients' risk of UHE in the subsequent 7 and 30 day…
For 7-day predictions the model was less precise than clinicians, so it produced proportionally more false alarms, and whether it reduces hospital encounters is not yet shown.
The model was developed only in hemodialysis patients at a single center, which limits external validation, and its effect on reducing unplanned encounters has not yet been tested.
Evidence
- Peer-reviewedKidney Medicine2026-08-13
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Truvace Impact Record TRV-2026-1891, v1: “Predicting the Risk of Unplanned Hospital Encounters in Hemodialysis Patients: A Comparison Between Clinicians and a Machine Learning Prediction Algorithm.” Truvace, 2026-10-19. /record/TRV-2026-1891 (accessed at citation time). sha256 2c98139e50dd0901…
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