The role of novel COQ8B mutations in glomerulopathy and related kidney defects.

BACKGROUND AND OBJECTIVES: Glomerulopathies affect kidney glomeruli and can lead to end-stage renal disease if untreated. Clinical and experimental evidence have identified numerous (>20) genetic mutations in the mitochondrial coenzyme Q8B protein (COQ8B) primarily associated with nephrotic syndrome. Yet, little else is understood about COQ8B activity in renal pathogenesis and its role in mitochondrial dysfunction. We identified additional novel COQ8B mutations in a glomerulopathy patient and aimed to define the potential structural and functional defects of COQ8B mutations. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: Whole exome sequencing was performed on a Hispanic female presenting with proteinuria. Novel mutations in the COQ8B gene were identified. The effects of mutation on protein function, mitochondrial morphology, and disease progression were investigated by histopathology, transmission electron microscopy, homology modeling, and in silico structural analysis. RESULTS: We have characterized the pathophysiology of novel COQ8B mutations, compound heterozygous for two alterations c.1037T>G (p.I346S), and c.1560G>A (p.W520X), in the progression of proteinuria in a Hispanic female. Histopathology revealed defects in podocyte structure and mitochondrial morphology. In silico and computation analyses highlight possible structural origins of COQ8B dysfunction in the presence of mutations. CONCLUSIONS: Novel mutations in COQ8B present promising biomarkers for the early detection and therapeutic targeting of mitochondrial glomerulopathy. Insights from structural modeling suggest roles of mutation-dependent alterations in COQ8B allosteric regulation, protein folding, or stability in renal pathogenesis.

Association of genetic mutations and loss of ambulation in childhood-onset dystrophinopathy.

BACKGROUND: Quantifying associations between genetic mutations and loss of ambulation (LoA) among males diagnosed with childhood-onset dystrophinopathy is important for understanding variation in disease progression and may be useful in clinical trial design. METHODS: Genetic and clinical data from the Muscular Dystrophy Surveillance, Tracking, and Research Network for 358 males born and diagnosed from 1982 to 2011 were analyzed. LoA was defined as the age at which independent ambulation ceased. Genetic mutations were defined by overall type (deletion/duplication/point mutation) and among deletions, those amenable to exon-skipping therapy (exons 8, 20, 44-46, 51-53) and another group. Cox proportional hazards regression modeling was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Mutation type did not predict time to LoA. Controlling for corticosteroids, Exons 8 (HR = 0.22; 95% CI = 0.08, 0.63) and 44 (HR = 0.30; 95% CI = 0.12, 0.78) were associated with delayed LoA compared to other exon deletions. CONCLUSIONS: Delayed LoA in males with mutations amenable to exon-skipping therapy is consistent with previous studies. These findings suggest that clinical trials including exon 8 and 44 skippable males should consider mutation information prior to randomization.

Comorbidities Among Young Adults with Congenital Heart Defects: Results from the Congenital Heart Survey To Recognize Outcomes, Needs, and well-beinG - Arizona, Arkansas, and Metropolitan Atlanta, 2016-2019.

An estimated 1.4 million adults in the United States live with congenital heart defects (CHDs), yet their health outcomes are not well understood (1). Using self-reported, cross-sectional data from 1,482 respondents in the 2016-2019 Congenital Heart Survey To Recognize Outcomes, Needs, and well-beinG (CH STRONG) (2), CDC and academic partners estimated the prevalence of comorbidities among adults with CHDs aged 20-38 years born in Arizona (AZ), Arkansas (AR), and metropolitan Atlanta, Georgia (GA) compared with the general population (aged 20-38 years) from the National Health and Nutrition Examination Survey (NHANES) during 2015-2018 (3) and the AZ, AR, and GA Behavioral Risk Factor Surveillance Systems (BRFSS) during 2016-2018 (4). Adults with CHDs were more likely than those in the general population to report cardiovascular comorbidities, such as a history of congestive heart failure (4.3% versus 0.2%) and stroke (1.4% versus 0.3%), particularly those with severe CHDs (2). Adults with CHDs were more likely to report current depressive symptoms (15.1% versus 8.5%), but less likely to report previous diagnoses of depression (14.2% versus 22.6%), asthma (12.7% versus 16.9%), or rheumatologic disease (3.2% versus 8.0%). Prevalence of noncardiovascular comorbidities was similar between adults whose CHD was considered severe and those with nonsevere CHDs. Public health practitioners and clinicians can encourage young adults with CHDs to seek appropriate medical care to help them live as healthy a life as possible.

