Cost‐Effectiveness Model for Evaluating New Diagnostic Tests in the Evaluation of Patients With Inflammatory Arthritis at Risk of Having Rheumatoid Arthritis
Abstract:Objective. New opportunities have emerged for early diagnosis with the arrival of new technologies that assess the impact of genomics, proteomics, metabolomics, and cytomics on rheumatoid arthritis (RA) risk. This early health technology assessment study assesses the short-term cost effectiveness of 4 add-on diagnostic tests in early inflammatory arthritis patients at risk of RA. Methods. We modeled 4 diagnostic add-on tests to the American College of Rheumatology/European League Against Rheumatism 2010 RA cla… Show more
“…Figure 1 shows the decision model comparing the current diagnostic test strategy with the new add-on diagnostic test strategy for intermediate-risk patients (described subsequently). This 5-year cost-effectiveness model is an extension of our 1-year model published elsewhere [ 33 ]. Fig.…”
BackgroundThere is a lack of information about the sensitivity, specificity and costs new diagnostic tests should have to improve early diagnosis of rheumatoid arthritis (RA). Our objective was to explore the early cost-effectiveness of various new diagnostic test strategies in the workup of patients with inflammatory arthritis (IA) at risk of having RA.MethodsA decision tree followed by a patient-level state transition model, using data from published literature, cohorts and trials, was used to evaluate diagnostic test strategies. Alternative tests were assessed as add-on to or replacement of the ACR/EULAR 2010 RA classification criteria for all patients and for intermediate-risk patients. Tests included B-cell gene expression (sensitivity 0.60, specificity 0.90, costs €150), MRI (sensitivity 0.90, specificity 0.60, costs €756), IL-6 serum level (sensitivity 0.70, specificity 0.53, costs €50) and genetic assay (sensitivity 0.40, specificity 0.85, costs €750). Patients with IA at risk of RA were followed for 5 years using a societal perspective. Guideline treatment was assumed using tight controlled treatment based on DAS28; if patients had a DAS28 >3.2 at 12 months or later patients could be eligible for starting biological drugs. The outcome was expressed in incremental cost-effectiveness ratios (€2014 per quality-adjusted life year (QALY) gained) and headroom.ResultsThe B-cell test was the least expensive strategy when used as an add-on and as replacement in intermediate-risk patients, making it the dominant strategy, as it has better health outcomes and lower costs. As add-on for all patients, the B-cell test was also the most cost-effective test strategy. When using a willingness-to-pay threshold of €20,000 per QALY gained, the IL-6 and MRI strategies were not cost-effective, except as replacement. A genetic assay was not cost-effective in any strategy. Probabilistic sensitivity analysis revealed that the B-cell test was consistently superior in all strategies. When performing univariate sensitivity analysis for intermediate-risk patients, specificity and DAS28 in the B-cell add-on strategy, and DAS28 and sensitivity in the MRI add-on strategy had the largest impact on the cost-effectiveness.ConclusionsThis early cost-effectiveness analysis indicated that new tests to diagnose RA are most likely to be cost-effective when the tests are used as an add-on in intermediate-risk patients, and have high specificity, and the test costs should not be higher than €200–€300.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-016-1020-3) contains supplementary material, which is available to authorized users.
“…Figure 1 shows the decision model comparing the current diagnostic test strategy with the new add-on diagnostic test strategy for intermediate-risk patients (described subsequently). This 5-year cost-effectiveness model is an extension of our 1-year model published elsewhere [ 33 ]. Fig.…”
BackgroundThere is a lack of information about the sensitivity, specificity and costs new diagnostic tests should have to improve early diagnosis of rheumatoid arthritis (RA). Our objective was to explore the early cost-effectiveness of various new diagnostic test strategies in the workup of patients with inflammatory arthritis (IA) at risk of having RA.MethodsA decision tree followed by a patient-level state transition model, using data from published literature, cohorts and trials, was used to evaluate diagnostic test strategies. Alternative tests were assessed as add-on to or replacement of the ACR/EULAR 2010 RA classification criteria for all patients and for intermediate-risk patients. Tests included B-cell gene expression (sensitivity 0.60, specificity 0.90, costs €150), MRI (sensitivity 0.90, specificity 0.60, costs €756), IL-6 serum level (sensitivity 0.70, specificity 0.53, costs €50) and genetic assay (sensitivity 0.40, specificity 0.85, costs €750). Patients with IA at risk of RA were followed for 5 years using a societal perspective. Guideline treatment was assumed using tight controlled treatment based on DAS28; if patients had a DAS28 >3.2 at 12 months or later patients could be eligible for starting biological drugs. The outcome was expressed in incremental cost-effectiveness ratios (€2014 per quality-adjusted life year (QALY) gained) and headroom.ResultsThe B-cell test was the least expensive strategy when used as an add-on and as replacement in intermediate-risk patients, making it the dominant strategy, as it has better health outcomes and lower costs. As add-on for all patients, the B-cell test was also the most cost-effective test strategy. When using a willingness-to-pay threshold of €20,000 per QALY gained, the IL-6 and MRI strategies were not cost-effective, except as replacement. A genetic assay was not cost-effective in any strategy. Probabilistic sensitivity analysis revealed that the B-cell test was consistently superior in all strategies. When performing univariate sensitivity analysis for intermediate-risk patients, specificity and DAS28 in the B-cell add-on strategy, and DAS28 and sensitivity in the MRI add-on strategy had the largest impact on the cost-effectiveness.ConclusionsThis early cost-effectiveness analysis indicated that new tests to diagnose RA are most likely to be cost-effective when the tests are used as an add-on in intermediate-risk patients, and have high specificity, and the test costs should not be higher than €200–€300.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-016-1020-3) contains supplementary material, which is available to authorized users.
