aceClinical Phenotypes, Machine-Learning Risk Stratification and Treatment Outcomes of Constipation in Stroke Patients Receiving Enteral Nutrition: A Retrospective Study
Background: Stroke-related complications and frequent feeding interruptions often compromise nutritional intake, making enteral nutrition (EN) a cornerstone therapy to improve nutritional status and support recovery. Nevertheless, constipation is highly prevalent and clinically consequential among stroke patients receiving EN, particularly in critically ill populations, where enteral nutrition-associated constipation can impede rehabilitation, increase complications and prolong intensive care unit (ICU) stay. Ai…
With full-hospitalisation data, a machine-learning model (Table PFN-3) predicted constipation in stroke ICU patients on tube feeding with good discrimination (ROC-AUC 0.771) and close calibration.
The models were validated only internally on retrospective data, and the expanded model used information that becomes available only later in hospitalisation. Treatment comparisons were descriptive, and the clustering silhouette coefficient was low (0.108).
Evidence
- Peer-reviewedNursing in Critical Care2026-11-01
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Truvace Impact Record TRV-2026-2313, v1: “Clinical Phenotypes, Machine-Learning Risk Stratification and Treatment Outcomes of Constipation in Stroke Patients Receiving Enteral Nutrition: A Retrospective Study.” Truvace, 2026-10-29. /record/TRV-2026-2313 (accessed at citation time). sha256 ca17546ee57dc1be…
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