The risk factors related to the severity of pain in patients with Chronic Prostatitis/Chronic Pelvic Pain Syndrome

Jing Chen, Haomin Zhang, Di Niu, Hu Li, Kun Wei, Li Zhang, Shuiping Yin, Longfei Liu, Xiansheng Zhang, Meng Zhang, Chaozhao Liang, Jing Chen, Haomin Zhang, Di Niu, Hu Li, Kun Wei, Li Zhang, Shuiping Yin, Longfei Liu, Xiansheng Zhang, Meng Zhang, Chaozhao Liang

Abstract

Background: Chronic Prostatitis/Chronic Pelvic Pain Syndrome (CP/CPPS) is a disease with diverse clinical manifestations, such as pelvic pain or perineal pain. Although recent studies found several risk factors related to the pain severity of CP/CPPS patients, results were inconsistent. Here, we aimed to identify novel risk factors that are closely related to the severity of pain in patients with CP/CPPS.

Methods: We retrospectively collected the clinical records from patients with CP/CPPS from March 2019 to October 2019. The questionnaire was used to obtain related parameters, such as demographics, lifestyle, medical history, etc. To identify potential risk factors related to pain severity, we used the methods of univariate and multivariate logistic regression analyses. Further, to confirm the relationship between these confirmed risk factors and CP/CPPS, we randomly divided CP/CPPS patients into the training and the validation cohorts with a ratio of 7:3. According to the co-efficient result of each risk factor calculated by multivariate logistic regression analysis, a predicting model of pain severity was established. The receiver operating characteristic curve (ROC), discrimination plot, calibration plot, and decision curve analyses (DCA) were used to evaluate the clinical usage of the current model in both the training and validation cohorts.

Results: A total of 272 eligible patients were enrolled. The univariate and multivariate logistic regression analysis found that age [odds ratio (OR): 2.828, 95% confidence intervals (CI): 1.239-6.648, P = 0.004], holding back urine (OR: 2.413, 95% CI: 1.213-4.915, P = 0.005), anxiety or irritability (OR: 3.511, 95% CI: 2.034-6.186, P < 0.001), contraception (OR: 2.136, 95% CI:1.161-3.014, P = 0.029), and smoking status (OR: 1.453, 95% CI: 1.313-5.127, P = 0.013) were the risk factors of pain severity. We then established a nomogram model, to test whether these factors could be used to predict the pain severity of CP/CPPS patients in turn. Finally, ROC, DCA, and calibration analyses proved the significance and stability of this nomogram, further confirmed that these factors were closely related to the pain severity of CP/CPPS patients.

Conclusions: We identify age, holding back urine, anxiety or irritability, contraception, and smoking are risk factors closely related to the pain severity in patients with CP/CPPS. Our results provide novel inspirations for clinicians to design the personalized treatment plan for individual CP/CPPS patient who has suffered different encounters.

Keywords: Chronic prostatitis; Nomogram; Pain; Personalized treatment; Risk factors.

Conflict of interest statement

All the authors state they have no conflicts of interest.

Figures

Fig. 1
Fig. 1
Prognostic analysis patient disposition
Fig. 2
Fig. 2
Novel nomogram of predicting the risk of the severity of pain of CP/CPPS patients. The severity of the pain of CP/CPPS patients nomogram was developed in the cohort, with the use of age, holding back urine, anxiety or irritability, contraception, and smoking
Fig. 3
Fig. 3
Apparent performance of the predictive nomogram in the cohort. Calibration curves of the pain severity nomogram prediction in the training cohort (a) and the validation cohort (d): The x-axis represents the predicted pain severity of CP/CPPS patients. The y-axis represents the actual pain severity of CP/CPPS patients. Receiver operating characteristic (ROC) curves of the nomogram in the training cohort (b) and the validation cohort (e): The ROC curve is displayed in solid line, and the reference is displayed in dotted line. The ROCs of the predictive nomogram in the training and validation cohorts, with the AUC of 0.781 and 0.735, respectively. Decision curve analysis (DCA) of the nomogram in the training cohort (c) and the validation cohort (f): The y-axis measures the net benefit. The blue solid line represents the pain severity predictive nomogram. The thin solid line represents the hypothesis that all patients are mild pain. The thin thick solid line represents the assumption that patients are moderate to severe pain. The DCA showed that if the threshold probability of a patient and a doctor are > 25% and < 83% in training cohort (c) and > 16% and < 78% in the validation cohort (f), respectively. Using this predictive nomogram to predict the pain severity of CP/CPPS patients adds more benefit than the intervention-all-patients scheme or the intervention-none scheme

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