A Global Survey of Emergency Department Responses to the COVID-19 Pandemic

Introduction: Emergency departments (ED) globally are addressing the coronavirus disease 2019 (COVID-19) pandemic with varying degrees of success. We leveraged the 17-country, Emergency Medicine Education & Research by Global Experts (EMERGE) network and non-EMERGE ED contacts to understand ED emergency preparedness and practices globally when combating the COVID-19 pandemic. Methods: We electronically surveyed EMERGE and non-EMERGE EDs from April 3–June 1, 2020 on ED capacity, pandemic preparedness plans, triage methods, staffing, supplies, and communication practices. The survey was available in English, Mandarin Chinese, and Spanish to optimize participation. We analyzed survey responses using descriptive statistics. Results: 74/129 (57%) EDs from 28 countries in all six World Health Organization global regions responded. Most EDs were in Asia (49%), followed by North America (28%), and Europe (14%). Nearly all EDs (97%) developed and implemented protocols for screening, testing, and treating patients with suspected COVID-19 infections. Sixty percent responded that provider staffing/back-up plans were ineffective. Many sites (47/74, 64%) reported staff missing work due to possible illness with the highest provider proportion of COVID-19 exposures and infections among nurses. Conclusion: Despite having disaster plans in place, ED pandemic preparedness and response continue to be a challenge. Global emergency research networks are vital for generating and disseminating large-scale event data, which is particularly important during a pandemic.

Under-specification as the Source of Ambiguity and Vagueness in Narrative Phenotype Algorithm Definitions

IntroductionCurrently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. MethodsThis study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. ResultsWe developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. Discussion & ConclusionOur findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.

Facilitating Genetics Aware Clinical Decision Support: Putting the eMERGE Infrastructure into Practice

This editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format.

A scalable EHR-based approach for phenotype discovery and variant interpretation for hereditary cancer genes

Knowledge of the clinical spectrum of rare genetic disorders helps in disease management and variant pathogenicity interpretation. Leveraging electronic health record (EHR)-linked genetic testing data from the eMERGE network, we determined the associations between a set of 23 hereditary cancer genes and 3017 phenotypes in 23544 individuals. This phenome-wide association study replicated 45% (184/406) of known gene-phenotype associations (P = 5.1 ×10-125). Meta-analysis with an independent EHR-derived cohort of 3242 patients confirmed 14 novel associations with phenotypes in the neoplastic, genitourinary, digestive, congenital, metabolic, mental and neurologic categories. Phenotype risk scores (PheRS) based on weighted aggregations of EHR phenotypes accurately predicted variant pathogenicity for at least 50% of pathogenic variants for 8/23 genes. We generated a catalog of PheRS for 7800 variants, including 5217 variants of uncertain significance, to provide empirical evidence of potential pathogenicity. This study highlights the potential of EHR data in genomic medicine.

Association between triglycerides, known risk SNVs and conserved rare variation in SLC25A40 in a multi-ancestry cohort

Background Elevated triglycerides (TG) are associated with, and may be causal for, cardiovascular disease (CVD), and co-morbidities such as type II diabetes and metabolic syndrome. Pathogenic variants in APOA5 and APOC3 as well as risk SNVs in other genes [APOE (rs429358, rs7412), APOA1/C3/A4/A5 gene cluster (rs964184), INSR (rs7248104), CETP (rs7205804), GCKR (rs1260326)] have been shown to affect TG levels. Knowledge of genetic causes for elevated TG may lead to early intervention and targeted treatment for CVD. We previously identified linkage and association of a rare, highly conserved missense variant in SLC25A40, rs762174003, with hypertriglyceridemia (HTG) in a single large family, and replicated this association with rare, highly conserved missense variants in a European American and African American sample. Methods Here, we analyzed a longitudinal mixed-ancestry cohort (European, African and Asian ancestry, N = 8966) from the Electronic Medical Record and Genomics (eMERGE) Network. We tested associations between median TG and the genes of interest, using linear regression, adjusting for sex, median age, median BMI, and the first two principal components of ancestry. Results We replicated the association between TG and APOC3, APOA5, and risk variation at APOE, APOA1/C3/A4/A5 gene cluster, and GCKR. We failed to replicate the association between rare, highly conserved variation at SLC25A40 and TG, as well as for risk variation at INSR and CETP. Conclusions Analysis using data from electronic health records presents challenges that need to be overcome. Although large amounts of genotype data is becoming increasingly accessible, usable phenotype data can be challenging to obtain. We were able to replicate known, strong associations, but were unable to replicate moderate associations due to the limited sample size and missing drug information.

