Rapid whole genome sequencing impacts care and resource utilization in infants with congenital heart disease

Abstract

Congenital heart disease (CHD) is the most common congenital anomaly and a major cause of infant morbidity and mortality. While morbidity and mortality are highest in infants with underlying genetic conditions, molecular diagnoses are ascertained in only ~20% of cases using widely adopted genetic tests. Furthermore, cost of care for children and adults with CHD has increased dramatically. Rapid whole genome sequencing (rWGS) of newborns in intensive care units with suspected genetic diseases has been associated with increased rate of diagnosis and a net reduction in cost of care. In this study, we explored whether the clinical utility of rWGS extends to critically ill infants with structural CHD through a retrospective review of rWGS study data obtained from inpatient infants < 1 year with structural CHD at a regional children's hospital. rWGS diagnosed genetic disease in 46% of the enrolled infants. Moreover, genetic disease was identified five times more frequently with rWGS than microarray ± gene panel testing in 21 of these infants (rWGS diagnosed 43% versus 10% with microarray ± gene panels, p = 0.02). Molecular diagnoses ranged from syndromes affecting multiple organ systems to disorders limited to the cardiovascular system. The average daily hospital spending was lower in the time period post blood collection for rWGS compared to prior (p = 0.003) and further decreased after rWGS results (p = 0.000). The cost was not prohibitive to rWGS implementation in the care of this cohort of infants. rWGS provided timely actionable information that impacted care and there was evidence of decreased hospital spending around rWGS implementation.

Conflict of interest statement

D.D. declares the following potential conflicts of interest: Biomarin (consultant for pegvaliase trials), Audentes Therapeutics (scientific advisory board), and Ichorion Therapeutics (consultant for mitochondrial disease drugs). The rest of the authors declare that there are no competing interests.

Figures

Fig. 1. Flow diagram of families referred for rWGS and the rate of consent to the study.

Eighty-six percent of families who underwent the informed consent process enrolled in the rWGS study. *Three families did not undergo informed consent process (one maxed out on number of contact attempts per research protocol, two for unknown reasons). The majority of families underwent trio rWGS (67%). **Four families declined participation after informed consent process: two secondary to concerns for lack of protection under GINA (Genetic Information Nondiscrimination Act of 2008, Pub. L. 110–233, 122 Stat. 881), one wanted more than just phenotype driven genetic information, and one did not follow up after consent process.

Fig. 2. Rate of genetic diagnosis with rWGS, CMA and gene panels.

a–c Rate of Genetic diagnosis with rWGS, CMA and gene panels. a Rate of diagnosis in cohort by rWGS. rWGS had a higher rate of diagnosis (11/24) in the cohort compared to microarray (1/19) and microarray +/− gene panel (2/21). b Rate of diagnosis in group tested by rWGS and CMA. The rate of diagnosis was statistically significant higher with rWGS compared to microarray (p = 0.04*; McNemar’s Test) when comparing the rate of diagnosis within the group that had both microarray and rWGS testing (n = 19). c Rate of diagnosis in group tested by rWGS and CMA/Gene Panels. When comparing the rate of diagnosis of rWGS within the group that received microarray +/− gene panels testing (n = 21) and rWGS, rWGS still outperformed in yielding a diagnosis (p = 0.02**; McNemar’s Test).

Fig. 3. Temporal trends in hospital costs around the time of rWGS testing.

Evaluation of spending trend surrounding the rWGS process showed an overall decreased spending post rWGS results (Supplementary Table 2b). There is a significant association between time period and cost (p = 0.01; repeated measures ANOVA). Specifically, there is a significant difference in cost between periods 1 and 3 (mean difference 2266.4; 95% CI 1035.6–3497.1; *p = 0.001; paired t-test) and periods 2 and 3 (mean difference 1917.1; 95% CI 1077.2–2756.9; **p = 0.0001; paired t-test). There is not a significant difference in cost between the nondiagnostic and diagnostic groups by time period (p = 0.70; repeated measures ANOVA).

Fig. 4. Average daily hospital cost per tercile of hospitalization.

Total Hospital cost was divided in three equal parts and the average daily hospital costs calculated (Supplementary Table 2c). There is a significant association between time period and cost (p = 0.047; repeated measures ANOVA). There was statistically significant decrease in average daily hospital cost from the first third of the hospitalization compared to the last third (p = 0.036, mean difference 1438.53, SE 518.81, 95% CI 76.60–2800.47), but there was no statistically significant decrease when comparing the first third of the hospitalization with the second third (p = 1, mean difference 178.22, SE 352.18, 95% CI −746.30 to 1102.73) or the second third with the last third (p = 0.1, mean difference 1260.32, SE 551.59, 95% CI −187.66 to 2708.30; repeated measures ANOVA with Bonferroni correction).