Predicting prostate surgery outcome from standard clinical assessments of lower urinary tract symptoms to derive prognostic symptom and flowmetry criteria

Background; Assessment of male lower urinary tract symptoms (LUTS) needs to identify predictors of symptom outcomes, where interventional treatment is planned. Objective; Develop a novel prediction model for prostate surgery outcomes and validate it using a separate patient cohort, deriving thresholds for key clinical parameters. Design, Setting, and Participants; The UPSTREAM trial of 820 men seeking treatment for LUTS, analysing bladder diary (BD), IPSS, IPSS-QoL, and uroflowmetry data of 176 participants who underwent prostate surgery and provided complete data. External validation used a retrospective surgery outcomes database from a Japanese urology department (n = 227). Outcome Measurements and Statistical Analysis; Symptom improvement was defined as ≥3 points reduction in total IPSS. Multiple logistic regression, classification tree analysis and random forest models were generated, including versions with and without BD data. Results and Limitation; Multiple logistic regression without BD identified age (P=0.029), total IPSS (P=0.0016), and maximum flow rate (Qmax) (P=0.066) as predictors of outcome, with area under curve (AUC) of 77.1%. Classification tree analysis without BD gave thresholds of IPSS