Construction and validation of a predictive model for the return to work of patients with permanent colorectal stoma: a cross-sectional study

The issue of patients returning to work is increasingly garnering attention from countries worldwide. This study aims to investigate the risk factors associated with patients returning to work after undergoing permanent enterostomies. Additionally, it seeks to establish and validate a nomogram prediction model, thereby providing a more effective reference for patients aiming to return to work. This study was a cross-sectional investigation conducted between September 2022 and September 2023. We conveniently selected 293 postoperative patients with permanent colorectal stomas due to colorectal cancer from three tertiary hospitals in Liaoning Province. Participants were categorized into Returned and Non-Returned groups based on their return to work status. Data were collected using a general information questionnaire, a Stoma Acceptance Questionnaire, and the Ostomy Adjustment Inventory. Binary logistic regression analysis was performed using SPSS 25.0 software to identify independent influencing factors. A predictive model was constructed using R Studio 4.3.0 software. Internal validation was conducted through 1,000 rounds of Bootstrap resampling, and model performance was assessed using Receiver Operating Characteristic (ROC) curves, the Hosmer-Lemeshow (H-L) test, and calibration curves. After surgery, the return-to-work rate for patients with permanent colorectal stomas was 29.69%. Age, education level, postoperative time, stoma complication, adjuvant therapy, stoma acceptance score, and ostomy adjustment inventory score were identified as independent factors influencing the return-to-work status of these patients (P 0.05). Calibration curves indicated a close alignment between predicted and actual probabilities. The postoperative return-to-work rate is low in patients with permanent enterostomies. The prediction model developed in this study demonstrates strong performance and offers predictive value, providing a scientific foundation for assessing patients' return to work. Caregivers should prioritize the early identification of various patient types for proactive interventi