Comparative study on urinary incontinence prediction model constructed after radical prostatectomy based on decision tree C5.0 and Logistic regression
10.3760/cma.j.cn115682-20231127-02272
- VernacularTitle:基于决策树C5.0和Logistic回归构建前列腺癌根治术后尿失禁预测模型的比较研究
- Author:
Jie CHEN
1
;
Hua LI
;
Jiean DING
Author Information
1. 海军军医大学第一附属医院麻醉科手术室，上海 200433
- Keywords:
Radical prostatectomy;
Postoperative urinary incontinence;
Risk factor;
Decision tree C5.0;
Logistic regression;
Predictive performance
- From:
Chinese Journal of Modern Nursing
2024;30(28):3810-3818
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct risk prediction models for postoperative urinary incontinence in patients undergoing radical prostatectomy using decision tree C5.0 and Logistic regression, and to compare the predictive effects of the two models.Methods:A total of 260 patients with prostate cancer who underwent radical prostatectomy in the First Affiliated Hospital of Naval Medical University of PLA from January 2019 to January 2022 were selected by the convenient sampling method. Clinical data of the patients were retrospectively collected, and the patients were divided into the urinary incontinence group ( n=74) and the non-urinary incontinence group ( n=186) according to whether they had urinary incontinence during the six-month follow-up. The clinical data of the two groups were compared. Decision tree C5.0 and Logistic regression were retrospectively used to establish the risk prediction model of urinary incontinence after radical prostatectomy. The predictive performance of the two models was tested using receiver operating characteristic (ROC) curve, negative predictive value, positive predictive value, accuracy, sensitivity, specificity and Youden index. A total of 150 patients with prostate cancer undergoing radical prostatectomy in the First Affiliated Hospital of Naval Medical University of PLA were collected and the two prediction models were conducted external validation. Results:Both decision tree C5.0 and Logistic regression models showed that preoperative prostate volume ≥ 40 ml and age ≥ 60 years old were independent risk factors for postoperative urinary incontinence in patients undergoing radical prostatectomy ( P<0.05), while preserving the intact bladder neck during surgery was a protective factor for postoperative urinary incontinence in patients undergoing radical prostatectomy ( P<0.05). Meanwhile, Logistic regression analysis also showed that the body mass index ≥ 24 kg/m 2 was a risk factor for postoperative urinary incontinence ( P<0.05). The accuracy of decision tree C5.0 and Logistic regression models was 75.0% and 74.6%, respectively. The positive predictive values were respectively 58.2% and 59.1%, and negative predictive values were respectively 71.5% and 77.8%. The sensitivity was respectively 71.6% and 68.9%, and the specificity was respectively 67.2% and 74.7%. The Youden index was 38.8% and 43.6%, respectively. The areas under ROC curve were 0.744 and 0.777, respectively. External verification results showed that the area under ROC curve of the Decision Tree C5.0 model is 0.689, with a sensitivity and specificity of 71.1% and 66.7%, respectively, and the area under the Logistic regression model was 0.762, and the sensitivity and specificity were 77.8% and 63.8%, respectively. Conclusions:Age≥ 60 years old, body mass index≥ 24 kg/m 2 and preoperative prostate volume≥ 40 ml are independent risk factors for urinary incontinence after radical prostatectomy, while maintaining the intact bladder neck during surgery reduce the risk of postoperative urinary incontinence. The Logistic regression model constructed based on the above factors has better predictive performance for urinary incontinence after radical prostatectomy than the decision tree C5.0 model.
