Budgetary Impact of Including the Urinary Genomic Marker Cxbladder Detect in the Evaluation of Microhematuria Patients

You have accessUrology PracticeBusiness1 Jan 2024Budgetary Impact of Including the Urinary Genomic Marker Cxbladder Detect in the Evaluation of Microhematuria PatientsThis article is commented on by the following:Editorial Comment Mark D. Tyson, Robert Abouassaly, Adri Durant, Antonia Bosworth Smith, Kim Seemann, and Daniel A. Shoskes Mark D. TysonMark D. Tyson *Corresponding Author: Mark D. Tyson, MD, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, AZ 85054 ( E-mail Address: [email protected] Mayo Clinic Arizona, Phoenix, Arizona , Robert AbouassalyRobert Abouassaly Cleveland Clinic, Cleveland, Ohio , Adri DurantAdri Durant https://orcid.org/0000-0002-3065-5628 Mayo Clinic Arizona, Phoenix, Arizona , Antonia Bosworth SmithAntonia Bosworth Smith Coreva Scientific GmbH & Co.KG, Königswinter, North Rhine-Westphalia, Germany , Kim SeemannKim Seemann Coreva Scientific GmbH & Co.KG, Königswinter, North Rhine-Westphalia, Germany , and Daniel A. ShoskesDaniel A. Shoskes Pacific Edge Diagnostics USA, Hummelstown, Pennsylvania View All Author Informationhttps://doi.org/10.1097/UPJ.0000000000000489AboutAbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Abstract Introduction: Current AUA guidelines mandate a risk-stratified approach for the evaluation of microhematuria. Urine genomic tests with high negative predictive value could further reduce unnecessary diagnostic testing and morbidity, but the economic impact is unknown. This study modeled the financial impact of Cxbladder Detect on microhematuria evaluations. Methods: A decision tree analysis was constructed by Coreva Scientific comparing 1-year costs of the standard microhematuria evaluation using the AUA guidelines vs an algorithm incorporating Cxbladder Detect. Cxbladder Detect–positive patients had cystoscopy and imaging, whereas patients with negative tests were reevaluated in 6 months. Patients with positive diagnostic testing underwent cystoscopy, and positive cystoscopies led to transurethral resection of bladder tumor. Test performance was based on published literature, and costs were based on Medicare allowable fees. Results: Using the decision tree model, the average savings of using Cxbladder Detect was $559 compared with the standard of care, with an average reduction of 0.38 procedures per patient. Probabilistic analysis showed statistical significance with a median reduction in the total cost of $498 per patient (95% CrI [−1356, −2]) and a significant median reduction in diagnostic procedures per patient of 0.36 (95% CrI [−0.52, −0.16]) without impact on the number of cancers diagnosed. Conclusions: This model-based study demonstrates the potential economic value of using a Cxbladder-driven protocol for microhematuria evaluations. Hematuria is a common clinical presentation that raises concerns for underlying urological malignancies, most commonly bladder cancer. Prompt and accurate diagnosis is crucial for timely treatment and improving patient outcomes. The AUA reports that approximately 3% of microhematuria patients are diagnosed with genitourinary malignancies.1 Current diagnostic practices often involve invasive procedures, such as cystoscopy, which can be uncomfortable and lead to adverse side effects, and CT urogram with radiation exposure and contrast risks. Moreover, following published guidelines may result in unnecessary investigations, leading to additional financial burden for patients and health insurance payers.2 Thus, there is a pressing need for noninvasive and cost-effective diagnostic tools to optimize the risk stratification of microhematuria patients and improve the efficiency of diagnostic pathways. Cxbladder Detect, a noninvasive genomic urinary biomarker test, has shown promise as a valuable diagnostic tool for improving the accuracy of bladder cancer diagnosis and reducing unnecessary diagnostic procedures.3 Utilizing a proprietary algorithm based on 5 mRNA biomarkers, Cxbladder Detect provides a risk score to guide clinical decision-making regarding the need for cystoscopy in hematuria patients.4 However, to implement Cxbladder Detect effectively into the diagnostic pathway, evaluating its budgetary and clinical implications is essential for supporting informed clinical decision-making and enhancing