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 <16 and Qmax ≥13ml/sec, (AUC of 75.0%). Random forest model, involving all clinical parameters except BD, had AUC of 94.7%. Internal validation by bootstrap method showed reasonable AUCs (69.6-85.8%). Analyses using BD data improved the model fits marginally. External validation gave comparable AUCs for logistic regression, classification tree analysis and random forest (all without BD) (70.9%, 67.3% and 68.5%, respectively). Limitations included the significant number of men providing incomplete baseline data, and limited assessments in the external validation cohort.
Conclusions; Outcome of prostate surgery is predicted preoperatively by age, total IPSS and uroflowmetry, with prognostic thresholds of 16 for IPSS and 13 ml/sec for Qmax.
Patient Summary; This study identified key preoperative factors that can predict prostate surgery outcome, including which patients are at risk of bad outcome.