Given the severe and rapid impact of COVID-19, the pace of information sharing has been accelerated. However, traditional methods of disseminating and digesting medical information can be time-consuming and cumbersome. In a pilot study, the authors used social listening to quickly extract information from social media channels to explore what people with COVID-19 are talking about regarding symptoms and disease progression. The goal was to determine whether, by amplifying patient voices, new information could be identified that might have been missed through other sources. Two data sets from social media groups of people with or presumed to have COVID-19 were analyzed: a Facebook group poll, and conversation data from a Reddit group including detailed disease natural history-like posts. Content analysis and a customized analytics engine that incorporates machine learning and natural language processing were used to quickly identify symptoms mentioned. Key findings include more than 20 symptoms in the data sets that were not listed in online lists of symptoms from 4 respected medical information sources. The disease natural history-like posts revealed that people can experience symptoms for many weeks and that some symptoms change over time. This study demonstrates that social media can offer novel insights into patient experiences as a source of real-world data. This inductive research approach can quickly generate descriptive information that can be used to develop hypotheses and new research questions. Also, the method allows rapid assessments of large numbers of social media conversations that could be applied to monitor public health for emerging and rapidly spreading diseases such as COVID-19.

Purpose: To assess clinical chromosomal microarray (CMA) genomic testing reports for the following: (a) usage of reporting elements consistent with 2011 ACMG guidelines and other elements identified in the primary literature, (b) information quality, and (c) readability.Methods: We retrospectively analyzed genomic testing reports from 2011 to 2016 provided to, or by our laboratory to aid in clinical detection and interpretation of copy number variants. Analysis was restricted to the following sections: interpretation, recommendations, limitations, and citations. Analysis included descriptive characteristics, reporting elements, reading difficulty using the Simple Measure of Gobbledygook (SMOG), and quality ratings using a subset of questions adapted from the DISCERN‐Genetics questionnaire.Results: The analysis included 44 unique reports from 26 laboratories comprising four groups: specialty laboratories (SL; N = 9), reference laboratories (RL; N = 12), hospital laboratories (HL; N = 10), and university‐based laboratories (UL; N = 13). There were 23 abnormal/pathogenic reports and 21 of uncertain/unknown significance. Nine laboratories did not include one or more pieces of information based on ACMG guidelines; only one of ten laboratories reported condition‐specific management/treatment information when available and relevant. Average quality ratings and readability scores were not significantly different between laboratory types or result classification.Conclusions: Reporting practices for most report elements varied widely; however, readability and quality did not differ significantly between laboratory types. Management and treatment information, even for well‐known conditions, are rarely included. Effectively communicating test results may be improved if certain reporting elements are incorporated. Recommendations to improve laboratory reports are provided.

Introduction: Chromosomal microarray analysis (CMA) is recognized as the first-tier test in the genetic evaluation of children with developmental delays, intellectual disabilities, congenital anomalies and autism spectrum disorders of unknown etiology. Array Design: To optimize detection of clinically relevant copy number variants associated with these conditions, we designed a whole-genome microarray, FirstStep^Dx PLUS (FSDX). A set of 88,435 custom probes was added to the Affymetrix CytoScanHD platform targeting genomic regions strongly associated with these conditions. This combination of 2,784,985 total probes results in the highest probe coverage and clinical yield for these disorders. Results and Discussion: Clinical testing of this patient population is validated on DNA from either non-invasive buccal swabs or traditional blood samples. In this report we provide data demonstrating the analytic and clinical validity of FSDX and provide an overview of results from the first 7,570 consecutive patients tested clinically. We further demonstrate that buccal sampling is an effective method of obtaining DNA samples, which may provide improved results compared to traditional blood sampling for patients with neurodevelopmental disorders who exhibit somatic mosaicism.

Autism spectrum disorder (ASD) is a heterogeneous condition with a complex genetic etiology. The objective of this study is to identify the complex genetic factors that underlie the ASD phenotype and other clinical features of Professor Temple Grandin, an animal scientist and woman with high-functioning ASD. Identifying the underlying genetic cause for ASD can impact medical management, personalize services and treatment, and uncover other medical risks that are associated with the genetic diagnosis. Prof. Grandin underwent chromosomal microarray analysis, whole exome sequencing, and whole genome sequencing, as well as a comprehensive clinical and family history intake. The raw data were analyzed in order to identify possible genotype-phenotype correlations. Genetic testing identified variants in three genes (SHANK2, ALX1, and RELN) that are candidate risk factors for ASD. We identified variants in MEFV and WNT10A, reported to be disease-associated in previous studies, which are likely to contribute to some of her additional clinical features. Moreover, candidate variants in genes encoding metabolic enzymes and transporters were identified, some of which suggest potential therapies. This case report describes the genomic findings in Prof. Grandin and it serves as an example to discuss state-of-the-art clinical diagnostics for individuals with ASD, as well as the medical, logistical, and economic hurdles that are involved in clinical genetic testing for an individual on the autism spectrum.