resource allocation in urology practices. In this context, our study aims to address this gap in knowledge by developing a robust cost-consequence analysis for Cxbladder Detect in the United States health care setting. By comparing the cost-effectiveness and diagnostic efficiency of Cxbladder Detect to the current AUA Microhematuria Guidelines standard of care, this research will provide valuable insights into its potential budgetary implications for health insurance payers while maintaining the same level of care for patients. We hypothesize that increasing the pool of low-risk patients who can avoid cystoscopy and imaging at the initial visit will have a favorable budgetary impact despite the cost of the genomic test and not lead to more missed tumors. The findings from this study will offer urologists, health care policymakers, and payers crucial information for informed decision-making, supporting the rational incorporation of Cxbladder Detect into the diagnostic pathway for microhematuria patients. Materials and Methods To evaluate the financial implications of integrating Cxbladder Detect into the existing care pathway for microhematuria patients, we conducted a cost-consequence analysis from a health insurance perspective in the United States. The model was developed in Microsoft Excel, adhering to ISPOR’s principles of good health economic modeling practice.5 The analysis compared the implementation of Cxbladder Detect (intervention) with the current standard of care (comparator) as per the AUA guidelines over a 1-year time horizon.1 The model population was comprised of patients presenting with microhematuria at a urologist's office in the United States based on the AUA definition of ≥3 red blood cells per high-power field on microscopic evaluation of a urine specimen (Figure 1).1 Figure 1. Decision tree for microhematuria evaluation. Schematic representing 2 diagnostic pathways for microhematuria evaluation: the standard of care and the Cxbladder Detect–incorporated approach. CT indicates computed tomography; TURBT, transurethral resection of bladder tumor. Standard of Care Following physical exam and clinical history assessment, patients were stratified based on their risk level into high, intermediate, or low risk. All high-risk patients received a CT urogram followed by cystoscopy in a serial diagnostic setting, subject to clinician evaluation; identifying a tumor led to transurethral resection of the bladder tumor (TURBT). If cystoscopy results were negative, patients were directed to surveillance, irrespective of CT urogram findings. In cases where a negative cystoscopy contradicted a false positive CT urogram (eg, suspicious bladder thickening found to be trabeculation on cystoscopy), the attending physician's judgment (clinical insight hereafter) was conservatively modeled to exclude 67% of these cases from TURBT. Intermediate-risk patients underwent similar serial diagnostic tests consisting of renal ultrasonography, cystoscopy, and clinician assessment of results. Negative findings in this category resulted in surveillance urinalysis at 6 months. Low-risk patients were scheduled for surveillance urinalysis in 6 months. However, the model accommodates a 5% subset of low-risk patients who may opt for the complete workup typically prescribed for intermediate-risk patients. Should urinalysis reveal positive signs for hematuria at the 6-month follow-up, the patient would undergo the same workup as intermediate-risk patients; otherwise, a surveillance urinalysis would be scheduled in 12 months. Cxbladder Detect In the Cxbladder Detect arm, all patients underwent Cxbladder Detect testing before performing any other workup. True or false positives from this test would receive the same workup as high-risk patients in the standard of care arm (cystoscopy and CT urogram). True or false negatives would be scheduled for a surveillance urinalysis in 6 months; if positive for hematuria, they would undergo high-risk patient evaluation; otherwise, another surveillance urinalysis would be scheduled in 12 months. Like the standard of care arm, 5% of patients negative at Cxbladder Detect opted for the complete intermediate-risk workup. Model Input Data Model input data, including population, clinical details, diagnostic test efficacies, and cost inputs, along with their sources, are consolidated in Table 