Clinical Phenotypes, Machine‐Learning Risk Stratification and Treatment Outcomes of Constipation in Stroke Patients Receiving Enteral Nutrition: A Retrospective Study
Su‐Xia Zhang, Yu‐Kun Zhang, Ye Li, Wei‐Fan Yin, Rong‐Fang Yi, Qing HuABSTRACT
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.
Aim
This study aimed to explore potentially important determinants of enteral nutrition‐associated constipation (ENAC) in patients with stroke using artificial intelligence approaches, characterise relevant clinical subgroups, compare internally validated machine‐learning models and examine how the temporal availability of candidate predictors may influence model performance.
Study Design
Clinical data from patients admitted to a neurological intensive care unit between December 2019 and December 2023 were retrospectively analysed. K‐means clustering was used to identify clinical subgroups. Six algorithms—logistic regression, random forest, gradient boosting tree, XGBoost, LightGBM and Table PFN‐3—were compared under temporally restricted and expanded retrospective feature frameworks using five repeats of nested five‐fold cross‐validation. Performance was assessed using ROC‐AUC, average precision, Brier score, calibration and decision‐curve analysis. Treatment outcomes were summarised descriptively.
Results
Among 362 patients, 233 (64.4%) had documented constipation. Table PFN‐3 achieved the highest discrimination in both frameworks. The temporally restricted model yielded a ROC‐AUC of 0.629 (95% CI 0.567–0.689), whereas the expanded retrospective model achieved a ROC‐AUC of 0.771 (95% CI 0.715–0.820), average precision of 0.857, Brier score of 0.177 and calibration slope of 0.965. The ROC‐AUC increased by 0.142 (95% CI 0.090–0.201) after full‐hospitalisation information was included. K‐means clustering retained three subgroups ( K = 3; silhouette coefficient = 0.108), with constipation frequencies of 67.35%, 56.98% and 88.89% in Clusters 0, 1 and 2, respectively. Among evaluable patients, observed relief was 80.3% with glycerin enema, 60.0% with lactulose, 72.7% with combination treatment and 65.7% with neither treatment; these treatment comparisons were descriptive.
Conclusions
Table PFN‐3 achieved the strongest internally validated performance, with the expanded retrospective framework combining good discrimination, strong precision–recall performance and close calibration. The 0.142 increase in ROC‐AUC after completed hospital‐course variables were incorporated quantifies the substantial discriminative information that emerges as hospitalisation evolves. Together with the distinct clinical phenotypes and descriptive treatment findings, these results establish a clinically relevant framework for trajectory‐aware bowel monitoring and provide a clear rationale for developing dynamic, time‐updated prediction models.
Relevance to Clinical Practice
By integrating machine‐learning benchmarking, clinical phenotyping and observed treatment outcomes, this study identifies prolonged, treatment‐intensive hospital trajectories as a particularly high‐burden constipation phenotype and demonstrates that classification performance changes materially as hospital‐course information accumulates. These findings support structured reassessment of bowel risk during neurological intensive care rather than reliance on a single static evaluation and provide a practical rationale for future dynamic monitoring tools.