The genetic architecture of pediatric cardiomyopathy.

To understand the genetic contribution to primary pediatric cardiomyopathy, we performed exome sequencing in a large cohort of 528 children with cardiomyopathy. Using clinical interpretation guidelines and targeting genes implicated in cardiomyopathy, we identified a genetic cause in 32% of affected individuals. Cardiomyopathy sub-phenotypes differed by ancestry, age at diagnosis, and family history. Infants < 1 year were less likely to have a molecular diagnosis (p < 0.001). Using a discovery set of 1,703 candidate genes and informatic tools, we identified rare and damaging variants in 56% of affected individuals. We see an excess burden of damaging variants in affected individuals as compared to two independent control sets, 1000 Genomes Project (p < 0.001) and SPARK parental controls (p < 1 × 10-16). Cardiomyopathy variant burden remained enriched when stratified by ancestry, variant type, and sub-phenotype, emphasizing the importance of understanding the contribution of these factors to genetic architecture. Enrichment in this discovery candidate gene set suggests multigenic mechanisms underlie sub-phenotype-specific causes and presentations of cardiomyopathy. These results identify important information about the genetic architecture of pediatric cardiomyopathy and support recommendations for clinical genetic testing in children while illustrating differences in genetic architecture by age, ancestry, and sub-phenotype and providing rationale for larger studies to investigate multigenic contributions.

A big data approach to evaluate receipt of optimal care in childhood cerebral palsy.

PURPOSE: Through automated electronic health record (EHR) data extraction and analysis, this project systematically quantified actual care delivery for children with cerebral palsy (CP) and evaluated alignment with current evidence-based recommendations. METHODS: Utilizing EHR data for over 8000 children with CP, we developed an approach to define and quantify receipt of optimal care, and pursued proof-of-concept with two children with unilateral CP, Gross Motor Function Classification System (GMFCS) Level II. Optimal care was codified as a cluster of four components including physical medicine and rehabilitation (PMR) care, spasticity management, physical therapy (PT), and occupational therapy (OT). A Receipt of Care Score (ROCS) quantified the degree of adherence to recommendations and was compared with the Pediatric Outcomes Data Collection Instrument (PODCI) and Pediatric Quality of Life Inventory (PEDS QL). RESULTS: The two children (12 year old female, 13 year old male) had nearly identical PMR and spasticity component scores while PT and OT scores were more divergent. Functional outcomes were higher for the child who had higher adjusted ROCS. CONCLUSIONS: ROCSs demonstrate variation in real-world care delivered over time and differentiate between components of care. ROCSs reflect overall function and quality of life. The ROCS methods developed are novel, robust, and scalable and will be tested in a larger sample.IMPLICATIONS FOR REHABILITATIONOptimal practice, with an emphasis on integrated multidisciplinary care, can be defined and quantified utilizing evidence-based recommendations.Receipt of optimal care for childhood cerebral palsy can be scored using existing electronic health record data.Big Data approaches can contribute to the understanding of current care and inform approaches for improved care.

Mental Health Diagnoses Risk Among Children and Young Adults With Cerebral Palsy, Chronic Conditions, or Typical Development.

Importance: Mental health (MH) issues in children with cerebral palsy (CP) are poorly understood compared with other pediatric populations. Objective: To examine MH diagnosis code assignment among children and young adults with CP and compare with typically developing (TD) and chronic condition (CC) pediatric populations. Design, Setting, and Participants: This case-control study used International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes to create a CP case set and CC and TD control sets using electronic health record data of children and young adults from a large tertiary care children's hospital in the midwestern United States between 2010 and 2022. Case-control matching was performed to control for demographic factors. Data were analyzed from June to December 2023. Exposures: All MH diagnosis codes were mapped to ICD-10-CM and categorized using Clinical Classifications Software Refined (CCSR). Main Outcomes and Measures: The incidence rates of MH CCSR categories were calculated. Descriptive and comparative statistics were used to evaluate the significance and odds associated with factors. Results: Data from 216 794 individuals (mean [SD] baseline age, 4.3 [5.1] years; 118 562 [55%] male) were analyzed, including 3544 individuals with CP, 142 160 individuals with CC, and 71 080 TD individuals. The CP cohort spread across Gross Motor Function Classification System (GMFCS) levels I (981 individuals [28%]), II (645 individuals [18%]), III (346 individuals [10%]), IV (502 individuals [14%]), and V (618 individuals [17%]). Rates varied significantly for anxiety (824 individuals with CP [23%]; 25 877 individuals with CC [9%]; 6274 individuals with TD [18%]), attention-deficit/hyperactivity disorder (534 individuals with CP [15%]; 22 426 individuals with CC [9%]; 6311 individuals with TD [16%]); conduct or impulse disorder (504 individuals with CP [14%]; 13 209 individuals with CC [5%]; 3715 individuals with TD [9%]), trauma or stress disorders (343 individuals with CP [10%]; 18 229 individuals with CC [8%]; 5329 individuals with TD [13%]), obsessive-compulsive disorder (251 individuals with CP [7%]; 3795 individuals with CC [1%]; 659 individuals with TD [3%]), depression (108 individuals with CP [3%]; 12 224 individuals with CC [5%]; 4007 individuals with TD [9%]), mood disorders (74 individuals with CP [2%]; 4355 individuals with CC [2%]; 1181 individuals with TD [3%]), and suicidal ideation (72 individuals with CP [2%]; 7422 individuals with CC [5%]; 3513 individuals with TD [5%]). There was significant variation in odds of MH diagnoses by GMFCS level (I-II vs III-V: odds ratio [OR], 1.23; 95% CI, 1.09-1.40; P = .001). Among individuals with CP, males were more likely than females to have diagnosis codes for conduct or impulse disorders (OR, 1.41; 95% CI, 1.16-1.73) and attention-deficit/hyperactivity disorder (OR, 1.41 [95% CI, 1.15-1.73]). Black individuals, compared with White individuals, were more likely to have diagnoses for obsessive-compulsive disorder (OR, 1.57 [95% CI, 1.14-2.16]), other mood disorders (OR, 1.85 [95% CI, 1.01-3.38]), and trauma or stress disorders (OR, 1.94 [95% CI, 1.44-2.63]). Odds for trauma or stress disorders were elevated for individuals who identified as other races compared with White individuals (OR, 2.80 [95% CI, 2.03-3.87]). Conclusions and Relevance: In this case-control study of children and young adults with CP and matched comparisons, anxiety and conduct or impulse diagnoses were higher in individuals with CP. The lower diagnosis rates of depression and suicidal ideation may indicate underdiagnosis among individuals with CP. There is likely a need for assessment tools that are more suitable for children with CP.

Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic.

Importance: The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents. Objective: To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic. Design, Setting, and Participants: This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children's hospitals in the US and France. Main Outcomes and Measures: Change in the monthly proportion of mental health condition-associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis. Results: There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5%] female) and 11 101 during the pandemic period (7603 [68.5%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55%; 95% CI, 0.26%-0.84%), depression (0.50%; 95% CI, 0.19%-0.79%), and suicidality or self-injury (0.38%; 95% CI, 0.08%-0.68%). There was an estimated 0.60% increase (95% CI, 0.31%-0.89%) overall in the monthly proportion of mental health-associated hospitalizations following onset of the pandemic compared with the prepandemic period. Conclusions and Relevance: In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children's hospitals to care for adolescents with mental health conditions during the pandemic and beyond.

Implicating Gene and Cell Networks Responsible for Differential COVID-19 Host Responses via an Interactive Single Cell Web Portal.

Numerous studies have provided single-cell transcriptome profiles of host responses to SARS-CoV-2 infection. Critically lacking however is a datamine that allows users to compare and explore cell profiles to gain insights and develop new hypotheses. To accomplish this, we harmonized datasets from COVID-19 and other control condition blood, bronchoalveolar lavage, and tissue samples, and derived a compendium of gene signature modules per cell type, subtype, clinical condition, and compartment. We demonstrate approaches to probe these via a new interactive web portal (http://toppcell.cchmc.org/COVID-19). As examples, we develop three hypotheses: (1) a multicellular signaling cascade among alternatively differentiated monocyte-derived macrophages whose tasks include T cell recruitment and activation; (2) novel platelet subtypes with drastically modulated expression of genes responsible for adhesion, coagulation and thrombosis; and (3) a multilineage cell activator network able to drive extrafollicular B maturation via an ensemble of genes strongly associated with risk for developing post-viral autoimmunity.

An interactive single cell web portal identifies gene and cell networks in COVID-19 host responses.

Numerous studies have provided single-cell transcriptome profiles of host responses to SARS-CoV-2 infection. Critically lacking however is a data mine that allows users to compare and explore cell profiles to gain insights and develop new hypotheses. To accomplish this, we harmonized datasets from COVID-19 and other control condition blood, bronchoalveolar lavage, and tissue samples, and derived a compendium of gene signature modules per cell type, subtype, clinical condition, and compartment. We demonstrate approaches to interacting with, exploring, and functional evaluating these modules via a new interactive web portal ToppCell (http://toppcell.cchmc.org/). As examples, we develop three hypotheses: (1) alternatively-differentiated monocyte-derived macrophages form a multicelllar signaling cascade that drives T cell recruitment and activation; (2) COVID-19-generated platelet subtypes exhibit dramatically altered potential to adhere, coagulate, and thrombose; and (3) extrafollicular B maturation is driven by a multilineage cell activation network that expresses an ensemble of genes strongly associated with risk for developing post-viral autoimmunity.

Genetic Causes of Cardiomyopathy in Children: First Results From the Pediatric Cardiomyopathy Genes Study.

Background Pediatric cardiomyopathy is a genetically heterogeneous disease with substantial morbidity and mortality. Current guidelines recommend genetic testing in children with hypertrophic, dilated, or restrictive cardiomyopathy, but practice variations exist. Robust data on clinical testing practices and diagnostic yield in children are lacking. This study aimed to identify the genetic causes of cardiomyopathy in children and to investigate clinical genetic testing practices. Methods and Results Children with familial or idiopathic cardiomyopathy were enrolled from 14 institutions in North America. Probands underwent exome sequencing. Rare sequence variants in 37 known cardiomyopathy genes were assessed for pathogenicity using consensus clinical interpretation guidelines. Of the 152 enrolled probands, 41% had a family history of cardiomyopathy. Of 81 (53%) who had undergone clinical genetic testing for cardiomyopathy before enrollment, 39 (48%) had a positive result. Genetic testing rates varied from 0% to 97% between sites. A positive family history and hypertrophic cardiomyopathy subtype were associated with increased likelihood of genetic testing (P=0.005 and P=0.03, respectively). A molecular cause was identified in an additional 21% of the 63 children who did not undergo clinical testing, with positive results identified in both familial and idiopathic cases and across all phenotypic subtypes. Conclusions A definitive molecular genetic diagnosis can be made in a substantial proportion of children for whom the cause and heritable nature of their cardiomyopathy was previously unknown. Practice variations in genetic testing are great and should be reduced. Improvements can be made in comprehensive cardiac screening and predictive genetic testing in first-degree relatives. Overall, our results support use of routine genetic testing in cases of both familial and idiopathic cardiomyopathy. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT01873963.