A new and simple nomogram for predicting the risk of bladder cancer in patients with microscopic hematuria could optimize the diagnostic work up process, according to a recent study.

The tool may help improve patient understanding about their risk of bladder cancer, as well as alleviate unnecessary diagnostic evaluations for some patients.

“The goal of this study was to identify objective clinical factors associated with a bladder cancer diagnosis and to use these factors to create a nomogram that accurately predicts risk of bladder cancer,” wrote Richard S. Matulewicz, MD, MS, of Northwestern University, Chicago, and colleagues in Urologic Oncology.

Researchers identified 4,178 patients with a new diagnosis of microscopic hematuria from 2007 to 2015. Data was collected from an enterprise data repository of the Northwestern Medicine healthcare system. Study participants who underwent a full microhematuria evaluation were randomized to either a training or validation subgroup. In the training cohort, logistic regression analysis was used to detect factors linked to the diagnosis of bladder cancer. In the model, receiver operating curves were built to predict a diagnosis of bladder cancer among participants. In addition, calibration plots were computed for both subgroups to evaluate the discriminative ability of the model. After analysis, the researchers found significant differences in urinalysis results and demographics among patients with and without a diagnosis of bladder cancer. Patients with bladder cancer had a higher amount of microhematuria (RBC/hpf) on urinalysis (P less than .0001), were more likely previous or current smokers (P = .001), were more often male (68.2% vs. 49.7%; P = .0002), and were older (69.1 vs. 58.2 years; P less than .0001).

With respect to the predictive ability of the model, the area under the curve (AUC) in the training and validation set was 0.79 (95% confidence interval, 0.75-0.83) and 0.74 (95% CI, 0.67-0.80), respectively.

In addition, calibration plots demonstrated that the tool was able to predict the risk of bladder cancer diagnosis for patients with a probability of 0.3 or below.

“These results indicate that the model works best for a range of probabilities of (0-0.30), which is the vast majority of patients clinically and in our data,” the researchers explained.

The team acknowledged that characterizing risk beyond these levels should be done with caution given poor calibration beyond this threshold.

“External validation [of the model] and continued evolution of risk stratification models are needed,” they concluded.

The study was funded by the National Institutes of Health and the American Association of Medical Colleges. The authors reported having no conflicts of interest.

SOURCE: Matulewicz RS et al. Urol Oncol. 2020 Jan 14. doi: 10.1016/j.urolonc.2019.12.010.