“…2016 Jul;68(7):927–35. [35]16Kip MMA, Steuten LMG, Koffijberg H, IJzerman MJ, Kusters R. Using expert elicitation to estimate the potential impact of improved diagnostic performance of laboratory tests: a case study on rapid discharge of suspected non-ST elevation myocardial infarction patients. J Eval Clin Pract.…”
Section: Appendixmentioning
confidence: 99%
“…2016 Jul;68(7):927–35. [35]36Huygens SA, Rutten-van Mölken MPMH, Bekkers JA, Bogers AJJC, Bouten CVC, Chamuleau SAJ, et al Conceptual model for early health technology assessment of current and novel heart valve interventions. Open Heart.…”
Early health technology assessment is increasingly being used to support health economic evidence development during early stages of clinical research. Such early models can be used to inform research and development about the design and management of new medical technologies to mitigate the risks, perceived by industry and the public sector, associated with market access and reimbursement. Over the past 25 years it has been suggested that health economic evaluation in the early stages may benefit the development and diffusion of medical products. Early health technology assessment has been suggested in the context of iterative economic evaluation alongside phase I and II clinical research to inform clinical trial design, market access, and pricing. In addition, performing early health technology assessment was also proposed at an even earlier stage for managing technology portfolios. This scoping review suggests a generally accepted definition of early health technology assessment to be “all methods used to inform industry and other stakeholders about the potential value of new medical products in development, including methods to quantify and manage uncertainty”. The present review also aimed to identify recent published empirical studies employing an early-stage assessment of a medical product. With most included studies carried out to support a market launch, the dominant methodology was early health economic modeling. Further methodological development is required, in particular, by combining systems engineering and health economics to manage uncertainty in medical product portfolios.
“…Imaging modalities have been investigated to assist in early diagnosis for IA, including ultrasound and magnetic resonance imaging. Studies to date have yielded mixed results, without sufficient evidence to support the incorporation of such measures in routine assessment, particularly when factors of time, access and cost are taken into account 27–29 …”
Background: Arthritis is a common condition, which frequently involves the hands.Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis.Aim: To develop and test a screening tool combining an image of a patient's hands, a short series of questions and a single examination technique to determine the most likely diagnosis in a patient presenting with hand arthritis. Machine learning techniques were used to develop separate algorithms for each component, which were combined to produce a diagnosis.Methods: A total of 280 consecutive new patients presenting to a rheumatology practice with hand arthritis were enrolled. Each patient completed a nine-part questionnaire, had photographs taken of each hand and had a single examination result recorded. The rheumatologist diagnosis was recorded following a 45-min consultation. The photograph algorithm was developed from 1000 previous hand images and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the rheumatologist.
Results:The combined algorithms in the present study were able to predict inflammatory arthritis with an accuracy, precision, recall and specificity of 96.8%, 97.2%, 98.6% and 90.5% respectively. Similar results were found when inflammatory arthritis was subclassified into rheumatoid arthritis and psoriatic arthritis. The corresponding figures for osteoarthritis were 79.6%, 85.9%, 61.9% and 92.6%.
Conclusion:The present study demonstrates a novel application combining image processing and a patient questionnaire with applied machine-learning methods to facilitate the diagnosis of patients presenting with hand arthritis. Preliminary results are encouraging for the application of such techniques in clinical practice.
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