Development and Validation of a Dynamic Online Nomogram Prediction Model for Assessing the Risk of Erectile Dysfunction.

Purpose: Erection dysfunction (ED) represents a globally prevalent men's health problem and affected by a variety of factors. This study aimed to develop a dynamic nomogram model to assess the probability of ED in a population through a multitude of factors.

Materials and methods: A total of 2,668 subjects from the National Health and Nutrition Examination Survey were included in this study. The entire dataset was randomly divided into training and validation sets, with the training set comprising 70% of the data and the validation set comprising 30%. The Least Absolute Shrinkage and Selection Operator and multivariate logistic regression analysis determined the predictors for constructing the nomogram, and the model was evaluated by Concordance Index (C-index), calibration curve, Hosmer-Lemeshow test, and decision curve analysis.

Results: The nomogram model consisted of 9 predictors, which were age, education, stroke, lymphocyte, diabetes, poverty income ratio, prostate disease, activity, and hypertension. The C-index for the training set was 0.828 and for the validation set was 0.825, indicating that the model shows good clinical applicability and calibration of the model on both the training and validation sets. Additionally, we created an online dynamic nomogram (https://wvknly-liu-guodong.shinyapps.io/dynnomapp/) that anyone can evaluate on a web page.

Conclusions: Our dynamic nomogram can integrate multiple risk factors to provide a personalized risk assessment that is highly clinically predictive and provides a valuable tool for early intervention and prospective management of ED. This could help physicians to identify and manage high-risk populations early and provide personalized treatment plans.