Costs of the diagnostic odyssey in children with inherited leukodystrophies

The natural history and burden of illness of metachromatic leukodystrophy: a systematic literature review

BACKGROUND

Metachromatic leukodystrophy (MLD; OMIM 250100 and 249900) is a rare lysosomal storage disease caused by deficient arylsulfatase A activity, leading to accumulation of sulfatides in the nervous system. This systematic literature review aimed to explore the effect of MLD on the lives of patients.

METHODS

The Ovid platform was used to search Embase, MEDLINE, and the Cochrane Library for articles related to the natural history, clinical outcomes, and burden of illness of MLD; congress and hand searches were performed using 'metachromatic leukodystrophy' as a keyword. Of the 531 publications identified, 120 were included for data extraction following screening. A subset of findings from studies relating to MLD natural history and burden of illness (n = 108) are presented here.

RESULTS

The mean age at symptom onset was generally 16-18 months for late-infantile MLD and 6-10 years for juvenile MLD. Age at diagnosis and time to diagnosis varied widely. Typically, patients with late-infantile MLD presented predominantly with motor symptoms and developmental delay; patients with juvenile MLD presented with motor, cognitive, and behavioral symptoms; and patients with adult MLD presented with cognitive symptoms and psychiatric and mood disorders. Patients with late-infantile MLD had more rapid decline of motor function over time and lower survival than patients with juvenile MLD. Commonly reported comorbidities/complications included ataxia, epilepsy, gallbladder abnormalities, incontinence, neuropathy, and seizures.

CONCLUSIONS

Epidemiology of MLD by geographic regions, quantitative cognitive data, data on the differences between early- and late-juvenile MLD, and humanistic or economic outcomes were limited. Further studies on clinical, humanistic (i.e., quality of life), and economic outcomes are needed to help inform healthcare decisions for patients with MLD.

Attaining Diagnostic Excellence: How the Structure and Function of a Rare Disease Service Contribute to Ending the Diagnostic Odyssey

Patients with rare or otherwise undiagnosed disorders frequently find themselves on a diagnostic odyssey, the often-prolonged journey toward diagnosis that can be characterized by significant physical, emotional, and financial hardship, as well as by diagnostic errors and delays. The wider availability of clinical exome sequencing has helped end many diagnostic odysseys, though diagnostic success rates of around 35% for exome sequencing leave many patients undiagnosed. Diagnostic yields can be improved via the implementation of advanced genetic testing modalities, though both these modalities and exome sequencing perform significantly better when paired with high-quality phenotypic data. Diagnostic centers of excellence can improve outcomes for patients on a diagnostic odyssey by providing a process and environment that address shortfalls in diagnostic access while providing high-quality phenotyping. Features of successful undiagnosed and rare disease evaluation teams are discussed and an illustrative case is provided.

Approaches to diagnosis for individuals with a suspected inherited white matter disorder

Leukodystrophies are heritable disorders with white matter abnormalities observed on central nervous system magnetic resonance imaging. Pediatric leukodystrophies have long been known for their classically high, "unsolved" rate. Indeed, these disorders provide a diagnostic dilemma for many clinicians as over 100 genetic disorders alone may present with white matter abnormalities, with this figure not taking into account the substantial number of infectious agents, toxicities, and acquired disorders that may affect the white matter of the brain. Achieving a diagnosis may be the single most important step in the clinical course of a leukodystrophy-affected individual, with important implications for care and quality of life. For certain disorders, prompt recognition can direct therapeutic intervention with significant implications and requires urgent recognition. In this review, we cover newborn screening efforts, standard-of-care testing methodologies, and next generation sequencing approaches that continue to change the landscape of leukodystrophy diagnosis. Early studies have shown that next generation sequencing approaches, particularly exome and now genome sequencing have proven to be powerful in helping resolve many cases that were refractory to a single gene or linkage analysis approach. In addition, other methods are required for cases that remain persistently unsolved after next generation sequencing methods have been used. In the past more than half of affected individuals never achieved an etiologic diagnosis, and when they did, the reported times to diagnosis were >5 years although molecular testing has allowed this to be reduced to closer to 16 months. For affected families, next generation sequencing technologies have finally provided a way to fill gaps in diagnosis.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.

