Deconstructing Risk: A Machine Learning-Guided Logistic Regression Model for Predicting Early Mucosal Edema Following Prophylactic Ileostomy in Elderly Rectal Cancer Patients.

Authors

Yu Zeng, Li Bao, Jian Li, Yanyan Hong

Published in

International journal of general medicine. Volume 19. Pages 631230. Epub Sep 09, 2026.

Abstract

Early mucosal edema is a common and burdensome complication following prophylactic ileostomy in elderly rectal cancer patients, yet effective tools for its pre-operative prediction are lacking. This study aimed to develop and validate an interpretable, machine learning-guided logistic regression model for this specific outcome.
In this retrospective cohort study, 296 eligible patients were included. The cohort was randomly split into a training set (n=207) for model development and an internal test set (n=89) for validation. A consensus machine learning approach, integrating Least Absolute Shrinkage and Selection Operator (LASSO) regression, stepwise forward logistic regression, and Random Forest with Recursive Feature Elimination (RF-RFE), was employed to select core predictors from comprehensive clinical data. The final predictive model is a standard multivariable logistic regression. Model performance was validated via internal bootstrap resampling, ten-fold cross-validation, and external dataset testing, and assessed by discrimination, calibration, and decision curve analysis, with interpretability enhanced using SHAP (SHapley Additive exPlanations) values. Generalizability was tested in an external cohort (n=40).
Five variables were consistently selected as core predictors: the Prognostic Nutritional Index (PNI), preoperative neutrophil-to-lymphocyte ratio (NLR), intraoperative fluid intake, abdominal wall opening size, and time to first postoperative bowel movement. The model incorporates early postoperative variables and is intended for early perioperative risk stratification, not solely preoperative prediction. The final multivariable logistic regression model, presented as a nomogram, demonstrated good discriminatory ability in the training (AUC=0.838, 95% CI: 0.706-0.845) and test (AUC=0.826, 95% CI: 0.743-0.910) sets. Internal robustness was supported by ten-fold cross-validation (mean AUC=0.813, 95% CI: 0.680-0.960). The model maintained acceptable performance in the external validation cohort (AUC=0.820), though some miscalibration was noted. Subgroup analysis suggested that a smaller abdominal opening and delayed bowel function were specifically associated with progression to severe edema.
We developed and validated an interpretable, machine learning-guided logistic regression model for early mucosal edema, incorporating five readily available clinical variables. The model, available as an online nomogram, provides a practical tool for perioperative risk stratification, potentially guiding individualized patient counseling and targeted management strategies to mitigate this complication.

PMID:
42733687
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.