Risk factors and risk prediction model for mucocutaneous separation in enterostomy patients: A single center experience

Yun Liu; Hong Li; Jin-Jing Wu; Jian-Hong Ye

doi:10.12998/wjcc.v12.i33.6620

Risk factors and risk prediction model for mucocutaneous separation in enterostomy patients: A single center experience

World J Clin Cases. 2024 Nov 26;12(33):6620-6628. doi: 10.12998/wjcc.v12.i33.6620.

Authors

Yun Liu¹, Hong Li¹, Jin-Jing Wu¹, Jian-Hong Ye²

Affiliations

¹ Department of Reconstructive, Hand and Plastic Surgery, Zhejiang Chinese Medical University, Lishui Central Hospital, 289 Kuocang Road, Liandu District, Lishui 323000, Zhejiang Province, China.
² Department of Operation Room, Zhejiang Chinese Medical University, Lishui Central Hospital, 289 Kuocang Road, Liandu District, Lishui 323000, Zhejiang Province, China. 442339139@qq.com.

Abstract

Background: Mucocutaneous separation (MCS) is a common postoperative complication in enterostomy patients, potentially leading to significant morbidity. Early identification of risk factors is crucial for preventing this condition. However, predictive models for MCS remain underdeveloped.

Aim: To construct a risk prediction model for MCS in enterostomy patients and assess its clinical predictive accuracy.

Methods: A total of 492 patients who underwent enterostomy from January 2019 to March 2023 were included in the study. Patients were divided into two groups, the MCS group (n = 110), and the non-MCS (n = 382) based on the occurrence of MCS within the first 3 weeks after surgery. Univariate and multivariate analyses were used to identify the independent predictive factors of MCS and the model constructed. Receiver operating characteristic curve analysis was used to assess the model's performance.

Results: The postoperative MCS incidence rate was 22.4%. Suture dislodgement (P < 0.0001), serum albumin level (P < 0.0001), body mass index (BMI) (P = 0.0006), hemoglobin level (P = 0.0409), intestinal rapture (P = 0.0043), incision infection (P < 0.0001), neoadjuvant therapy (P = 0.0432), stoma site (P = 0.0028) and elevated intra-abdominal pressure (P = 0.0395) were potential predictive factors of MCS. Suture dislodgement [P < 0.0001, OR: 28.0075 95%CI: (11.0901-82.1751)], serum albumin level (P = 0.0008, OR: 0.3504, 95%CI: [0.1902-0.6485]), BMI [P = 0.0045, OR: 2.1361, 95%CI: (1.2660-3.6235)], hemoglobin level [P = 0.0269, OR: 0.5164, 95%CI: (0.2881-0.9324)], intestinal rapture [P = 0.0351, OR: 3.0694, 95%CI: (1.0482-8.5558)], incision infection [P = 0.0179, OR: 0.2885, 95%CI: (0.0950-0.7624)] and neoadjuvant therapy [P = 0.0112, OR: 1.9769, 95%CI: (1.1718-3.3690)] were independent predictive factors and included in the model. The model had an area under the curve of 0.827 and good clinical utility on decision curve analysis.

Conclusion: The mucocutaneous separation prediction model constructed in this study has good predictive performance and can provide a reference for early warning of mucocutaneous separation in enterostomy patients.

Keywords: Enterostomy; Mucocutaneous separation; Performance validation; Risk assessment model.