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

INTRODUCTION

Despite the superior diagnostic performance of exome and genome sequencing compared with conventional genetic tests, evidence gaps related to clinical utility and cost effectiveness have limited their availability in routine clinical practice in many jurisdictions. To inform adoption and reimbursement policy, this protocol provides a chain of evidence approach to determining the diagnostic utility, clinical utility and cost-effectiveness of whole exome sequencing (WES) from seven medical genetic centres in two Canadian provinces.

METHODS AND ANALYSIS

Using a multicentre observational cohort design, we will extract data specific to the pre-WES diagnostic pathway and 1-year post-WES medical management from electronic medical records for 650 patients with rare disease of suspected genetic aetiology who receive WES. The date from the clinical record will be linked to provincial administrative health database to capture healthcare resource use and estimate costs. Our analysis will: (1) define and describe diagnostic testing pathways that occur prior to WES among patients with rare disease, (2) determine the diagnostic utility of WES, characterised as the proportion of patients for whom causative DNA variants are identified, (3) determine the clinical utility of WES, characterised as a change in medical management triggered by WES results, (4) determine the pattern and cost of health service utilisation prior and 1 year following WES among patients who receive a diagnosis, do not receive a diagnosis, or receive an uncertain diagnosis and (5) estimate the cost-effectiveness of WES compared with conventional diagnostic testing pathways, measured by the incremental cost per additional patient diagnosed by WES using simulation modelling.

ETHICS AND DISSEMINATION

This protocol was approved by Clinical Trials Ontario (CTO-1577) and research ethics boards at the University of Calgary (REB18-0744 and REB20-1449) and University of Alberta (Pro0009156). Findings will be disseminated through academic publications and policy reports.

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

PURPOSE

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.

Cost of Elective Labor Induction Compared With Expectant Management in Nulliparous Women

OBJECTIVE

To compare the actual health-system cost of elective labor induction at 39 weeks of gestation with expectant management.

METHODS

This was an economic analysis of patients enrolled in the five Utah hospitals participating in a multicenter randomized trial of elective labor induction at 39 weeks of gestation compared with expectant management in low-risk nulliparous women. The entire trial enrolled more than 6,000 patients. For this subset, 1,201 had cost data available. The primary outcome was relative direct health care costs of maternal and neonatal care from a health system perspective. Secondary outcomes included the costs of each phase of maternal and neonatal care. Direct health system costs of maternal and neonatal care were measured using advanced costing analytics from the time of randomization at 38 weeks of gestation until exit from the study up to 8 weeks postpartum. Costs in each randomization arm were compared using generalized linear models and reported as the relative cost of induction compared with expectant management. With a fixed sample size, we had adequate power to detect a 7.3% or greater difference in overall costs.

RESULTS

The total cost of elective induction was no different than expectant management (mean difference +4.7%; 95% CI -2.1% to +12.0%; P=.18). Maternal outpatient antenatal care costs were 47.0% lower in the induction arm (95% CI -58.3% to -32.6%; P<.001). Maternal inpatient intrapartum and delivery care costs, conversely, were 16.9% higher among women undergoing labor induction (95% CI +5.5% to +29.5%; P=.003). Maternal inpatient postpartum care, maternal outpatient care after discharge, neonatal hospital care, and neonatal care after discharge did not differ between arms.

CONCLUSION

Total costs of elective labor induction and expectant management did not differ significantly. These results challenge the assumption that elective induction of labor leads to significant cost escalation.

Development of a Social Network for People Without a Diagnosis (RarePairs): Evaluation Study

Background

Diagnostic delay in rare disease (RD) is common, occasionally lasting up to more than 20 years. In attempting to reduce it, diagnostic support tools have been studied extensively. However, social platforms have not yet been used for systematic diagnostic support. This paper illustrates the development and prototypic application of a social network using scientifically developed questions to match individuals without a diagnosis.

Objective

The study aimed to outline, create, and evaluate a prototype tool (a social network platform named RarePairs), helping patients with undiagnosed RDs to find individuals with similar symptoms. The prototype includes a matching algorithm, bringing together individuals with similar disease burden in the lead-up to diagnosis.

Methods

We divided our project into 4 phases. In phase 1, we used known data and findings in the literature to understand and specify the context of use. In phase 2, we specified the user requirements. In phase 3, we designed a prototype based on the results of phases 1 and 2, as well as incorporating a state-of-the-art questionnaire with 53 items for recognizing an RD. Lastly, we evaluated this prototype with a data set of 973 questionnaires from individuals suffering from different RDs using 24 distance calculating methods.

Results

Based on a step-by-step construction process, the digital patient platform prototype, RarePairs, was developed. In order to match individuals with similar experiences, it uses answer patterns generated by a specifically designed questionnaire (Q53). A total of 973 questionnaires answered by patients with RDs were used to construct and test an artificial intelligence (AI) algorithm like the k-nearest neighbor search. With this, we found matches for every single one of the 973 records. The cross-validation of those matches showed that the algorithm outperforms random matching significantly. Statistically, for every data set the algorithm found at least one other record (match) with the same diagnosis.

Conclusions

Diagnostic delay is torturous for patients without a diagnosis. Shortening the delay is important for both doctors and patients. Diagnostic support using AI can be promoted differently. The prototype of the social media platform RarePairs might be a low-threshold patient platform, and proved suitable to match and connect different individuals with comparable symptoms. This exchange promoted through RarePairs might be used to speed up the diagnostic process. Further studies include its evaluation in a prospective setting and implementation of RarePairs as a mobile phone app.

Yield of next-generation neuropathy gene panels in axonal neuropathies

The use and utility of targeted gene panels for diagnosing the type of Charcot-Marie-Tooth have grown rapidly because commercial gene panels that contain most of the relevant genes are available and affordable for many patients. We used a targeted gene panel to analyze 175 patients who had an unexplained axonal polyneuropathy affecting large myelinated axons, 86 of whom reported a family history of neuropathy, and 89 of whom did not. In patients reporting a family history, the panel identified a pathogenic variant causing the neuropathy in six cases (7%); in patients not reporting a family history, the gene panel identified pathogenic variants causing neuropathy in two patients (2%). Interpretation in a tertiary referral setting, current gene panels identify the genetic cause of neuropathy in a small minority of patients who have an unexplained axonal neuropathy, even in those reporting a family history.

Magnetic resonance imaging in the diagnosis of white matter signal abnormalities

Background White matter abnormalities (WMAs) pose a diagnostic challenge when trying to establish etiologic diagnoses. During childhood and adult years, genetic disorders, metabolic disorders and acquired conditions are included in differential diagnoses. To assist clinicians and radiologists, a structured algorithm using cranial magnetic resonance imaging (MRI) has been recommended to aid in establishing working diagnoses that facilitate appropriate biochemical and genetic investigations. This retrospective pilot study investigated the validity and diagnostic utility of this algorithm when applied to white matter signal abnormalities (WMSAs) reported on imaging studies of patients seen in our clinics. Methods The MRI algorithm was applied to 31 patients selected from patients attending the neurometabolic/neurogenetic/metabolic/neurology clinics at a tertiary care hospital. These patients varied in age from 5 months to 79 years old, and were reported to have WMSAs on cranial MRI scans. Twenty-one patients had confirmed WMA diagnoses and 10 patients had non-specific WMA diagnoses (etiology unknown). Two radiologists, blinded to confirmed diagnoses, used clinical abstracts and the WMSAs present on patient MRI scans to classify possible WMA diagnoses utilizing the algorithm. Results The MRI algorithm displayed a sensitivity of 100%, a specificity of 30.0% and a positive predicted value of 74.1%. Cohen's kappa statistic for inter-radiologist agreement was 0.733, suggesting "good" agreement between radiologists. Conclusions Although a high diagnostic utility was not observed, results suggest that this MRI algorithm has promise as a clinical tool for clinicians and radiologists. We discuss the benefits and limitations of this approach.

Care and cost consequences of pediatric whole genome sequencing compared to chromosome microarray

The clinical use of whole-genome sequencing (WGS) is expected to alter pediatric medical management. The study aimed to describe the type and cost of healthcare activities following pediatric WGS compared to chromosome microarray (CMA). Healthcare activities prompted by WGS and CMA were ascertained for 101 children with developmental delay over 1 year. Activities following receipt of non-diagnostic CMA were compared to WGS diagnostic and non-diagnostic results. Activities were costed in 2016 Canadian dollars (CDN). Ongoing care accounted for 88.6% of post-test activities. The mean number of lab tests was greater following CMA than WGS (0.55 vs. 0.09; p = 0.007). The mean number of specialist visits was greater following WGS than CMA (0.41 vs. 0; p = 0.016). WGS results (diagnostic vs. non-diagnostic) modified the effect of test type on mean number of activities (p < 0.001). The cost of activities prompted by diagnostic WGS exceeded $557CDN for 10% of cases. In complex pediatric care, CMA prompted additional diagnostic investigations while WGS prompted tailored care guided by genotypic variants. Costs for prompted activities were low for the majority and constitute a small proportion of total test costs. Optimal use of WGS depends on robust evaluation of downstream care and cost consequences.

Genetic Testing among Children in a Complex Care Program

Little is known about the pattern of genetic testing and frequency of genetic diagnoses among children enrolled in structured complex care programs (CCPs). Such information may inform the suitability of emerging genome diagnostics for this population. The objectives were to describe the proportion of children with undiagnosed genetic conditions despite genetic testing and measure the testing period, types and costs of genetic tests used. A retrospective analysis of 420 children enrolled in Toronto’s Hospital for Sick Children’s CCP from January 2010 until June 2014 was conducted. Among those who underwent genetic testing (n = 319; 76%), a random sample of 20% (n = 63) was further analyzed. A genetic diagnosis was confirmed in 48% of those who underwent testing. Those with no genetic diagnosis underwent significantly more genetic tests than those with a confirmed genetic diagnosis [median interquartile range (IQR): six tests (4–9) vs. three tests (2–4), p = 0.002], more sequence-level tests and a longer, more expensive testing period than those with a genetic diagnosis [median (IQR): length of testing period: 4.12 years (1.73–8.42) vs. 0.35 years (0.12–3.04), p < 0.001; genetic testing costs C$8496 ($4399–$12,480) vs. C$2614 ($1605–$4080), p < 0.001]. A genetic diagnosis was not established for 52% of children. Integrating genome-wide sequencing into clinical care may improve diagnostic efficiency and yield in this population.

The Changing Landscape of Molecular Diagnostic Testing: Implications for Academic Medical Centers

Over the last decade, the field of molecular diagnostics has undergone tremendous transformation, catalyzed by the clinical implementation of next generation sequencing (NGS). As technical capabilities are enhanced and current limitations are addressed, NGS is increasingly capable of detecting most variant types and will therefore continue to consolidate and simplify diagnostic testing. It is likely that genome sequencing will eventually serve as a universal first line test for disorders with a suspected genetic origin. Academic Medical Centers (AMCs), which have been at the forefront of this paradigm shift are now presented with challenges to keep up with increasing technical, bioinformatic and interpretive complexity of NGS-based tests in a highly competitive market. Additional complexity may arise from altered regulatory oversight, also triggered by the unprecedented scope of NGS-based testing, which requires new approaches. However, these challenges are balanced by unique opportunities, particularly at the interface between clinical and research operations, where AMCs can capitalize on access to cutting edge research environments and establish collaborations to facilitate rapid diagnostic innovation. This article reviews present and future challenges and opportunities for AMC associated molecular diagnostic laboratories from the perspective of the Partners HealthCare Laboratory for Molecular Medicine (LMM).