Diagnostic yield of exome and genome sequencing for critically ill pediatric cardiac patients

Introduction

Pediatric cardiovascular disease encompasses a wide spectrum of conditions including congenital heart disease (CHD), arrhythmias, and cardiomyopathies. Recognition of underlying genetic etiologies of pediatric cardiovascular disease is increasing, with recent literature suggesting pathologic genetic variation may explain 40% of CHD (1), 20% of primary arrhythmias (2,3), and 20–40% of cardiomyopathies (2-5). While cardiology-focused guidelines exist for next-generation sequencing in the latter groups (4-6), cardiovascular societies have not broadly recommended next-generation sequencing for CHD (7-9). Etiologic genetic factors have been found in both isolated CHD and CHD associated with extracardiac anomalies, highlighting the importance of understanding pathogenic variation for elucidating disease mechanisms, guiding therapy, assessing effects on post-operative and long-term outcomes, and determining familial recurrence risk (10-14).

Genetic testing is increasingly being integrated into the diagnostic approach for pediatric heart disease. At many centers, chromosomal microarray (CMA) analysis is a first-line genetic test in patients with CHD (8,15). Microarray can detect aneuploidies and copy number variants involving smaller regions of DNA. While CMA offers higher diagnostic yield than karyotyping, it cannot detect copy number variants below approximately 5,000 base pairs, small insertions/deletions, or single nucleotide variants (16). Consequently, CMA fails to diagnose genetic etiologies in a considerable proportion of patients, with diagnostic yields of 8–24% (15-20).

Advances in next-generation sequencing technologies revolutionized genetic testing, with the American College of Medical Genetics and Genomics (ACMG) recommending exome sequencing (ES) or genome sequencing (GS) as first- or second-line genetic tests for congenital anomalies, including CHD (21). However, while existing cardio-genetic testing guidelines recognize the potential value of ES/GS (4,5), they emphasize use of disease-specific gene panels for conditions such as primary ventricular arrhythmias and cardiomyopathies (4-6). Despite the ACMG recommendations, most current cardiology-focused recommendations do not include broad use of next-generation sequencing technologies in structural CHD (7,8). ES/GS are increasingly feasible, with high diagnostic yields in various pediatric populations, including critically ill infants, and non-critically ill cardiac and non-cardiac patients (3,22-24). However, a recent survey found that <12% of institutions with cardiac intensive care units (CICUs) use ES or GS as a first-line test (25). We herein aimed to report the diagnostic yield of ES/GS in pediatric patients at a single pediatric institution who presented with critical illness due to wide ranging cardiac pathologies. We also aimed to describe the mutational spectrum of diagnostic variants and explore whether variants reported on ES/GS would likely have been reported on commercial cardiac panels or detected by CMA. We present this article in accordance with the TREND reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-877/rc).

Methods

We conducted a retrospective review of all patients who underwent clinical ES/GS in the CICU at a single quaternary care children’s hospital from January 2020 to August 2023. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional review board of Nationwide Children’s Hospital (No. IRB00000568), and informed patient consent was waived for this retrospective analysis. Data collected included demographic information (age, sex, ethnicity, and race) and clinical characteristics including presence of extracardiac anomalies, cardiac phenotype, and clinical outcome. Race and ethnicity were reported as included in the medical record using hospital-based categories. Study data were managed using Research Electronic Data Capture tools (REDCap) hosted at Nationwide Children’s Hospital. Data supporting the findings of this study are available upon reasonable request.

Classification of cardiac and extracardiac phenotype

Patients were categorized into cardiac phenotype groups: CHD and “non-CHD”, which included patients with ventricular dysfunction and primary arrhythmia. In cases of overlapping cardiac phenotypes of CHD and arrhythmia, priority was given to CHD. Two patients with both CHD and hypertrophic cardiomyopathy were assigned to the CHD group. One patient with cardiac rhabdomyomas was classified as “other”. Patients were further classified by extracardiac anomaly status.

