1. Ministry-of-Education Key Laboratory of Hazard Assessment and Control in Special Operational Environment, Department of Military Health Statistics, Faculty of Military Preventive Medicine, Air Force Medical University, Xi’an, Shaanxi 710032, China; 2. Xijing Hospital of Air Force Medical University, Xi’an, Shaanxi 710032, China
Abstract：Objective To explore the related factors affecting premature ejaculation (PE) patients, and to establish a predictive model for PE based on LASSP logistic regression. Methods A predictive model for PE based on LASSO logistic regression was constructed through the results of questionnaire surveys and scale scores of male subjects recruited from outpatient departments of 5 hospitals like Xijing Hospital. The cross validation method was used to select the coefficient λ. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were employed to evaluate the performance of LASSP logistic model, and the evaluation results were compared with the parameters of full-variable logistic regression and stepwise logistic regression. Area under curve and calibration curve were applied to evaluating the discriminability and accuracy of the model, and the nomogram was drawn. Results A total of 3,180 subjects were enrolled into this study, with 2,663 (83.7%) cases in the PE group, 517 (16.3%) cases in the non-PE group. The λ selected by cross validation was 0.004. The independent variables included age, residence, occupation, IIEF-5 score, PEDT score and GAD-7 score. The values of AIC and BIC were 2,240.2 and 2,282.7 respectively, which were lower than those of full-variable logistic regression (2,262.9/2,292.2) and stepwise logistic regression (2,257.3/2,293.7). The ROC curve was used to analyze the predictive value of LASSO logistic regression model, and the results showed an area under the ROC curve of 0.799, which was significantly higher than those of full-variable logistic regression and stepwise logistic regression (P<0.05). Calibration curve confirmed that the nomogram model had high accuracy of prediction. Conclusion The noninvasive nomogram model based on LASSO logistic regression is established by using parameters including age, residence, occupation, IIEF-5 score, PEDT score and GAD-7 score, and as a quantitative tool for clinical diagnosis of PE, it has a high diagnostic efficiency and thus holds promise for clinical application.
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2023, Vol. 30 
