542 - SeqFirst: Building equity access to a precise genetic diagnosis in critically ill newborns

Publication Number: 542.6821

Tara L. Wenger, Seattle Children's Hospital, Lake Tapps, WA, United States; Abbey A. Scott, Seattle Children's, Seattle, WA, United States; Lukas Kruidenier, Seattle Children's, Seattle, WA, United States; Megan Sikes, Seattle Children's, Lake Forest Park, WA, United States; Alexandra C. Keefe, Seattle Children's, Seattle, WA, United States; Kati J.. Buckingham, University of Washington School of Medicine, Seattle, WA, United States; Colby T. Marvin, University of Washington School of Medicine, Seattle, WA, United States; Kathryn M. Shively, University of Washington School of Medicine, Seattle, WA, United States; Tamara J. Bacus, University of Washington School of Medicine, Seattle, WA, United States; Olivia M. Sommerland, University of Washington School of Medicine, Seattle, WA, United States; Kailyn Anderson, University of Washington School of Medicine, Seattle, WA, United States; Danny Miller, Seattle Children's, Seattle, WA, United States; Jamie Love-Nichols, Seattle Children's, Seattle, WA, United States; Bethany Friedman, GeneDx, Gaithersburg, MD, United States; Julia Parish-Morris, Perelman School of Medicine at the University of Pennsylvania, FORT WASHINGTON, PA, United States; Kirsty McWalter, GeneDx, Gaithersburg, MD, United States; Deborah Copenheaver, GeneDx, Potomac, MD, United States; Kyle Retterer, Geisinger, Danville, PA, United States; Jane Juusola, GeneDx, Gaithersburg, MD, United States; Joon-Ho Yu, University of Washington School of Medicine, Seattle, WA, United States; Dan Doherty, University of Washington School of Medicine, Seattle, WA, United States; Katrina Dipple, Seattle Children's, Seattle, WA, United States; Paul Kruszka, GeneDx, Alexandria, VA, United States; Michael Bamshad, University of washington, seattle, WA, United States

Background: Access to a precise genetic diagnosis (PrGD) in critically ill newborns is limited and inequitable because complex inclusion criteria used to prioritize testing eligibility omits many patients at high risk for a genetic condition. SeqFirst-neo is a program to test whether a genotype-driven workflow using simple, broad exclusion criteria to assess eligibility for rapid whole genome sequencing (rWGS) increases access to a PrGD in critically ill newborns.

Objective: To test whether use of simple exclusion criteria (infants not fully explained by prematurity, infection or trauma) and rapid genome sequencing increased access to PrGD in neonates admitted at a Level IV NICU, compared to conventional care (genetics consult in infants suspected of having a genetic condition by neonatology followed by clinical genetic testing recommended by the consultation service).

Design/Methods: All 408 newborns admitted to a neonatal intensive care unit between January 2021 and February 2022 were assessed and of 240 eligible infants, 126 were offered rWGS (i.e., intervention group [IG]) and compared to 114 infants who received conventional care in parallel (i.e., conventional care group [CCG]).

Results: A PrGD was made in 62/126 (49.2%) IG neonates compared to 11/114 (9.7%) CCG infants. The odds of receiving a PrGD was ~9 times greater in the IG vs. the CCG, and this difference was maintained at 12 months follow up. Access to a PrGD in the IG versus CCG differed significantly between infants identified as non-white (34/74, 45.9% vs. 6/29, 20.7%; p=0.024) and Black (8/10, 80.0% vs. 0/4; p=0.015). Neonatologists were significantly less successful at predicting a PrGD in non-white than non-Hispanic white patients. Use of a standard workflow in the IG with a PrGD revealed that a PrGD would have been missed in 26/62 (42%) of infants.

Conclusion(s): Use of simple, broad exclusion criteria that increases access to genetic testing significantly increases access to a PrGD, improves access equity and results in fewer missed diagnoses.

This work was funded by the Brotman Baty Institute, GeneDx and Illumina (Mike Bamshad, PI)

Figure 1: Diagnostic yield in the intervention group
PAS Figure 1 SeqFirst.pdfDiagnostic yield in the intervention group. Overall diagnostic yield stratified by test result category in the intervention group (IG) and the conventional care group (CCG) at 90 days and one year post ascertainment.

Figure 2: Diagnostic yield in the intervention group by phenotypic presentation and by racial construct
PAS Figure 2 SeqFirst.pdf(A) Diagnostic yield in the intervention group (IG) stratified by phenotype presentation, parent or provider assigned racial construct (PPARC) and test result category. Abbreviations: multiple congenital anomalies, MCA; congenital heart defects, CHD. (B) Access to a PrGD / diagnostic yield in the intervention group (IG), access to a PrGD in the conventional care group (CCG) and the diagnostic yield in the conventional care group who received genetic testing (CCG-tested) stratified by PPARC. Abbreviations: precise genetic diagnosis (PrGD). Asterisks denote statistically significant differences between groups.

Figure 3: Adjudication of variants reported in patients in the intervention cohort
PAS Figure 3 SeqFirst.pdfOf the 126 participants, 85 had one or more potentially explanatory variants reported by GeneDx based on the American College of Medical Genetics and Genomics (ACMG) guidelines for clinical sequence interpretation (variant of uncertain significance [VUS], likely pathogenic [LP], pathogenic [P]). We grouped these variants by the genes/regions in which they were reported (i.e., single gene, cytogenetic event, mitochondrial event) and by the inferred inheritance pattern(s) of the condition(s) they underlie (autosomal dominant [AD] or X-linked [XL], autosomal recessive [AR], or unknown mode of inheritance [MOI] for genes that underlie both AD and AR conditions. Variant(s) or event(s) and the MOI were then adjudicated together to determine whether each combination was explanatory of clinical findings (either partly or fully), unrelated to clinical findings and secondary, or of uncertain relationship to clinical findings of each participant. Variants / events in each participant were counted separately so for example the same genotype in two participants would be counted twice.