Automated Extraction of Diagnostic Criteria From Electronic Health Records for Autism Spectrum Disorders: Development, Evaluation, and Application.

BACKGROUND: Electronic health records (EHRs) bring many opportunities for information utilization. One such use is the surveillance conducted by the Centers for Disease Control and Prevention to track cases of autism spectrum disorder (ASD). This process currently comprises manual collection and review of EHRs of 4- and 8-year old children in 11 US states for the presence of ASD criteria. The work is time-consuming and expensive. OBJECTIVE: Our objective was to automatically extract from EHRs the description of behaviors noted by the clinicians in evidence of the diagnostic criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM). Previously, we reported on the classification of entire EHRs as ASD or not. In this work, we focus on the extraction of individual expressions of the different ASD criteria in the text. We intend to facilitate large-scale surveillance efforts for ASD and support analysis of changes over time as well as enable integration with other relevant data. METHODS: We developed a natural language processing (NLP) parser to extract expressions of 12 DSM criteria using 104 patterns and 92 lexicons (1787 terms). The parser is rule-based to enable precise extraction of the entities from the text. The entities themselves are encompassed in the EHRs as very diverse expressions of the diagnostic criteria written by different people at different times (clinicians, speech pathologists, among others). Due to the sparsity of the data, a rule-based approach is best suited until larger datasets can be generated for machine learning algorithms. RESULTS: We evaluated our rule-based parser and compared it with a machine learning baseline (decision tree). Using a test set of 6636 sentences (50 EHRs), we found that our parser achieved 76% precision, 43% recall (ie, sensitivity), and >99% specificity for criterion extraction. The performance was better for the rule-based approach than for the machine learning baseline (60% precision and 30% recall). For some individual criteria, precision was as high as 97% and recall 57%. Since precision was very high, we were assured that criteria were rarely assigned incorrectly, and our numbers presented a lower bound of their presence in EHRs. We then conducted a case study and parsed 4480 new EHRs covering 10 years of surveillance records from the Arizona Developmental Disabilities Surveillance Program. The social criteria (A1 criteria) showed the biggest change over the years. The communication criteria (A2 criteria) did not distinguish the ASD from the non-ASD records. Among behaviors and interests criteria (A3 criteria), 1 (A3b) was present with much greater frequency in the ASD than in the non-ASD EHRs. CONCLUSIONS: Our results demonstrate that NLP can support large-scale analysis useful for ASD surveillance and research. In the future, we intend to facilitate detailed analysis and integration of national datasets.

Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.

OBJECTIVE: Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with increasing prevalence. METHODS: We use unstructured data from the Centers for Disease Control and Prevention (CDC) surveillance records labeled by a CDC-trained clinician with ASD A1-3 and B1-4 criterion labels per sentence and with ASD cases labels per record using Diagnostic and Statistical Manual of Mental Disorders (DSM5) rules. One rule-based and three deep ML algorithms and six ensembles were compared and evaluated using a test set with 6773 sentences (N = 35 cases) set aside in advance. Criterion and case labeling were evaluated for each ML algorithm and ensemble. Case labeling outcomes were compared also with seven traditional tests. RESULTS: Performance for criterion labeling was highest for the hybrid BiLSTM ML model. The best case labeling was achieved by an ensemble of two BiLSTM ML models using a majority vote. It achieved 100% precision (or PPV), 83% recall (or sensitivity), 100% specificity, 91% accuracy, and 0.91 F-measure. A comparison with existing diagnostic tests shows that our best ensemble was more accurate overall. CONCLUSIONS: Transparent ML is achievable even with small datasets. By focusing on intermediate steps, deep ML can provide transparent decisions. By leveraging data redundancies, ML errors at the intermediate level have a low impact on final outcomes.

Multi-center implementation of rapid whole genome sequencing provides additional evidence of its utility in the pediatric inpatient setting.

Objective: Multi-center implementation of rapid whole genome sequencing with assessment of the clinical utility of rapid whole genome sequencing (rWGS), including positive, negative and uncertain results, in admitted infants with a suspected genetic disease. Study design: rWGS tests were ordered at eight hospitals between November 2017 and April 2020. Investigators completed a survey of demographic data, Human Phenotype Ontology (HPO) terms, test results and impacts of results on clinical care. Results: A total of 188 patients, on general hospital floors and intensive care unit (ICU) settings, underwent rWGS testing. Racial and ethnic characteristics of the tested infants were broadly representative of births in the country at large. 35% of infants received a diagnostic result in a median of 6 days. The most common HPO terms for tested infants indicated an abnormality of the nervous system, followed by the cardiovascular system, the digestive system, the respiratory system and the head and neck. Providers indicated a major change in clinical management because of rWGS for 32% of infants tested overall and 70% of those with a diagnostic result. Also, 7% of infants with a negative rWGS result and 23% with a variant of unknown significance (VUS) had a major change in management due to testing. Conclusions: Our study demonstrates that the implementation of rWGS is feasible across diverse institutions, and provides additional evidence to support the clinical utility of rWGS in a demographically representative sample of admitted infants and includes assessment of the clinical impact of uncertain rWGS results in addition to both positive and negative results.