1.1,4,6-11 Notably, the sensitivity and specificity of white light cystoscopy were determined in relation to enhanced cystoscopy techniques.6 The sensitivity and specificity of urinalysis for detecting or ruling out hematuria were assumed to be 100%. Cost inputs were based on Medicare allowable 2022 rates and may vary by patient insurance and location. The relative risks for high-, intermediate-, and low-risk patients were derived from the respective prevalence data from the literature (Table 1). Table 1. Summary of Model Inputs Parameter Value (variance) Distribution Reference Population with microhematuria High-risk patients 83.60% Beta 7 Intermediate-risk patients 11.80% Beta 7 Low-risk patients 4.60% Beta 7 Prevalence of genitourinary malignancies 3.00% Beta 1 Relative risk of bladder cancer Log-normal 7 High risk 6.30 Intermediate risk 1.00 Low risk 0.40 Persistent hematuria (at 6 mo) 16.2% Beta 8 Diagnostic accuracy Cxbladder Detect sensitivity 0.82 Normal 4 Cxbladder Detect specificity 0.94 Normal 4 White light cystoscopy sensitivity 0.71 (0.66, 0.76) Normal 6 White light cystoscopy specificity 0.71 (0.57, 0.81) Normal 6 Microscopy urinalysis sensitivity for blood 1.00 Normal Assumption Microscopic urinalysis specificity for blood 1.00 Normal Assumption CT urogram sensitivity 0.86 Normal 9 CT urogram specificity 0.92 Normal 9 Renal ultrasound sensitivity 0.51 (0.422, 0.587) Normal 10 Renal ultrasound specificity 0.99 (0.989, 0.996) Normal 10 Urine cytology sensitivity 0.42 (0.38, 0.45) Normal 11 Urine cytology specificity 0.96 (0.95, 0.97) Normal 11 Clinical insight 0.67 Normal Assumption Costs Cxbladder Detect $760.00 Gamma CPT 0012M White light cystoscopy $550.87 Gamma CPT 52000 Urinalysis $3.17 Gamma CPT 81001 Urine cytology $60.07 Gamma CPT 88112 CT urogram $457.14 Gamma CPT 74178 Renal ultrasound $146.05 Gamma CPT 76770 Abbreviations: CPT, Current Procedural Terminology; CT, computed tomography. Model Outcomes The main model outcomes were the total costs per patient, the average number of diagnostics per patient, the number of correctly diagnosed bladder cancer cases, and the number of TURBTs performed. Sensitivity Analysis A 2-pronged sensitivity analysis addressed inherent uncertainty in model parameters and validated results. Each input parameter was individually varied by ±10% to evaluate the impact of each parameter on the model outcomes in a deterministic, one-way sensitivity analysis. In addition, a Monte Carlo probabilistic sensitivity analysis was implemented over 10,000 randomly seeded iterations and reported as a 95% credible interval (CrI), which is the Bayesian counterpart of the 95% confidence interval. Results Compared with standard of care, the use of Cxbladder Detect showed an average reduction in costs per patient of $559.21, driven by savings in the costs of TURBT, CT urogram, cystoscopy, and ultrasound which counterbalanced increased costs of the Cxbladder Detect assay and more urinalysis tests. The average reduction in number of diagnostic procedures per patient was 0.38, and there was no difference in number of tumors found. Using the Bayesian probabilistic model, Cxbladder Detect was associated with a median reduction in the total cost of $498 per patient (95% CrI [−1356, −2]; Table 2). Refined diagnostic efficiency emerged, as manifested by a significant median reduction in the number of diagnostic procedures per patient of 0.36 (95% CrI [−0.52, −0.16]). No substantial variation was discerned in the correct identification of bladder cancer cases (95% CrI [−0.51, 0.09]). Table 2. Comparison of Standard of Care vs Cxbladder Detect in the Evaluation of Microhematuria Patients Metric Median 95% CrI lower 95% CrI upper Total cost 1000 patients, $ difference −498,324 −1,356,529 −1944 Cost per patient, $ difference −498 −1356 −2 Median diagnostics per patient, No. difference −0.36 −0.52 −0.16 Correct bladder cancer diagnoses, No. difference 0.01 −0.51 0.09 TURBT, No. difference −86 −195 −24 Abbreviations: CrI, credible interval; TURBT, transurethral resection of bladder tumor. The table illustrates the median differences and 95% CrIs for including total cost per 1000 patients, total cost per patient, mean diagnostics per patient, expected bladder cancer cases, correct bladder cancer diagnoses, and TURBT interventions. Negative values indicate reductions associated with the use of