A segregation index combining phenotypic (clinical characteristics) and genotypic (gene expression) biomarkers from a urine sample to triage out patients presenting with hematuria who have a low probability of urothelial carcinoma

Laimonis Kavalieris, Paul J O'Sullivan, James M Suttie, Brent K Pownall, Peter J Gilling, Christophe Chemasle, David G Darling, Laimonis Kavalieris, Paul J O'Sullivan, James M Suttie, Brent K Pownall, Peter J Gilling, Christophe Chemasle, David G Darling

Abstract

Background: Hematuria can be symptomatic of urothelial carcinoma (UC) and ruling out patients with benign causes during primary evaluation is challenging. Patients with hematuria undergoing urological work-ups place significant clinical and financial burdens on healthcare systems. Current clinical evaluation involves processes that individually lack the sensitivity for accurate determination of UC. Algorithms and nomograms combining genotypic and phenotypic variables have largely focused on cancer detection and failed to improve performance. This study aimed to develop and validate a model incorporating both genotypic and phenotypic variables with high sensitivity and a high negative predictive value (NPV) combined to triage out patients with hematuria who have a low probability of having UC and may not require urological work-up.

Methods: Expression of IGFBP5, HOXA13, MDK, CDK1 and CXCR2 genes in a voided urine sample (genotypic) and age, gender, frequency of macrohematuria and smoking history (phenotypic) data were collected from 587 patients with macrohematuria. Logistic regression was used to develop predictive models for UC. A combined genotypic-phenotypic model (G + P INDEX) was compared with genotypic (G INDEX) and phenotypic (P INDEX) models. Area under receiver operating characteristic curves (AUC) defined the performance of each INDEX: high sensitivity, NPV >0.97 and a high test-negative rate was considered optimal for triaging out patients. The robustness of the G + P INDEX was tested in 40 microhematuria patients without UC.

Results: The G + P INDEX offered a bias-corrected AUC of 0.86 compared with 0.61 and 0.83, for the P and G INDEXs respectively. When the test-negative rate was 0.4, the G + P INDEX (sensitivity = 0.95; NPV = 0.98) offered improved performance compared with the G INDEX (sensitivity = 0.86; NPV = 0.96). 80% of patients with microhematuria who did not have UC were correctly triaged out using the G + P INDEX, therefore not requiring a full urological work-up.

Conclusion: The adoption of G + P INDEX enables a significant change in clinical utility. G + P INDEX can be used to segregate hematuria patients with a low probability of UC with a high degree of confidence in the primary evaluation. Triaging out low-probability patients early significantly reduces the need for expensive and invasive work-ups, thereby lowering diagnosis-related adverse events and costs.

Figures

Figure 1
Figure 1
Standards for Reporting of Diagnostic Accuracy (STARD) diagram for patient recruitment and enrolment. Legend: (A) Patients with macrohematuria across all three cohorts in this study; B) patients with microhematuria included in this study.
Figure 2
Figure 2
ROC curves representing the three classification models. Legend: P INDEX (dotted), G INDEX (dashed) and G + P INDEX (solid).
Figure 3
Figure 3
NPV versus proportion of patients with hematuria testing negative according to the three classification models. Legend: P INDEX (dotted), G INDEX (dashed) and G + P INDEX (solid) models. Note that the curve for the P INDEX is discrete as only 16 possible combinations of phenotypic variables are possible. The only possible values taken are indicated by the prominent points on this curve.

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