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TRUVACE RECORD VERSION
record: TRV-2026-2313
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-29T18:35:12.995328Z
status: published
lens: g_space
sector: health
headline: Clinical Phenotypes, Machine-Learning Risk Stratification and Treatment Outcomes of Constipation in Stroke Patients Receiving Enteral Nutrition: A Retrospective Study
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: the expanded retrospective model achieved a ROC-AUC of 0.771 (95% CI 0.715-0.820)
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers retrospectively analysed records of 362 stroke patients treated in a neurological intensive care unit between 2019 and 2023, all receiving enteral nutrition. About 64% had documented constipation. Six algorithms were compared, and the TabPFN-based model discriminated best.

The findings suggest bowel risk should be reassessed repeatedly during intensive care rather than judged once, and they support developing dynamic, time-updated tools. The model was only internally validated on retrospective data, so its value for real-time bedside use has not been shown.
limitation: 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).
tag: Evidence-backed gain
key_points: Of 362 stroke patients, 233 (64.4%) had documented constipation. | Table PFN-3 performed best; ROC-AUC was 0.629 with temporally restricted data and 0.771 with full-hospitalisation data. | ROC-AUC rose by 0.142 once completed hospital-course variables were included. | K-means clustering found three subgroups, with constipation frequencies of 67.35%, 56.98% and 88.89%. | Observed relief was 80.3% with glycerin enema, 60.0% with lactulose, 72.7% with combined treatment and 65.7% with neither; these comparisons were descriptive.
rundown: Patients came from a single neurological ICU. Six models were compared: logistic regression, random forest, gradient boosting tree, XGBoost, LightGBM and Table PFN-3. Evaluation used five repeats of nested five-fold cross-validation, with discrimination, precision, calibration and decision-curve analysis.

Models were run under a temporally restricted feature set and an expanded retrospective set. Discrimination was modest early (ROC-AUC 0.629) and much better with data from the whole hospital stay, showing that predictive information accumulates as admission progresses.

Clustering identified three clinical phenotypes, one with an 88.89% constipation frequency, described as a prolonged, treatment-intensive trajectory. Treatment results were summarised only descriptively, without controlled comparison.
sources:
- peer_reviewed | Nursing in Critical Care | https://doi.org/10.1111/nicc.70763 | 2026-11-01
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