Cxbladder Detect. Using the deterministic analysis, varying inputs by 10% showed that the specificity of CT urogram, Cxbladder Detect specificity, urinalysis specificity, cystoscopy specificity, and the costs of both Cxbladder Detect and TURBT were salient determinants of cost saving (Figure 2). Other inputs had a negligible impact on the results. The model was then adjusted to find the minimal input variability of at least 1 variable that would eliminate the cost savings and that threshold was not reached until 38% (Figure 3), with 38% reduction of specificity of Cxbladder Detect, Urinalysis, or 38% increase of specificity for cystoscopy eliminating the average cost savings. Figure 2. Tornado diagram illustrating the sensitivity of the deterministic analysis to variations in input parameters. The diagram shows the impact of a +10% (blue) and −10% (red) change in inputs on cost saving. The values represent the range of effects on the model's outcomes. Other parameters not reported in the diagram have marginal influence (≤1%). Positive percent of change in saving favors Cxbladder Detect over the standard of care. CT indicates computed tomography; TURBT, transurethral resection of bladder tumor. Figure 3. Tornado diagram illustrating the sensitivity of the deterministic analysis to variations in input parameters. The diagram shows the impact of a +38% (blue) and −38% (red) change in inputs on cost saving. The values represent the range of effects on the model's outcomes. Other parameters not reported in the diagram have marginal influence (≤1%). Positive percent of change in saving favors Cxbladder Detect over the standard of care. CT indicates computed tomography; TURBT, transurethral resection of bladder tumor. Discussion Evaluating asymptomatic microscopic hematuria presents a multifaceted challenge in contemporary health care, requiring a balance between efficacious cancer detection and the minimization of undue costs and procedures. The findings of the present study contribute to elucidating the possible benefits of incorporating Cxbladder Detect into the diagnostic pathway. The theoretical savings of this model with an average cost reduction per patient of $559 might possess broad-reaching consequences when extrapolated to a larger population, which even in the worst-case scenario of the Bayesian probabilistic model was budget neutral. From an operational standpoint, the significant decrement in the mean number of diagnostic procedures per patient could expedite diagnoses; facilitate more nuanced resource allocation; foster a patient-centric methodology; and, coupled with the minimization of TURBT interventions, mitigate patient morbidity, aligning with prevailing emphases on patient experience and resource optimization without imposing any additional financial burden. The maintenance of diagnostic precision with Cxbladder Detect ensures that these efficiency gains do not undermine diagnostic integrity, guaranteeing that integration can be pursued without compromising patient safety or care quality. From the point of view of payers and integrated health care systems, a hematuria protocol with Cxbladder Detect can provide value while saving money. For urologists, a reduction in cystoscopies that are normal and TURBT procedures for suspicious lesions that are benign ensures that these procedure blocks can be better utilized on patients with actual pathology, improving patient access. For patients there is the advantage of not having an invasive procedure just to be told you have no disease. For society, eliminating a large number of unnecessary cystoscopies will have a significant environmental impact, both by reducing patient travel to the procedures and their significant carbon footprint, as well as the waste generated from disposables.12 To date, a nascent body of research has underscored the potential of Cxbladder Detect as a noninvasive urine marker test for optimizing the diagnosis of bladder cancer. Raman et al (2021) reported that the Cxbladder suite of tests demonstrated a sensitivity of 92.4% and specificity of 93.8% for detecting high-impact urothelial carcinoma, indicating its robust diagnostic performance relative to other biomarkers.13,14 In a meta-analysis of 6 urinary biomarkers, including Cxbladder, the sensitivities and specificities ranged from 0.66 to 0.97 and 0.58 to 0.83, respectively.15 While