Genetic testing outcomes and variant level analysis

All available clinical genetic testing results were collected for all subjects, which included karyotype, CMA, specific variant tests, gene panels, ES and GS. During the study period, an institutional clinical protocol directed use of karyotype/CMA for some patients with CHD in the CICU, but no protocol existed for ES/GS. ES/GS results were classified using modified Clinical Sequencing Evidence-Generating Research Sequence Analysis and Diagnostic Yield workgroup criteria (26,27) as outlined in Figure 1. Furthermore, diagnostic tests were adjudicated as “diagnostic for cardiac phenotype” and/or “diagnostic for noncardiac phenotype” to describe genotype:phenotype relationship. The research team, including a cardiologist, clinical geneticist, genetic counselor, and cardiac intensivist, reviewed testing results. Diagnostic variants were analyzed based on ACMG classification in the testing report. Variants were assessed by the research team to determine whether CMA would likely detect each variant. Based on gene and variant location and type, the research team determined whether each variant would be detectable on common commercially available cardiac gene panels at four laboratories: Invitae, GeneDx, Mayo Clinic, and Prevention Genetics (Table S1).

Figure 1 Flow chart illustrating exome/genome sequencing result classification. Tests designated as “positive” in clinical reports were classified as positive/“diagnostic”. Results reported as indeterminate by the testing laboratory but considered diagnostic by the patient’s clinical team (clinical geneticist or cardiology subspecialist with expertise in the phenotype or gene of interest) were considered diagnostic for the study. The remaining indeterminate results underwent research team review; one patient who was deemed to have a genotype-phenotype match was classified as diagnostic.

Statistical analysis

Patient demographic and clinical characteristics were described using median with interquartile range (IQR) for continuous variables and frequency (percent of total) for categorical variables. Wilcoxon rank sum tests were used to compare age at genetic testing by primary cardiac phenotype and presence of extracardiac anomalies. Extracardiac anomalies and diagnostic yield were compared between cardiac phenotype subgroups using Fisher’s exact test due to the relatively small sample sizes and the categorical nature of the data. P values were not adjusted for multiple comparisons. All statistical analyses were performed using R version 4.2.2 (R Core Team, Vienna, Austria).

Results

A total of 45 patients underwent clinical ES/GS during the study period. Table 1 summarizes patient characteristics, with median age 33 [7–905] days. Twenty-five patients (55.6%) had CHD and 19/45 (42.2%) had ventricular dysfunction or primary arrhythmia (“non-CHD”). GS was the predominant molecular test (30/45, 66.7%) with 50.0% diagnostic rate (15/30) compared to exome (5/15, 33.3%), Figure 2.

Figure 2 Comparisons of rates of diagnostic results by (A) next-generation sequencing test type, (B) cardiac phenotype group, and (C) presence of extracardiac anomalies using Fisher’s exact test. CHD, congenital heart disease.

Overall, ES/GS yielded diagnostic results for 20/45 (44.4%) patients, while 16/45 (35.6%) were negative, and 9/45 (20.0%) were indeterminate (Figure 1). Among diagnostic results, 18/20 (90.0%) were specifically diagnostic for cardiac phenotypes, yielding an overall cardiac phenotype diagnostic rate of 40.0% (18/45). The other two diagnostic tests were diagnostic only for non-cardiac phenotypes. Table S2 lists case-level data for all diagnostic cases. Of note, 4 patients with variant(s) of uncertain significance (VUS) were deemed to have diagnostic results based on the clinical assessment and documentation of either the genetics or electrophysiology specialists at our institution.

