ArticleMedicine2025
Predictive model for colorectal cancer stoma prolapse based on clinical characteristics: Construction path and validation system.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
In clinical practice, it is not possible to identify people at high risk of stoma prolapse during the perioperative and early postoperative periods and to implement preventive strategies based on the clinical characteristics of individual patients. This study investigates the risk factors for the development of stoma prolapse and to develop relevant predictive models. A total of 270 patients were collected in this study out of which 62 patients had stoma prolapse; the patients enrolled in this study were randomly divided into a training set and a validation set according to a ratio of 7:3, with 189 patients in the training set and 81 patients in the validation set. Information about the patients' past medical history and hospitalization period was collected separately to study the correlates affecting the emergence of stoma prolapse in the patients and to establish a prediction model. Possibly relevant factors were included in a one-way logistic regression, and after analyzing the results: age, elevated intra-abdominal pressure, type of stoma, and hypoproteinemia were potential risk factors for the development of stoma prolapse during the 6-month postoperative period in patients who had undergone colorectal cancer stoma, P < .2; The data obtained were further included in a multifactorial review: age, elevated intra-abdominal pressure, type of stoma and hypo-proteinaemia were independent risk factors for stoma prolapse in patients with colorectal cancer stoma within 6 months after surgery, P < .05. This model provides clinicians with a powerful tool for early identification of patients at high risk of postoperative stoma prolapse. It helps to take targeted preventive and interventional measures before the onset of the disease.
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