no published study has performed a head to head comparison between these urinary biomarkers, the observed cost savings in the current study illuminates Cxbladder Detect potential as a promising tool in the diagnostic pathway for microhematuria patients. Despite the encouraging findings, the limitations and potential threats to the validity of our cost-consequence analysis must be acknowledged. The accuracy of our estimates is contingent upon the quality, representativeness, and regional uniformity of the data sources utilized for parameter estimation. Variability in these factors could introduce uncertainty and limit the generalizability of our results. While both average and median values showed significant cost reduction and were statistically significant as the 95% CrI did not cross the 0 savings line, the lower 95% limit was close to 0 suggesting, at worst, budget neutrality. Nevertheless, this was without capturing indirect cost savings from improved opportunity costs and the costs of managing procedural complications (eg, urinary tract infection post cystoscopy). While our model offers insightful short-term cost projections and posits a direction toward budget-neutral health care improvements, the long-term ramifications of integrating Cxbladder Detect remain to be fully explored. Another consideration when incorporating Cxbladder Detect into microhematuria evaluations is the limitation of the test on identifying asymptomatic benign etiologies. While the current standard guidelines allow for discovery of nonmalignant anatomic abnormalities, adoption of a Cxbladder Detect based algorithm might limit or delay this evaluation. Investigating extended follow-up periods, conducting real-world data validation, and considering the variability in regional health care practices would serve to enhance the robustness and applicability of our findings. Conclusions In conclusion, our budget impact model highlights the potential value of Cxbladder Detect in improving diagnostic efficiency and reducing health care costs for microhematuria patients suspected of having bladder cancer. The model's findings provide a foundation for informed decision-making by health care policymakers and clinicians. Future research efforts should focus on real-world data validation, examination of cost impact beyond the first year, and direct comparisons with other diagnostic technologies to further validate the clinical and economic benefits of Cxbladder Detect. Ultimately, the successful integration of Cxbladder Detect into clinical practice could lead to earlier diagnosis, improved patient outcomes, and enhanced resource allocation, making a substantial contribution to the management of primary hematuria. References 1. . Microhematuria: AUA/SUFU guideline. J Urol. 2020; 204(4):778-786. Link, Google Scholar 2. . A simplified nomogram to assess risk of bladder cancer in patients with a new diagnosis of microscopic hematuria. Urol Oncol. 2020; 38(4):240-246. Crossref, Medline, Google Scholar 3. . Urinary analysis of FGFR3 and TERT gene mutations enhances performance of cxbladder tests and improves patient risk stratification. J Urol. 2023; 209(4):762-772. Link, Google Scholar 4. . A multigene urine test for the detection and stratification of bladder cancer in patients presenting with hematuria. J Urol. 2012; 188(3):741-747. Link, Google Scholar 5. . Budget impact analysis–principles of good practice: report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force. Value Health. 2014; 17(1):5-14. Crossref, Medline, Google Scholar 6. . Performance of narrow band imaging (NBI) and photodynamic diagnosis (PDD) fluorescence imaging compared to white light cystoscopy (WLC) in detecting non-muscle invasive bladder cancer: a systematic review and lesion-level diagnostic meta-analysis. Cancers (Basel). 2021; 13(17):4378. Crossref, Medline, Google Scholar 7. . Evaluation of the new American Urological Association guidelines risk classification for hematuria. J Urol. 2021; 205(5):1387-1393. Link, Google Scholar 8. . Evaluation of hematuria in a large public health care system. Bladder Cancer. 2019; 5(2):119-129. Crossref, Medline, Google Scholar 9. . A glance at imaging bladder cancer. Clin Transl Imaging. 