Abstract 15209: LPA Variants Are Associated With Aortic Valve Stenosis, Heart Failure and Chronic Kidney Disease

Background: The pathophysiology of lipoprotein (a) [Lp(a)] remains obscure. We conducted a phenome wide analysis study (PheWAS) to identify pleiotropic effects of LPA variants. Methods: Among 51,843 European-ancestry participants (age 58±16 years, 54% female) of the electronic MEdical Records and Genomics (eMERGE) network we assessed whether LPA variants exhibited pleiotropic affects through an electronic health record-based PheWAS. We adjusted significant associations by presence of atherosclerotic cardiovascular disease (ASCVD; defined as a composite of coronary heart disease, cerebrovascular disease, and peripheral artery disease) to determine whether those associations were independent of ASCVD. Results: In PheWAS analysis, we tested 864 phecodes with 36 independent LPA variants. We identified 21 significant associations with non-ASCVD phenotypes including heart failure, aortic valve stenosis, and chronic kidney disease of which 14 were replicated across independent cohorts. The strength of associations between the LPA variant rs10455872 and both heart failure and aortic valve stenosis related phecodes were significantly attenuated after adjustment for ASCVD. However, the association with chronic kidney disease phecode remained independent following adjustment for ASCVD without any attenuation in the estimated odds ratio (Table). Conclusions: LPA genetic variants were associated with aortic valve stenosis, heart failure and chronic kidney disease. The observed association of LPA variant rs10455872 with aortic stenosis and heart failure appear to be partially mediated by ASCVD, while the association with chronic kidney disease appears to be independent of ASCVD. Additional studies are needed to elucidate potential mechanisms by which Lp(a) may contribute to the pathogenesis of non-ASCVD disease phenotypes.

Generalizability of Polygenic Risk Scores for Breast Cancer in the Multiethnic eMERGE Study

Background: The majority of polygenic risk scores (PRS) for breast cancer have been developed and validated using cohorts of European ancestry (EA). Less is known about the generalizability of these PRS in other ancestral groups. Methods: The Electronic Medical Records and Genomics (eMERGE) network cohort dataset was used to evaluate the performance of seven previously developed PRS (three EA-based PRSs, and four non-EA based PRSs) in three major ancestral groups. Each PRS was separately evaluated in EA (cases: 3939; controls: 28840), African ancestry (AA) (cases: 121; controls: 1173) and self-reported LatinX ancestry (LA) (cases: 92; controls: 1363) women. We assessed the association between breast cancer risk and each PRS, adjusting forage, study site, breast cancer family history, and first three ancestry informative principal components. Results: EA-based PRSs were significantly associated with breast cancer risk in EA women per one SD increase (odds ratio [OR]=1.45, 95% confidence interval [CI]=1.40-1.51), and LA women (OR=1.41, 95% CI=1.13-1.77), but not AA women (OR=1.13, 95% CI=0.92-1.40). There was no statistically significant association for the non-EA PRSs in all ancestry groups, including an LA-based PRS and an AA-based PRS. Conclusion: We evaluated EA-derived PRS for estimating breast cancer risk using the eMERGE dataset and found they generalized well in LA women but not in AA women. For non-EA based PRSs, we did not replicate previously reported associations for the respective ancestries in the eMERGE cohort. Our results highlight the need to improve representation of diverse population groups, particularly AA women, in research cohorts.