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.

Keywords::

• Epidemiology
• Health Services
• Medical co-morbidities
• Risk factors

Transparency

Declaration of funding

Editorial support for this paper was funded by Endo Pharmaceuticals Inc.

Declaration of financial/other relationships

R.L.S. and A.L.B. have disclosed that they have no significant relationships with or financial interests in any commercial companies related to this study or article. S.B.C. and L.M. have disclosed that they are employees of Endo Pharmaceuticals Inc.

CMRO peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgments

The authors thank Angela M. Ginkel, of American Medical Systems Inc. (AMS), Minnetonka, MN, USA, for editorial review support.

Previous presentation: The Annual Meeting of the American Urological Association (AUA), 4–8 May 2013, San Diego, CA, USA.