Twenty-eight unique variants were identified among 20 diagnostic tests as summarized in Table S3. Most were pathogenic/likely pathogenic (23/28, 82.1%), with 17.9% VUS. Most variants (23/28, 82.1%) were single nucleotide variants or small insertions/deletions, and thus undetectable on CMA. Importantly, among the 25 variants found on 18 tests diagnostic for cardiac phenotypes, 9/25 (36.0%) would not have been detected on referenced cardiac gene panels because they were either copy number variants or single nucleotide variants in genes not included on these panels. Table S4 lists genes discovered on tests diagnostic for cardiac disease, stratified by phenotype. Table S5 details variant information for all diagnostic cases.

CHD patients were compared to non-CHD patients (Table 2), due to differences in cardiology societal recommendations for next-generation sequencing between these populations. As anticipated, CHD patients were significantly younger at genetic testing (17 days, IQR, 7–60 days) than non-CHD patients (2,316 days, IQR, 7–5,483 days; P=0.006), and more likely to have extracardiac anomalies (18/25 vs. 4/19; P=0.002). However, diagnostic yield did not differ significantly between these groups, with 48.0% yield in the CHD group (12/25) and 36.8% in the non-CHD group (7/19; P=0.55). GS demonstrated a non-significant trend toward higher diagnostic yields overall (50%), and among the CHD (61.1%) and extracardiac anomalies subgroups (66.7%) when compared to ES (33.3%; P=0.35, 14.3%; P=0.07, and 37.5%; P=0.22, respectively) (Figure 2).

Discussion

Our review of ES and GS in the pediatric CICU revealed several key findings. The diagnostic yield of ES/GS was 44.4%, with no significant difference between CHD and non-CHD patients. Diagnostic yield trended higher with genome (50%) compared to ES (33.3%), particularly in CHD patients and those with extracardiac anomalies. Many genetic diagnoses in our cohort would have been missed using common cardiac gene panels alone (36.0%). Our results underscore the potential diagnostic impact of ES/GS in critically ill pediatric cardiology populations.

The overall diagnostic yield of ES/GS in our study (20/45, 44.4%) is consistent with previous studies in pediatric cardiology, which reported yields ranging from 27–46% (3,24,28). We included cardiac and non-cardiac diagnoses, putting our yield on the higher end of this range. However, when accounting only for cardiac phenotypes, our diagnostic yield was still 40.0%. Understanding both cardiac and non-cardiac diagnoses is crucial, as either may impact complications, prognosis, and outcomes in critically ill patients. This study highlights the high diagnostic yield of ES/GS across a spectrum of pediatric cardiac phenotypes, as yield was comparable between CHD patients and patients with arrhythmia or ventricular dysfunction. Genetic contributions to ventricular dysfunction and primary arrhythmias are better understood, largely because these conditions are more prevalent in adults and have been extensively studied. In contrast, CHD has not received the same level of focus, resulting in fewer established guidelines for genetic evaluation. Current cardiology societal recommendations for next-generation sequencing are typically limited to conditions such as cardiomyopathies and arrhythmias, often involving targeted gene panels, with CMA usually recommended as the first-tier test for CHD (4-6,8). While a recent ACMG statement suggested ES or GS could be used as a first- or second-tier diagnostic test for any congenital anomaly, including CHD (21), ES/GS are not consistently applied in clinical cardiology practice, likely related this discrepancy between cardiology and genetic society guidelines (7,8,15).

Our study indicates ES/GS can be as valuable in CHD as in other pediatric cardiac phenotypes. It is notable, however, our CHD cohort had a higher prevalence of extracardiac anomalies (72.0%) compared to previously reported estimates of extracardiac anomalies in CHD (up to 43–50%) (29,30). This is likely because our institution utilized ES/GS more frequently in the extracardiac anomaly population than the isolated CHD population during the study period. This may have inflated the diagnostic yield in the CHD group, as patients with extracardiac anomalies are known to be more likely to carry a genetic diagnosis (3,20,31-35). In our study, patients with extracardiac anomalies were nearly twice as likely to have a diagnostic result compared to those with isolated cardiac disease, which is consistent with prior studies (31-34). However, about one-third of patients with isolated cardiac disease also had diagnostic results, highlighting the potential impact of broad genetic testing in cardiac disease rather than targeting only patients with extracardiac anomalies. This is especially significant when considering the use of ES/GS for neonates, as the presence of extracardiac anomalies may not yet be apparent in the newborn period. Additionally, recent literature showing that presence of extracardiac anomalies has a low-moderate screening performance as an indication for ES/GS and performing ES/GS based on extracardiac anomalies status leads to underdiagnosis in patients with isolated CHD (35).

