Preoperative predictive model of recovery of urinary continence after radical prostatectomy.

OBJECTIVE: To build a predictive model of urinary continence recovery after radical prostatectomy (RP) that incorporates magnetic resonance imaging (MRI) parameters and clinical data. PATIENTS AND METHODS: We conducted a retrospective review of data from 2,849 patients who underwent pelvic staging MRI before RP from November 2001 to June 2010. We used logistic regression to evaluate the association between each MRI variable and continence at 6 or 12 months, adjusting for age, body mass index (BMI) and American Society of Anesthesiologists (ASA) score, and then used multivariable logistic regression to create our model. A nomogram was constructed using the multivariable logistic regression models. RESULTS: In all, 68% (1,742/2,559) and 82% (2,205/2,689) regained function at 6 and 12 months, respectively. In the base model, age, BMI and ASA score were significant predictors of continence at 6 or 12 months on univariate analysis (P < 0.005). Among the preoperative MRI measurements, membranous urethral length, which showed great significance, was incorporated into the base model to create the full model. For continence recovery at 6 months, the addition of membranous urethral length increased the area under the curve (AUC) to 0.664 for the validation set, an increase of 0.064 over the base model. For continence recovery at 12 months, the AUC was 0.674, an increase of 0.085 over the base model. CONCLUSION: Using our model, the likelihood of continence recovery increases with membranous urethral length and decreases with age, BMI and ASA score. This model could be used for patient counselling and for the identification of patients at high risk for urinary incontinence in whom to study changes in operative technique that improve urinary function after RP.

Combining smoking information and molecular markers improves prognostication in patients with urothelial carcinoma of the bladder.

OBJECTIVES: Tissue-based markers improve the accuracy of prediction models in urothelial carcinoma of the bladder (UCB). Current smoking status and cumulative exposure also affect outcomes. To evaluate whether the combination of molecular markers and smoking features further improved the prognostication of patients who underwent radical cystectomy (RC) for UCB. MATERIALS AND METHODS: A total of 588 patients underwent RC and bilateral lymphadenectomy for UCB from 1995 to 2005. Immunohistochemistry for p53, p21, pRB, p27, Ki-67, and survivin was performed on tissue microarrays from the RC specimen. Smoking features were routinely assessed at diagnosis. Multivariable Cox regression models assessed time to disease recurrence and cancer-specific mortality. RESULTS: Of the 588 patients, 128 were never (22%), 283 former (48%), and 177 current smokers (30%). In total, 227 patients experienced disease recurrence, whereas 190 died of UCB. Smoking status was independently associated with both outcomes (hazard ratio [HR] = 1.48 and 2.62, for former and current vs. never smokers, respectively, P<0.001). All markers were significantly associated with both outcomes (P<0.05) except for survivin. The combination of the 4 cell cycle markers p53, p21, pRB, and p27 increased the discrimination of clinicopathologic model for former and current vs. never smokers with c-indices 0.779 and 0.780, respectively (base model c-indices of 0.741 and 0.740 for former and current vs. never smokers, respectively). The further addition of smoking features and biomarker status improved the discrimination of the model (c-indices of 0.783 and 0.786 for former and current vs. never smokers, respectively). CONCLUSIONS: We confirmed that smoking information and tissue markers status improve prognostication of UCB outcomes after RC; the combination of both reaching the highest level of discrimination.