Prediction model for penile prosthesis implantation for erectile dysfunction management

Abstract

Objective:Penile prosthesis surgery is indicated based on undesirability, contraindication or ineffectiveness of non-surgical options for erectile dysfunction. This definitive treatment is often delayed after initial diagnosis. Our objective was to develop a prediction tool based on a patient’s clinical history to determine likelihood of ultimately receiving a penile prosthesis.Research design and methods:This retrospective analysis used claims data from Commercial and Medicare supplemental databases. Inclusion criteria were 18 years of age with 1 year of continuous enrollment at the first diagnosis of erectile dysfunction. Patients’ demographics, co-morbidities and erectile dysfunction therapy were derived based on enrollment, medical and prescription histories.Main outcome measures:The Cox proportional hazards model with stepwise selection was used to identify and quantify (using relative risk) factors associated with a future penile prosthesis implant. Co-morbidities and therapies present prior to the index erectile dysfunction diagnosis were analyzed as fixed covariates.Results:Approximately 1% of the dataset’s population (N = 310,303 Commercial, N = 74,315 Medicare, respectively) underwent penile prosthesis implantation during the study period (3928 patients in the overall population: 2405 patients [0.78%] in the Commercial and 1523 patients [2.05%] in the Medicare population). Factors with the greatest predictive strength of penile prosthesis implantation included prostate cancer diagnosis (relative risk: 3.93, 2.29; 95% CI, 3.57–4.34, 2.03–2.6), diabetes mellitus (2.31, 1.23; 2.12–2.52, 1.1–1.37) and previous treatment with first-line therapy (1.39, 1.33; 1.28–1.5, 1.2–1.47) (all P < 0.01).Conclusion:The presence and extent of specific medical history factors at the time of erectile dysfunction diagnosis predict an individual’s future likelihood of penile prosthesis. Calculating the likelihood of penile prosthesis implantation based on the weight of these factors may assist clinicians with the definition of a care plan and patient counseling. The precision of the model may be limited by factors beyond medical history information that possibly influence the decision to proceed to surgery.