This study demonstrated higher rates of pathogenic genetic variation detection using GS than ES and using ES/GS compared to traditional genetic tests (microarray and gene panels). While this study was likely underpowered to detect a statistically significant difference between GS and ES, GS had a diagnostic yield of 50.0% compared to 33.3% for exome. Recent literature also supports that GS has higher diagnostic yield than ES (3,29). Historically, CMA has been more broadly recommended for CHD (7,8,15), however, our results suggest using microarray alone would miss a substantial proportion of underlying genetic diagnoses. Recent literature has highlighted the improved utility of GS over CMA (34,36), especially given large genomic variation detected by microarrays can typically be detected by GS. Gene panels detect single nucleotide variants and small insertions/deletions, thus detecting a range of pathogenic variants distinct from CMA (20). However, common current cardiology-focused gene panels alone would have missed over one-third of the variants detected in our study, which aligns with recent literature indicating higher diagnostic yield in ES/GS compared to gene panels (19,28,37). In an era of rapid genomic advancement, gene panels do not capture emerging diagnostic findings and do not facilitate future evaluation or re-interpretation of newly identified clinically relevant genes. The genes included in specific gene panels vary by laboratory (38), addition of new genes to panels often lags behind evidence-based data (37), and the chances of finding VUS may actually be higher on multi-gene panels than ES/GS (39). Additionally, while costs of ES/GS are generally higher than gene panels, the overall cost-benefit of a positive diagnostic result favors the more comprehensive test, especially in critically ill patients (39-41).

Limitations of this study included its retrospective nature, small sample size, and potential selection bias. The retrospective nature may lead to selection bias and overestimation of the yield as not all patients in the CICU underwent GS during the study period. Effects of genetic diagnoses on outcomes were outside the scope of this study and were not readily accessible form medical record documentation, though this is an opportunity for future prospective sequencing research. The study’s confinement to a single center and specialized CICU also limits generalizability to the broader pediatric cardiology population, though this is an area for future investigation. The small sample size prohibited an analysis of more specific cardiac substrates, thus was limited to classes of pathologies. Within each class, there is likely variability of diagnostic yield, which was unable to be reported in this study.

Conclusions

In summary, our study demonstrates the potential for a high diagnostic yield of ES/GS in pediatric CICU settings, especially in patients with extracardiac anomalies. Moreover, both GS and ES demonstrate significant clinical value compared to targeted gene panels or CMA. High diagnostic yield and abilities of ES/GS to uncover genetic etiologies of disease or allow discovery of novel genetic mechanisms make them invaluable. These findings support broader implementation of ES/GS for critically ill pediatric cardiac patients to improve diagnostic outcomes. Future studies involving larger, multi-center cohorts are warranted to further validate these findings and assess broader applicability of ES/GS in pediatric cardiology.

Acknowledgments

We would like to acknowledge Dr. Dennis Lewandowski, who reviewed and edited the manuscript. This work has been previously presented at the American Heart Association Scientific Sessions 2024.

Footnote

Reporting Checklist: The authors have completed the TREND reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-877/rc

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-877/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-877/prf

Funding: The study was supported by the National Institutes of Health (No. R21 HL161823 to P.W. and V.G.), and in part by The Ohio State University Clinical and Translational Science Institute (CTSI) and the National Center for Advancing Translational Sciences of the National Institutes of Health (No. UM1TR004548 to B.P.C.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-877/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional review board of Nationwide Children’s Hospital (No. IRB00000568), and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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