A simplified nomogram to assess risk of bladder cancer in patients with a new diagnosis of microscopic hematuria

Abstract

Introduction: The vast majority of patients who undergo a diagnostic evaluation for microscopic hematuria (MH) do not have occult bladder cancer. Identifying patients with MH at high risk of harboring bladder cancer can allow for a risk adjusted approach to diagnostic interventions with the goal of safely reducing unnecessary evaluations.

Methods: Patients with a new diagnosis of microhematuria during an 8.5 year period were retrospectively identified. All patients who had a complete MH evaluation were randomized to a training or a validation cohort. Logistic regression analysis was performed in the training cohort to identify factors related to a bladder cancer diagnosis and to develop our model. Receiver operating curves to identify bladder cancer were constructed for the training and validation cohort and tested for their ability to discriminate true cases. A nomogram to predict a bladder cancer diagnosis was created.

Results: In 4,178 patients split into training and validation cohorts, those diagnosed with bladder cancer were shown to be older, have a greater degree of MH (more RBC/hpf), and were former or current smokers. A nomogram created using this model was able to predict risk of a bladder cancer diagnosis with good discrimination (areas under the curve 0.79, 95% CI 0.75-0.83). A cutoff of 0.01 probability demonstrated a sensitivity of 99.1% and a negative predictive value of 99.7%.

Conclusion: A nomogram can accurately predict the risk of bladder cancer diagnosed during the evaluation of MH and can potentially be used avoid a significant number of work ups in those at the lowest risk.

Keywords: Bladder cancer; Cystoscopy; Hematuria; Microhematuria.

Figure 1-

Receiver operating curves (ROC) for training and validation models predicting bladder cancer. Solid line = training cohort (n=2126 patients with 107 malignancies) with an AUC=0.79, 95% CI 0.75–0.83. Dashed line = validation cohort (n=2052 patients with 84 malignancies) with an AUC=0.74, 95% CI 0.67–0.80.

Figure 2

Predictive nomogram. Draw a line perpendicular from the corresponding category of each risk factor until it reaches the bottom line labeled “Score.” Total the number of points across all risk factors and locate this total on the line labelled ‘Total Score’ to calculate the predicted probability of having bladder cancer diagnosed during an evaluation with those risk factors – ‘BCa (Bladder cancer) probability.