DMD Gene and Dystrophinopathy Phenotypes Associated With Mutations: A Systematic Review for Clinicians.

ABSTRACT: The diagnosis of Duchenne and Becker muscular dystrophy (DBMD) is made by genetic testing in approximately 95% of cases. Although specific mutations can be associated with skeletal muscle phenotype, pulmonary and cardiac comorbidities (leading causes of death in Duchenne) have not been associated with Duchenne muscular dystrophy mutation type or location and vary within families. Therefore, identifying predictors for phenotype severity beyond frameshift prediction is important clinically. We performed a systematic review assessing research related to genotype-phenotype correlations in DBMD. While there are severity differences across the spectrum and within mild and severe forms of DBMD, few protective or exacerbating mutations within the dystrophin gene were reported. Except for intellectual disability, clinical test results reporting genotypic information are insufficient for clinical prediction of severity and comorbidities and the predictive validity is too low to be useful when advising families. Including expanded information coupled with proposed severity predictions in clinical genetic reports for DBMD is critical for improving anticipatory guidance.

The International SCN8A Patient Registry: A Scientific Resource to Advance the Understanding and Treatment of a Rare Pediatric Neurodevelopmental Syndrome.

Genetic variants in the SCN8A gene underlie a wide spectrum of neurodevelopmental phenotypes that range from severe epileptic encephalopathy to benign familial infantile epilepsy to neurodevelopmental delays with or without seizures. A host of additional comorbidities also contribute to the phenotypic spectrum. As a result of the recent identification of the genetic etiology and the length of time it often takes to diagnose patients, little data are available on the natural history of these conditions. The International SCN8A Patient Registry was developed in 2015 to fill gaps in understanding the spectrum of the disease and its natural history, as well as the lived experiences of individuals with SCN8A syndrome. Another goal of the registry is to collect longitudinal data from participants on a regular basis. In this article, we describe the construction and structure of the International SCN8A Patient Registry, present the type of information available, and highlight particular analyses that demonstrate how registry data can provide insights into the clinical management of SCN8A syndrome.

Reproductive Health Counseling and Concerns Among Women with Congenital Heart Defects With and Without Disabilities.

Background: Women with disabilities are less likely to receive reproductive health counseling than women without disabilities. Yet, little is known about reproductive health counseling and concerns among women with congenital heart defects (CHD) and disabilities. Methods: We used population-based survey data from 778 women aged 19 to 38 years with CHD to examine contraceptive and pregnancy counseling and pregnancy concerns and experiences by disability status, based on six validated questions on vision, hearing, mobility, cognition, self-care, and living independently. Multivariable Poisson regression was used to examine adjusted prevalence ratios between disability status and each outcome, adjusted for CHD severity, age, race/ethnicity, place of birth (Arkansas, Arizona, Georgia), and insurance type. Results: Women with disabilities (n = 323) were 1.4 and 2.3 times more likely than women without disabilities (n = 455) to receive clinician counseling on safe contraceptive methods and avoiding pregnancy because of their CHD. Women with CHD and disabilities, compared to those without disabilities, were more likely to be concerned about their ability to have children (aPR = 1.2) and to have delayed or avoided pregnancy (aPR = 2.2); they were less likely to have ever been pregnant (aPR = 0.7). Associations differed slightly across specific disability types. All associations remained after excluding 71 women with chromosomal anomalies. Conclusion: Among women with CHD, reproductive counseling, concerns, and experiences differ by disability status.

Statewide county-level autism spectrum disorder prevalence estimates-seven U.S. states, 2018.

