Predicting Acupuncture Efficacy in Chronic Prostatitis/Chronic Pelvic Pain Syndrome: A Study on Model Development and Result Visualization

Abstract

<b><i>Introduction:</i></b> Acupuncture is one of primary treatment options for chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS), but its efficacy varies among patients. This study aimed to develop and validate a nomogram for predicting the efficacy of acupuncture in CP/CPPS. <b><i>Methods:</i></b> This study enrolled 220 patients with CP/CPPS who received acupuncture. Patients were divided into a responder group and nonresponder group based on the reduction in the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI). Potential variables were selected using the least absolute shrinkage and selection operator regression, and a nomogram was established using the multivariable logistic regression model. The performance of the nomogram was assessed by the receiver operating characteristic curves and calibration curves. <b><i>Results:</i></b> Two Hundred Twenty men were randomly assigned to the training cohort (<i>n</i> = 154) and the internal test cohort (<i>n</i> = 66). The developed nomogram included age, current drinking status, sedentary lifestyle, habit of staying up late, expectations for acupuncture, comorbidities, NIH-CPSI pain subscale and total scores. The area under the curve of the prediction model was 0.777 (95% CI: 0.702–0.851) in the training cohort, 0.752 (95% CI: 0.616–0.888) in the internal test cohort, demonstrating satisfactory discriminative ability as indicated by the calibration curve. <b><i>Conclusions:</i></b> The nomogram accurately identified CP/CPPS patients who would benefit from acupuncture. Factors such as youth, abstention from alcohol, avoiding sedentary habits and staying up late, having high expectations for acupuncture, being free from comorbidities, and baseline high scores on both the NIH-CPSI pain subscale and total scores may positively affect the efficacy of acupuncture. Further validation of our findings requires multicenter and large-sample prospective studies.