Access to clinically indicated genetic tests for pediatric patients with Medicaid: Evidence from outpatient genetics clinics in Texas.

PURPOSE: Little is known about how Medicaid coverage policies affect access to genetic tests for pediatric patients. Building upon and extending a previous analysis of prior authorization requests (PARs), we describe expected coverage of genetic tests submitted to Texas Medicaid and the PAR and diagnostic outcomes of those tests. METHODS: We retrospectively reviewed genetic tests ordered at 3 pediatric outpatient genetics clinics in Texas. We compared Current Procedural Terminology (CPT) codes with the Texas Medicaid fee-for-service schedule (FFSS) to determine whether tests were expected to be covered by Medicaid. We assessed completion and diagnostic yield of commonly ordered tests. RESULTS: Among the 3388 total tests submitted to Texas Medicaid, 68.9% (n = 2336) used at least 1 CPT code that was not on the FFSS and 80.7% (n = 2735) received a favorable PAR outcome. Of the tests with a CPT code not on the FFSS, 60.0% (n = 1400) received a favorable PAR outcome and were completed and 20.5% (n = 287) were diagnostic. The diagnostic yield of all tests with a favorable PAR outcome that were completed was 18.7% (n = 380/2029). CONCLUSION: Most PARs submitted to Texas Medicaid used a CPT code for which reimbursement from Texas Medicaid was not guaranteed. The frequency with which clinically indicated genetic tests were not listed on the Texas Medicaid FFSS suggests misalignment between genetic testing needs and coverage policies. Our findings can inform updates to Medicaid policies to reduce coverage uncertainty and expand access to genetic tests with high diagnostic utility.

An interactive web application for exploring human plasma and fibroblast metabolomics data from patients with inborn errors of metabolism.

Metabolomic profiling is instrumental in understanding the systemic and cellular impact of inborn errors of metabolism (IEMs), monogenic disorders caused by pathogenic genomic variants in genes involved in metabolism. This study encompasses untargeted metabolomics analysis of plasma from 474 individuals and fibroblasts from 67 subjects, incorporating healthy controls, patients with 65 different monogenic diseases, and numerous undiagnosed cases. We introduce a web application designed for the in-depth exploration of this extensive metabolomics database. The application offers a user-friendly interface for data review, download, and detailed analysis of metabolic deviations linked to IEMs at the level of individual patients or groups of patients with the same diagnosis. It also provides interactive tools for investigating metabolic relationships and offers comparative analyses of plasma and fibroblast profiles. This tool emphasizes the metabolic interplay within and across biological matrices, enriching our understanding of metabolic regulation in health and disease. As a resource, the application provides broad utility in research, offering novel insights into metabolic pathways and their alterations in various disorders.