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Risk factors and risk prediction model for mucocutaneous separation in enterostomy patients: A single center
Yun Liu1, Hong Li1, Jin-Jing Wu1
1Department of Reconstructive, Hand and Plastic Surgery, Zhejiang Chinese Medical University, Lishui Central Hospital, 289 Kuocang Road, Liandu District, Lishui 323000, Zhejiang Province, China.
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.
Insights
This study developed a predictive model to identify patients at risk for mucocutaneous separation (MCS) after enterostomy. The model, incorporating factors like suture dislodgement and albumin levels, shows good accuracy for early MCS detection.
Area of Science:
- Surgical Complications
- Enterostomy Management
- Predictive Modeling in Medicine
Background:
- Mucocutaneous separation (MCS) is a frequent and morbid complication following enterostomy surgery.
- Effective early identification of patients at risk for MCS is essential but current predictive models are limited.
Purpose of the Study:
- To develop and validate a risk prediction model for mucocutaneous separation (MCS) in patients undergoing enterostomy.
- To assess the clinical predictive accuracy of the developed MCS risk model.
Main Methods:
- A cohort of 492 enterostomy patients (January 2019-March 2023) were analyzed.
- Patients were categorized into MCS (n=110) and non-MCS (n=382) groups based on early postoperative outcomes.
- Univariate, multivariate analyses, and ROC curve analysis were employed to identify predictors and evaluate model performance.
Main Results:
- The overall incidence of postoperative MCS was 22.4%.
- Independent predictors identified for MCS included suture dislodgement, low serum albumin, lower BMI, lower hemoglobin, intestinal rupture, incision infection, neoadjuvant therapy, stoma site, and elevated intra-abdominal pressure.
- The final model demonstrated strong predictive performance with an Area Under the Curve (AUC) of 0.827 and good clinical utility.
Conclusions:
- A robust prediction model for mucocutaneous separation in enterostomy patients has been successfully developed.
- This model offers valuable clinical utility for the early detection and management of MCS, improving patient outcomes.
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