Machine learning-based prediction of permanent ileostomy in older adults after laparoscopic anterior resection for rectal cancer.

ABSTRACT:

Objective

Conversion from temporary stoma to a permanent stoma (PS) following loop ileostomy (LI) significantly impairs patients' quality of life and prognosis. However, few predictive tools are currently available in clinical practice. This study aims to develop and validate a machine learning (ML) model to predict the risk of PS following laparoscopic anterior resection (LAR) in older adults with rectal cancer.

Methods

Clinical data were retrospectively collected from 956 older adults (age ≥ 60 years) who underwent LAR with LI between June 2015 and December 2024 at two campuses of the First Hospital of China Medical University. After random stratification into training and test sets (7:3 ratio), variables were selected using the Least Absolute Shrinkage and Selection Opera

SUBMITTER: Zhang M 

PROVIDER: S-EPMC12802109 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature