Development of an economic evaluation of diagnostic strategies: the case of monogenic diabetes

The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion

BACKGROUND

Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field.

METHODS

Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice.

RESULTS

There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted.

CONCLUSIONS

We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.

Evaluating the Value for Money of Precision Medicine from Early Cycle to Market Access: A Comprehensive Review of Approaches and Challenges

OBJECTIVES

This study aimed to perform a comprehensive review of modeling approaches and methodological and policy challenges in the economic evaluation (EE) of precision medicine (PM) across clinical stages.

METHODS

First, a systematic review was performed to assess the approaches of EEs in the past 10 years. Next, a targeted review of methodological articles was conducted for methodological and policy challenges in performing EEs of PM. All findings were synthesized into a structured framework that focused on patient population, Intervention, Comparator, Outcome, Time, Equity and ethics, Adaptability and Modeling aspects, named the "PICOTEAM" framework. Finally, a stakeholder consultation was conducted to understand the major determinants of decision making in PM investment.

RESULTS

In 39 methodological articles, we identified major challenges to the EE of PM. These challenges include that PM applications involve complex and evolving clinical decision space, that clinical evidence is sparse because of small subgroups and complex pathways in PM settings, a one-time PM application may have lifetime or intergenerational impacts but long-term evidence is often unavailable, and that equity and ethics concerns are exceptional. In 275 EEs of PM, current approaches did not sufficiently capture the value of PM compared with targeted therapies, nor did they differentiate Early EEs from Conventional EEs. Finally, policy makers perceived the budget impact, cost savings, and cost-effectiveness of PM as the most important determinants in decision making.

CONCLUSIONS

There is an urgent need to modify existing guidelines or develop a new reference case that fits into the new healthcare paradigm of PM to guide decision making in research and development and market access.

Strategies to identify individuals with monogenic diabetes: results of an economic evaluation

OBJECTIVES

To evaluate and compare the lifetime costs associated with strategies to identify individuals with monogenic diabetes and change their treatment to more appropriate therapy.

DESIGN

A decision analytical model from the perspective of the National Health Service (NHS) in England and Wales was developed and analysed. The model was informed by the literature, routinely collected data and a clinical study conducted in parallel with the modelling.

SETTING

Secondary care in the UK.

PARTICIPANTS

Simulations based on characteristics of patients diagnosed with diabetes <30 years old.

INTERVENTIONS

Four test-treatment strategies to identify individuals with monogenic diabetes in a prevalent cohort of diabetics diagnosed under the age of 30 years were modelled: clinician-based genetic test referral, targeted genetic testing based on clinical prediction models, targeted genetic testing based on biomarkers, and blanket genetic testing. The results of the test-treatment strategies were compared with a strategy of no genetic testing.

PRIMARY AND SECONDARY OUTCOME MEASURES

Discounted lifetime costs, proportion of cases of monogenic diabetes identified.

RESULTS

Based on current evidence, strategies using clinical characteristics or biomarkers were estimated to save approximately £100-£200 per person with diabetes over a lifetime compared with no testing. Sensitivity analyses indicated that the prevalence of monogenic diabetes, the uptake of testing, and the frequency of home blood glucose monitoring had the largest impact on the results (ranging from savings of £400-£50 per person), but did not change the overall findings. The model is limited by many model inputs being based on very few individuals, and some long-term data informed by clinical opinion.

CONCLUSIONS

Costs to the NHS could be saved with targeted genetic testing based on clinical characteristics or biomarkers. More research should focus on the economic case for the use of such strategies closer to the time of diabetes diagnosis.

TRIAL REGISTRATION NUMBER

NCT01238380.

Economics of Genetic Testing for Diabetes

PURPOSE OF REVIEW

Monogenic diabetes is an uncommon but important form of diabetes, with the most common causes benefitting from management that accounts for the genetic mutation. This often results in decreased costs and treatment burden for affected individuals. Misdiagnosis as type 1 and type 2 diabetes is common. Given the significant burden of diabetes costs to the healthcare system, it is important to assess the economic impact of incorporating genetic testing for monogenic diabetes into clinical care through formal cost-effectiveness analyses (CEAs). This article briefly summarizes the barriers to timely monogenic diabetes diagnosis and then summarizes findings from CEAs on genetic testing for monogenic diabetes.

RECENT FINDINGS

CEAs have shown that routine genetic testing of all patients with a clinical diagnosis of type 1 diabetes can be cost-saving when applied to the scenarios of neonatal diabetes or in a pediatric population. Routine screening has not been shown to be cost-effective in adult populations. However, next-generation sequencing strategies and applying biomarkers to identify and limit genetic testing to people most likely to have monogenic diabetes are promising ways to make testing strategies cost-effective. CEAs have shown that genetic testing for monogenic diabetes diagnosis can be cost-effective or cost-saving and should guide insurers to consider broader coverage of these tests, which would lead to accurate and timely diagnosis and impact treatment and clinical outcomes.

The rise of the genome and personalised medicine

Virtually all medical specialties are impacted by genetic disease. Enhanced understanding of the role of genetics in human disease, coupled with rapid advancement in sequencing technology, is transforming the speed of diagnosis for patients and providing increasing opportunities to tailor management. As set out in the Annual report of the Chief Medical Officer 2016: Generation Genome1 and the recent NHS England board paper Creating a genomic medicine service to lay the foundations to deliver personalised interventions and treatments,2 the increasing 'mainstreaming' of genetic testing into routine practice and plans to embed whole genome sequencing in the NHS mean that the profile and importance of genomics is on the rise for many clinicians. This article provides a brief overview of genomics and its current clinical applications, including its contribution to personalised medicine. Physicians will be signposted to key issues that will allow the successful implementation of genomics for rare disease diagnosis and cancer management.

Population-Based Assessment of a Biomarker-Based Screening Pathway to Aid Diagnosis of Monogenic Diabetes in Young-Onset Patients

OBJECTIVE

Monogenic diabetes, a young-onset form of diabetes, is often misdiagnosed as type 1 diabetes, resulting in unnecessary treatment with insulin. A screening approach for monogenic diabetes is needed to accurately select suitable patients for expensive diagnostic genetic testing. We used C-peptide and islet autoantibodies, highly sensitive and specific biomarkers for discriminating type 1 from non-type 1 diabetes, in a biomarker screening pathway for monogenic diabetes.

RESEARCH DESIGN AND METHODS

We studied patients diagnosed at age 30 years or younger, currently younger than 50 years, in two U.K. regions with existing high detection of monogenic diabetes. The biomarker screening pathway comprised three stages: 1) assessment of endogenous insulin secretion using urinary C-peptide/creatinine ratio (UCPCR); 2) if UCPCR was ≥0.2 nmol/mmol, measurement of GAD and IA2 islet autoantibodies; and 3) if negative for both autoantibodies, molecular genetic diagnostic testing for 35 monogenic diabetes subtypes.

RESULTS

A total of 1,407 patients participated (1,365 with no known genetic cause, 34 with monogenic diabetes, and 8 with cystic fibrosis-related diabetes). A total of 386 out of 1,365 (28%) patients had a UCPCR ≥0.2 nmol/mmol, and 216 out of 386 (56%) were negative for GAD and IA2 and underwent molecular genetic testing. Seventeen new cases of monogenic diabetes were diagnosed (8 common Maturity Onset Diabetes of the Young [Sanger sequencing] and 9 rarer causes [next-generation sequencing]) in addition to the 34 known cases (estimated prevalence of 3.6% [51/1,407] [95% CI 2.7-4.7%]). The positive predictive value was 20%, suggesting a 1-in-5 detection rate for the pathway. The negative predictive value was 99.9%.

CONCLUSIONS

The biomarker screening pathway for monogenic diabetes is an effective, cheap, and easily implemented approach to systematically screening all young-onset patients. The minimum prevalence of monogenic diabetes is 3.6% of patients diagnosed at age 30 years or younger.