Prediction model of uroschesis rate after radical cervical cancer resection based on machine learning
10.3760/cma.j.cn211501-20231016-00751
- VernacularTitle:基于机器学习的宫颈癌根治术后尿潴留风险预测模型构建
- Author:
Hui ZHANG
1
;
Yanqiong OUYANG
;
Xiuhua HUANG
Author Information
1. 武汉大学护理学院，武汉 430064
- Keywords:
Urinary retention;
Machine learning;
Model building;
Risk prediction;
Radical cervical cancer resection
- From:
Chinese Journal of Practical Nursing
2024;40(7):520-526
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a risk prediction model for urinary retention in patients undergoing radical cervical cancer surgery based on machine learning, and the prediction effect of the model was internally verified and evaluated, in order to provide reference for the early prevention and treatment of urinary retention in patients undergoing radical cervical cancer surgery.Methods:A total of 981 patients who underwent radical cervical cancer surgery in the First Affiliated Hospital of Anhui Medical University from June 2017 to February 2022 were selected and divided into the training set (687 cases) and the test set (294 cases) according to a ratio of 7∶3. Through literature review and risk factor analysis, the influencing factors of urinary retention after radical treatment of cervical cancer were explored, and the risk prediction model of urinary retention was constructed by using XGBoost, random forest, support vector machine and decision tree in machine learning. The accuracy rate, recall rate, F1 value and AUC of four machine learning algorithms were calculated by using the method of 10-fold cross-validation, and the model with the highest predictive efficiency was selected.Results:Among the 981 patients included, the incidence of urinary retention after radical cervical cancer surgery was 18.86% (185/981). The median age of urinary retention group was 51 years old, and that of non urinary retention group was 50 years old. Statistically significant variables in the univariate analysis and influencing factors summarized by literature review were featured, including patient age, intraoperative blood loss, body mass index (BMI), cancer stage, surgical method, surgical resection scope, whether pelvic lymph node dissection was performed, comorbidities and residual urine. Among the four model building methods of machine learning, the random forest model has the best effect, its training set F1 value was 0.94, the test set F1 value was 0.77, the ROC was plotted and the AUC was calculated to be 0.73. Age, intraoperative blood loss, BMI, cancer stage and surgical method contributed significantly to the classification of random forest model.Conclusions:The prediction model of urinary retention risk after radical cervical cancer surgery based on random forest method has the best efficacy. It is useful to help nursing personnel evaluate the risk of the uroschesis for a patient and then take targeted nursing interventions to actively prevent postoperative urinary retention.
