Development of clinical models for predicting erectile function after localized prostate cancer treatment - Abstract

OBJECTIVES: To develop clinical prediction models estimating the probability of maintaining erections adequate for intercourse 2 years after prostate cancer treatment, based on pretreatment characteristics.

METHODS: Study participants consisted of prostate cancer patients with localized disease and functional erections before undergoing surgery (n = 536) or radiation therapy (n = 240) at a single USA institution. Baseline patient- and treatment-related data were collected from a clinical database and through chart review. Erectile function at 2 years post-treatment was prospectively assessed through a self-administered single-item measure. Multivariate logistic regression using backward selection was used to derive clinical prediction models to predict erectile function at 2 years for surgery and radiation therapy patients; the models were internally validated using bootstrapping methods.

RESULTS: The final prediction model for surgery patients included the predictor variables of age, body mass index, smoking, diabetes, hypertension and nerve-sparing procedures, whereas the model for radiation therapy patients included hypertension, risk category and radiation technique. The new models showed acceptable calibration and discrimination: c-statistic = 0.71 (95% confidence interval 0.68-0.76) for surgery and 0.66 (95% confidence interval 0.61-0.74) for radiation therapy models.

CONCLUSIONS: New clinical prediction models based on patient and treatment characteristics show promising accuracy in predicting erectile function at 2 years in patients treated with surgery and radiation for localized prostate cancer. More work is required to confirm and validate these models in different patient populations.

Written by:
Haskins AE, Han PK, Lucas FL, Bristol I, Hansen M.   Are you the author?
Center for Outcomes Research and Evaluation, Maine Medical Center, Portland, Maine, USA.

Reference: Int J Urol. 2014 Jul 23. Epub ahead of print.
doi: 10.1111/iju.12566


PubMed Abstract
PMID: 25056284

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