PURPOSE: Autism spectrum disorder (ASD) prevalence information is necessary for identifying community needs such as addressing disparities in identification and services. METHODS: Seven Autism and Developmental Disabilities Monitoring (ADDM) Network sites participated in a pilot project to link statewide health and education data to generate statewide and county-level prevalence estimates for a broader age range for their states for the first time. RESULTS: Statewide prevalence of ASD for ages 3-21 years in 2018 ranged from 1.5% in Tennessee and Wisconsin to 2.3% in Arizona. The median county-level prevalence of ASD was 1.4% of residents ages 3-21 years. More boys than girls had ASD at all sites, and prevalence was lower among non-Hispanic Black, Hispanic, Asian/Pacific Islander, and American Indian/Alaska Native residents compared to non-Hispanic White residents at most sites. ASD prevalence estimates for children aged 8 years were similar to 2018 ADDM Network estimates that used record review to provide more in-depth information, but showed greater variation for children aged 4 years. CONCLUSIONS: Linkage of statewide data sets provides less detailed but actionable local information when more resource-intensive methods are not possible.

Missense variants in TMEM67 in a patient with Joubert syndrome.

We present a patient with a clinical diagnosis of Joubert syndrome with COACH phenotype who carries two TMEM67 variants of uncertain significance (VUS). One VUS can be reclassified as "likely pathogenic" by adding clinical data. As genetic testing becomes more accessible, more VUS will require clinical correlation for accurate classification.

Optimizing Corpus Creation for Training Word Embedding in Low Resource Domains: A Case Study in Autism Spectrum Disorder (ASD).

Automating the extraction of behavioral criteria indicative of Autism Spectrum Disorder (ASD) in electronic health records (EHRs) can contribute significantly to the effort to monitor the condition. Word embedding algorithms such as Word2Vec can encode semantic meanings of words in vectors and assist in automated vocabulary discovery from EHRs. However, text available for training word embeddings for ASD is miniscule compared to the billions of tokens typically used. We evaluate the importance of corpus specificity versus size and hypothesize that for specific domains small corpora can generate excellent word embeddings. We custom-built 6 ASD-themed corpora (N=4482), using ASD EHRs and abstracts from PubMed (N=39K) and PsychInfo (N=69K) and evaluated them. We were able to generate the most useful 200-dimension embeddings based on the small ASD EHR data. Due to diversity in its vocabulary, the abstract-based embeddings generated fewer related terms and saw minimal improvement when the size of the corpus increased.

Postfolate spina bifida lesion level change.

BACKGROUND: Spina bifida accounts for a large proportion of birth defects in the United States. Studies have evaluated the decrease in prevalence at birth after folate fortification of food grains, but little is known about neurologic functional changes related to fortification. This study assesses the functional level of lesions in the prefortification and postfortification eras. METHODS: Data were collected through retrospective review of medical records from a regional multispecialty clinic in Arizona. This study included individuals born between 1981-1995 (prefortification) and 1999-2013 (postfortification). Patients were included if they had a primary diagnosis of spina bifida with or without hydrocephalus. RESULTS: There was a significant difference in functional lesion level with an 85% reduction in thoracic level lesions in the postfortification era (p < .005). There were no differences in gender or ethnicity across eras; however, Hispanic ethnicity had a higher number of cases overall (51.7%). The most common lesion level in both eras was mid-lumbar, accounting for 35.7 and 34.4% of cases in the prefolate and postfolate eras, respectively. CONCLUSIONS: This study demonstrates a significant difference in the distribution of lesion level of spina bifida patients born in the postfortification era, based on neurologic function. Further research with a larger sample size is needed to determine if this observation holds true nationally.

Recognition of clinical characteristics for population-based surveillance of fetal alcohol syndrome.

BACKGROUND: The diagnosis of fetal alcohol syndrome (FAS) rests on identification of characteristic facial, growth, and central nervous system (CNS) features. Public health surveillance of FAS depends on documentation of these characteristics. We evaluated if reporting of FAS characteristics is associated with the type of provider examining the child. METHODS: We analyzed cases aged 7-9 years from the Fetal Alcohol Syndrome Surveillance Network II (FASSNetII). We included cases whose surveillance records included the type of provider (qualifying provider: developmental pediatrician, geneticist, neonatologist; other physician; or other provider) who evaluated the child as well as the FAS diagnostic characteristics (facial dysmorphology, CNS impairment, and/or growth deficiency) reported by the provider. RESULTS: A total of 345 cases were eligible for this analysis. Of these, 188 (54.5%) had adequate information on type of provider. Qualifying physicians averaged more than six reported FAS characteristics while other providers averaged less than five. Qualifying physicians reported on facial characteristics and developmental delay more frequently than other providers. Also, qualifying physicians reported on all three domains of characteristics (facial, CNS, and growth) in 97% of cases while others reported all three characteristics on two thirds of cases. CONCLUSIONS: Documentation in medical records during clinical evaluations for FAS is lower than optimal for cross-provider communication and surveillance purposes. Lack of documentation limits the quality and quantity of information in records that serve as a major source of data for public health surveillance systems.