2018; 6(4):257-269. Crossref, Medline, Google Scholar 10. . Can renal and bladder ultrasound replace computerized tomography urogram in patients investigated for microscopic hematuria?. J Urol. 2018; 200(5):973-980. Link, Google Scholar 11. . A review on the accuracy of bladder cancer detection methods. J Cancer. 2019; 10(17):4038-4044. Crossref, Medline, Google Scholar 12. . The carbon footprint of single-use flexible cystoscopes compared with reusable cystoscopes. J Endourol. 2022; 36(11):1460-1464. Crossref, Medline, Google Scholar 13. . The diagnostic performance of Cxbladder Resolve, alone and in combination with other Cxbladder tests, in the identification and priority evaluation of patients at risk for urothelial carcinoma. J Urol. 2021; 206(6):1380-1389. Link, Google Scholar 14. . A holistic comparative analysis of diagnostic tests for urothelial carcinoma: a study of Cxbladder Detect, UroVysion® FISH, NMP22® and cytology based on imputation of multiple datasets. BMC Med Res Methodol. 2015; 15:45. Crossref, Medline, Google Scholar 15. . Utility of urinary biomarkers in primary haematuria: systematic review and meta-analysis. BJUI Compass. 2022; 3(5):334-343. Crossref, Medline, Google Scholar Support: This study was sponsored by Pacific Edge Diagnostics. Conflict of Interest Disclosures: D.A.S. is an employee of Pacific Edge Diagnostics. A.B.S. and K.S. are employees of Coreva Scientific GmbH & Co.KG; Coreva Scientific received consulting fees for the model presented here. M.D.T., R.A., and A.D. have no financial or other conflict of interest related to this study. Ethics Statement: This study was deemed exempt from Institutional Review Board review. Author Contributions: Conception and design: A.B.S., D.A.S., K.S., M.D.T.; Data analysis and interpretation: A.D., A.B.S., D.A.S., K.S., M.D.T.; Data acquisition: A.D.; Critical revision of the manuscript for scientific and factual content: A.B.S., K.S., M.D.T.; Drafting the manuscript: A.D., D.A.S., M.D.T.; Statistical analysis: A.D., A.B.S., D.A.S., K.S.; Supervision: M.D.T.; Data collection: A.B.S., D.A.S., K.S., M.D.T.; Stats interpretation: A.D., A.B.S., D.A.S., K.S., M.D.T.; Helped with conceptualization: A.D., A.B.S., D.A.S., K.S., M.D.T. Presented at the 99th Annual Meeting of the Western Section AUA, October 1 to 5, 2023. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetailsRelated articlesUrology Practice9 Nov 2023Editorial Comment Volume 11 Issue 1 January 2024 Page: 54-60 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.KeywordsCxbladderprimary hematuriaurinary biomarkerbladder cancerMetrics Author Information Mark D. Tyson Mayo Clinic Arizona, Phoenix, Arizona *Corresponding Author: Mark D. Tyson, MD, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, AZ 85054 ( E-mail Address: [email protected] More articles by this author Robert Abouassaly Cleveland Clinic, Cleveland, Ohio More articles by this author Adri Durant Mayo Clinic Arizona, Phoenix, Arizona More articles by this author Antonia Bosworth Smith Coreva Scientific GmbH & Co.KG, Königswinter, North Rhine-Westphalia, Germany More articles by this author Kim Seemann Coreva Scientific GmbH & Co.KG, Königswinter, North Rhine-Westphalia, Germany More articles by this author Daniel A. Shoskes Pacific Edge Diagnostics USA, Hummelstown, Pennsylvania More articles by this author Expand All Support: This study was sponsored by Pacific Edge Diagnostics. Conflict of Interest Disclosures: D.A.S. is an employee of Pacific Edge Diagnostics. A.B.S. and K.S. are employees of Coreva Scientific GmbH & Co.KG; Coreva Scientific received consulting fees for the model presented here. M.D.T., R.A., and A.D. have no financial or other conflict of interest related to this study. Ethics Statement: This study was deemed exempt from Institutional Review Board review. Author Contributions: Conception and design: A.B.S., D.A.S., K.S., M.D.T.; Data analysis and interpretation: A.D., A.B.S., D.A.S., K.S., M.D.T.; Data acquisition: A.D.; Critical revision of the manuscript for scientific and factual content: A.B.S., K.S., M.D.T.; Drafting the manuscript: A.D., D.A.S., M.D.T.; Statistical analysis: A.D., A.B.S., D.A.S., K.S.; Supervision: M.D.T.; Data collection: A.B.S., D.A.S., K.S., M.D.T.; Stats interpretation: A.D., A.B.S., D.A.S., K.S., M.D.T.; Helped with conceptualization: A.D., A.B.S., D.A.S., K.S., M.D.T. Presented at the 99th Annual Meeting of the Western Section AUA, October 1 to 5, 2023. Advertisement Advertisement PDF downloadLoading ...