Abstract
Doubts have been raised about the value of DNA-based screening for low-prevalence monogenic conditions following reports of testing this approach using available electronic health record (EHR) as the sole phenotyping source. We hypothesized that a better model for EHR-focused examination of DNA-based screening is Cystic Fibrosis (CF) since the diagnosis is proactively sought within the healthcare system. We reviewed CFTR variants in 50,778 exomes. In 24 cases with bi-allelic pathogenic CFTR variants, there were 21 true-positives. We considered three cases “potential” false-positives due to limitations in available EHR phenotype data. This genomic screening exhibited a positive predictive value of 87.5%, negative predictive value of 99.9%, sensitivity of 95.5%, and a specificity of 99.9%. Despite EHR-based phenotyping limitations in three cases, the presence or absence of pathogenic CFTR variants has strong predictive value for CF diagnosis when EHR data is used as the sole phenotyping source. Accurate ascertainment of the predictive value of DNA-based screening requires condition-specific phenotyping beyond available EHR data.
Highlights
Attempts to model genomic screening for long QT syndrome (LQTS) and arrhythmogenic right ventricular cardiomyopathy (ARVC), using electronic health record (EHR) as the lone source of phenotypic data has raised questions about the value of genomic screening more broadly in the accurate identification of genetic disease risk in low-prevalence cohorts.[1,2,3,4]Cystic fibrosis (CF), like LQTS and ARVC, is relatively uncommon but it does not meet the NIH definition of a rare disease.[5]
Expert EHR review confirmed the diagnosis of CF in 21 individuals, who were labeled true positives (TP); CF could not be confirmed definitively in three individuals (Table 1)
Chart review concluded that the second case has CFTR-Related Metabolic Syndrome (CRMS) variant (Table 1) and was labeled a true negative (TN)
Summary
Attempts to model genomic screening for long QT syndrome (LQTS) and arrhythmogenic right ventricular cardiomyopathy (ARVC), using electronic health record (EHR) as the lone source of phenotypic data has raised questions about the value of genomic screening more broadly in the accurate identification of genetic disease risk in low-prevalence cohorts.[1,2,3,4]Cystic fibrosis (CF), like LQTS and ARVC, is relatively uncommon but it does not meet the NIH definition of a rare disease.[5]. CF is distinct from LQTS and ARVC in that there has been over 20 years of extensive proactive CF diagnostic efforts to identify cases in adult, pediatric, and newborn care. These CF diagnostic efforts, incorporating both genetic and non-genetic diagnostic testing, have led to standardized diagnostic approaches to suspected cases of CF.[9] As a result, the EHR of patients in major healthcare systems, contain a wealth of data from the diagnostic workups of patients for whom either clinical or laboratory findings have raised suspicion of the diagnosis.[10]. CF is an autosomal recessive disorder that affects one in 3200 in the United States and is caused by bi-allelic pathogenic variants (either homozygous or compound heterozygous pathogenic alleles) in the cystic fibrosis transmembrane conductance regulator (CFTR) gene.[11,12,13] The most common pathogenic CFTR allele, F508del, accounts for ~70% of known CF-causing alleles.[14]
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