Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.
World J Gastroenterol
; 30(23): 2991-3004, 2024 Jun 21.
Article
em En
| MEDLINE
| ID: mdl-38946868
ABSTRACT
BACKGROUND:
Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM:
To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS:
Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (73 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model.RESULTS:
More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05) Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION:
This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Complicações Pós-Operatórias
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Reoperação
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Neoplasias Colorretais
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Aprendizado de Máquina
Limite:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
País/Região como assunto:
Asia
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article