Published online Mar 27, 2024. doi: 10.4240/wjgs.v16.i3.717
Peer-review started: September 27, 2023
First decision: December 28, 2023
Revised: January 12, 2024
Accepted: February 18, 2024
Article in press: February 18, 2024
Published online: March 27, 2024
The high incidence of postoperative complications in Crohn’s disease (CD) has prompted an urgent need for predicting postoperative risks, especially in perioperative decision-making. The improved machine learning (ML)-based models have demonstrated high accuracy in predicting medical outcomes and identifying high-risk patients.
Short-term major postoperative complications in CD deserve particular attention to enhance the accuracy of perioperative decision-making and the expected patient recovery.
This study aimed to clearly identify the key risk factors for short-term major postoperative complications in CD patients, construct and verify the logistics regression model and random forest (RF) model based on ML, so as to enhance the accuracy of surgical decision-making.
A retrospective analysis was conducted on surgical data from CD patients between 2017 and 2022. Patients underwent rigorous screening before being randomly assigned to training and validation groups. Independent risk factors for short-term postoperative major complications were determined by logistic regression analysis, and a nomogram prediction model was constructed. Concurrently, RF analysis was conducted to screen important factors for short-term post
Among the included 259 CD patients, it was observed that 5.0% experienced major complications within 30 d postoperatively. CD activity index ≥ 220, longer operation time, and reduced preoperative albumin levels were identified as significant factors influencing the occurrence of major postoperative short-term complications in both models.
Both the nomogram model and the RF model established in this study demonstrated good predictive performance, offer
The application of ML-based predictive models to assist personalized medical decision making in CD surgery is valuable. Predictive models across diverse clinical practices necessitate further integration, improvement, and promotion.