@Article{,
AUTHOR = {Uzoma A. Anele, Robert L. Segal, Brian V. Le, Arthur L. Burnett},
TITLE = {External validation of a prediction model for penile prosthesis implantation for erectile dysfunction management},
JOURNAL = {Canadian Journal of Urology},
VOLUME = {21},
YEAR = {2014},
NUMBER = {6},
PAGES = {7554--7559},
URL = {http://www.techscience.com/CJU/v21n6/61321},
ISSN = {1488-5581},
ABSTRACT = {<b>Introduction:</b> Penile prosthesis implantation (PPI) is the definitive surgical treatment for erectile dysfunction (ED), yet it is often delayed for a variety of reasons. From commercial and Medicare claims data, we previously developed a tool for determining a patient’s likelihood of eventually receiving PPI. We validated this instrument’s utility by comparing cohorts receiving surgical (PPI) versus non-surgical ED management at a single institution.<br/>

<b>Material and methods:</b> The prediction model was based on a logistic regression incorporating claims data on demographics, comorbidities and ED therapy. A risk score is calculated from the model as the product of relative risks for the individual variables. The current validation was a retrospective analysis of ED patients seen at this institution from January to December 2012. Inclusion criteria included ED diagnosis and either first-time PPI or non-surgical treatment (controls). Risk scores for patients receiving PPI were compared to those of non-surgical controls.<br/>

<b>Results:</b> We established a cohort of 60 PPI patients (mean age 54.4 ± 9.5) and compared them with 120 non-PPI patients (mean age 53.4 ± 11.2 years). The median score of the PPI cohort was 5.7 (IQR 2.8–9.9) versus the non-PPI cohort’s 1.8 (IQR 0.9–5.5) (p < 0.0001). The area under the receiver operator characteristic curve for predicting eventual PPI was 0.72 (95% CI, 0.64–0.79) (p < 0.0001).<br/>

<b>Conclusion:</b> The prediction model risk-stratified men who ultimately underwent PPI compared to non-surgically managed controls. This external validation study suggests that the prediction model may be used on an individual patient basis to support a recommendation of PPI for managing ED.},
DOI = {}
}