背景:结直肠癌显著影响全球健康,手术后计划外再手术是决定患者预后的关键因素。这些再手术的现有预测模型在整合复杂的临床数据方面缺乏精确性。
目的:开发并验证用于预测结直肠癌患者非计划再手术风险的机器学习模型。
方法:回顾性收集温州医科大学附属第一医院和温州市中心医院2020年3月至2022年3月接受结直肠癌治疗的患者资料(n=2044)。根据计划外再手术的发生,将患者分为实验组(n=60)和对照组(n=1984)。还将患者分为训练组和验证组(7:3比例)。我们使用了三种不同的机器学习方法来筛选特征变量。基于多因素逻辑回归创建了一个列线图,并使用接收器工作特性曲线评估模型性能,校正曲线,Hosmer-Lemeshow测试,和决策曲线分析。计算并比较两组的风险评分,验证模型。
结果:实验组患者年龄≥60岁,男性,有高血压病史,剖腹手术,低蛋白血症,与对照组相比。多因素logistic回归分析证实以下因素是非计划再次手术的独立危险因素(P<0.05):剖腹手术史,高血压,或中风,低蛋白血症,年龄,肿瘤淋巴结转移分期,手术时间,性别,和美国麻醉医师学会分类。受试者工作特征曲线分析表明,该模型具有良好的鉴别性和临床实用性。
结论:这项研究使用机器学习方法建立了一个模型,可以准确预测结直肠癌患者术后非计划再次手术的风险,这可以改善治疗决策和预后。
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.
OBJECTIVE: 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 (7:3 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.
CONCLUSIONS: 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.