Predicting results of daily-practice cystoscopies.

Objective: Our objective was to elaborate a predictive model of bladder cancer, in an unselected clinical population submitted to cystoscopy.

Materials And Methods: We recruited consecutive patients that underwent cystoscopy due to suspicion of bladder cancer or surveillance of a previously diagnosed bladder cancer. Urine cytology and a BTA-stat® (BTA) test were carried out for all patients. To avoid an assessment bias, the BTA-tests, cytologies and cystoscopies were conducted in a blinded fashion. We used logistic regression to predict cystoscopy results from cytology, BTA-test and clinical variables.

Results: From August 2011 to July 2012, we recruited 244 patients and 237 were valid for analysis. Newly diagnosed and surveillance cases were 13% and 87% respectively. Cytology and BTA-test sensitivities were 57.9% (CI 95: 42.2-72.1) and 63.2% (CI 95: 47.3-76.6) with specificities of 84.4% (CI 95: 78.7-88.8) and 82.9% (CI 95: 77.1-87.5). The predictive model included the BTA-test, cytology, time since previous tumour, and treatment with mitomicin or BGC during the last three months. The model predictive accuracy (AUC) was .85 (.78-.92), and dropped to 0.79 when excluding the BTA-test (P=.026). For the surveillance of bladder cancer, a 10% threshold on the model predicted probabilities resulted in an overall negative predictive value of 95.7%, and 95.0% in low grade tumours.

Conclusion: In a cost containment environment, our prediction model could be used to space out cystoscopies in patients with previous, low grade tumours, resulting in a more efficient use of resources in the healthcare system.