Predictive Factors for Tissue Inflammation in Patients with Benign Prostatic Hyperplasia: A Clinical Prediction Study.

Abstract

Introduction: Benign prostatic hyperplasia (BPH) is a common condition in older men, marked by the noncancerous enlargement of the prostate gland. Inflammation of the prostate plays a significant role in the progression of BPH and the symptoms it causes. The objective of this study was to create a predictive model for prostatic inflammation in men with BPH based on important clinical factors.

Methods: A retrospective cohort study was conducted with 137 patients diagnosed with BPH. Data collected included various factors such as age, prostate volume (PV), preoperative international prostate symptom score, preoperative maximum urine flow rate (Qmax), preoperative post-void residue, weight of the excised tissue, body mass index, fasting blood glucose (FBG), cholesterol levels, prostate-specific antigen, blood calcium, blood phosphorus, blood uric acid, triglycerides, hypertension status, and presence of prostate calcifications. Multivariate logistic regression and LASSO regression analyses were performed to identify significant predictors and develop a nomogram. The model's performance was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA).

Results: Among the patients, 9.49% showed no signs of prostatic inflammation, while 22.63% had mild, 47.45% had moderate, and 20.44% had severe inflammation. Factors such as PV, FBG, and prostate calcification were identified as important predictors of prostatic inflammation. The predictive model developed exhibited strong discrimination and calibration, as evidenced by a high area under the curve value, indicating reliable predictive accuracy. DCA further validated the clinical usefulness of the nomogram.

Conclusion: The developed nomogram, incorporating PV, FBG, and prostate calcification, effectively predicts prostatic inflammation in men with BPH. This tool can aid in early intervention and targeted treatment, potentially improving patient outcomes. Further validation in diverse populations is recommended to enhance its generalizability and clinical applicability.