The complexity of diagnosing rare disease: An organizing framework for outcomes research and health economics based on real-world evidence

Abstract

To facilitate robust economic analyses of clinical exome and genome sequencing, this study was taken up with the objective of establishing a framework for organizing diagnostic testing trajectories for patients with rare disease. Methods: We collected diagnostic investigations-related data before exome sequencing from the medical records of 228 cases. Medical geneticist experts participated in a consensus building process to develop the SOLVE Framework for organizing the complex range of observed tests. Experts categorized tests as indicator or nonindicator tests on the basis of their specificity for diagnosing rare diseases. Face validity was assessed using case vignettes. Results: Most cases had symptom onset at birth (42.5%) or during childhood (43.4%) and had intellectual disability (73.3%). On average, the time spent seeking a diagnosis before sequencing was 1989 days (SD = 2137) and included 16 tests (SD = 14). Agreement across experts on test categories ranged from 83% to 96%. The SOLVE Framework comprised observed tests, including 186 indicator and 39 nonindicator tests across cytogenetic/molecular, biochemical, imaging, electrical, and pathology test categories. Conclusion: Real-world diagnostic testing data can be ascertained and organized to reflect the complexity of the journey of the patients with rare diseases. SOLVE Framework will improve the accuracy and certainty associated with value-based assessments of genomic sequencing.

Real-world data: a brief review of the methods, applications, challenges and opportunities

“…Controlling for selection bias and confounding is key to the validity of this approach because of the lack of randomization and potentially unrecognized baseline differences, and the control group needs to be comparable with the treated group. RWD also provide a great opportunity to study rare events given the data voluminousness [ 70 – 72 ]. These studies also highlight the need for improving the RWD data quality, developing surrogate endpoints, and standardizing data collection for outcome measures in registries.…”

Positioning whole exome sequencing in the diagnostic pathway for rare disease to optimise utility: a protocol for an observational cohort study and an economic evaluation

“…To organise these pathways, we engaged in an expert-driven consensus process. 31 Using professional guidelines related to diagnostic algorithms for rare disease diagnosis as a reference point, [32][33][34] we asked three medical geneticist coinvestigators to assist with developing a framework for categorising tests recorded in our dataset. Categorising tests as indicator and non-indicators tests, we established the SOLVE Framework for organising pre-WES diagnostic pathways.…”

“…Indicator tests are typically higher cost, potentially invasive, less accessible and ordered/interpreted by a subspecialist and non-indicator tests are typically lower cost, non-invasive, locally accessible and ordered/interpreted by a generalist. 31 Guided Open access by this framework, we will use frequency counts and descriptive statistics to summarise the number, type and cost of diagnostic tests per person in each type of diagnostic pathway and the time to diagnosis for each pathway. Diagnostic pathways will serve as comparator groups for the economic evaluation (aim 5).…”

“…The time of each event is determined by the presence of an indicator test. Informed by our expert-driven consensus process, 31 patients will be grouped according to the number of events observed (ie, 1, 2, 3 or 4+ events). The number of events and the resource utilisation and costs incurred for each time period will be informed by observed data; the model will draw probabilistically from the observed distributions for each subgroup.…”

“…Based on diagnostic investigations captured in the pre-WES period, patients will be assigned to groups that reflect the complexity of their diagnostic pathway. To organise these pathways, we engaged in an expert-driven consensus process 31. Using professional guidelines related to diagnostic algorithms for rare disease diagnosis as a reference point,32–34 we asked three medical geneticist coinvestigators to assist with developing a framework for categorising tests recorded in our dataset.…”