Citations

Objective: To evaluate the effect of the COVID-19 pandemic on global access to care and practice patterns for children with epilepsy. Methods: We conducted a cross-sectional, online survey of pediatric neurologists across the world affiliated with the International Child Neurology Association, the Chinese Child Neurology Society, the Child Neurology Society, and the Pediatric Epilepsy Research Consortium. Results were analyzed in relation to regional burden of COVID-19 disease. Results: From April 10 to 24, 2020, a sample of 212 respondents from 49 countries indicated that the COVID-19 pandemic has dramatically changed many aspects of pediatric epilepsy care, with 91.5% reporting changes to outpatient care, 90.6% with reduced access to electroencephalography (EEG), 37.4% with altered management of infantile spasms, 92.3% with restrictions in ketogenic diet initiation, 93.4% with closed or severely limited epilepsy monitoring units, and 91.3% with canceled or limited epilepsy surgery. Telehealth use had increased, with 24.7% seeing patients exclusively via telehealth. Changes in practice were related both to COVID-19 burden and location. Conclusions: In response to COVID-19, pediatric epilepsy programs have implemented crisis standards of care that include increased telemedicine, decreased EEG use, changes in treatments of infantile spasms, and cessation of epilepsy surgery. The long-term impact of these abrupt changes merit careful study.

A 26-year-old female developed bilateral blindness after a complete remission for ALK-positive ALCL. Initial workup showed neurolymphomatosis involving the bilateral optic nerves. She underwent hematopoietic cell transplantation and she remained in complete remission after 1 year; however her vision never improved. ALCL likely led to destruction of the optic pathway.

AbstractIntroductionThe best management strategy for patients with atrial fibrillation (AF) with heart failure (HF) and preserved left ventricular ejection fraction (LVEF) is unknown.Methods and ResultsThis cohort study was conducted in Olmsted County, Minnesota, with resources of the Rochester Epidemiology Project. Patients with incident AF occurring between 2000 and 2014 with a prior or concurrent HF were included. Patients with LVEF ≥ 50% were designated as HF and preserved ejection fraction (HFpEF) and those with LVEF < 50% were designated as HF and reduced ejection fraction (HFrEF). Rhythm control in the first year after AF diagnosis was defined as prescriptions for an antiarrhythmic drug, catheter ablation, or maze procedure. The primary endpoint was all‐cause mortality. The secondary endpoints were cardiovascular death, cardiovascular hospitalization, and stroke or transient ischemic attack. Of 859 patients (age, 77.2 ± 12.1 years; 49.2%, female), 447 had HFpEF‐AF, and 412 had HFrEF‐AF. There was no difference in all‐cause mortality (10‐year mortality, 83% vs 79%; p = .54) or secondary endpoints between the HFpEF‐AF and HFrEF‐AF, respectively. Compared with the rate control strategy, rhythm control in HFpEF‐AF patients (n = 40, 15.9%) offered no survival benefits (adjusted HR, 0.70; 95% CI, 0.42–1.16; p = .16), whereas rhythm control in HFrEF‐AF patients (n = 52, 22.5%) decrease cardiovascular mortality (HR, 0.38; 95% CI, 0.17–0.86; p = .02).ConclusionsPatients with HFpEF‐AF and HFrEF‐AF had similar poor prognoses. Rhythm control strategy was seldom adopted in community care in patients with HF and AF. A rhythm control strategy may provide survival benefit for patients with HFrEF‐AF and the benefit of rhythm control in patients with HFpEF‐AF warrants further study.

AbstractIntroductionElectronic health record (EHR)‐driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time‐consuming, error‐prone, and platform‐specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high‐throughput, cross‐platform phenotyping.MethodsWe selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results.ResultsCQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross‐platform execution resulted in identical patient cohorts generated by both data platforms.ConclusionsCQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross‐platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR‐driven phenotyping and scale in learning health systems.

AbstractLarge observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

Abstract Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P &lt; 1.25 × 10−6) was observed in the meta-analysis (PGxE = 1.2 ×10−6, PJoint = 4.2 ×10−7). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.