Citations

Abstract The COVID-19 (coronavirus disease-19) has resulted in nationwide lockdowns, cessation of school and work, and social distancing. Reducing the negative impacts, such as weight-gain, has garnered global attention. In this dual-center prospective cohort study of 12,889 students, we assessed weight-gain in a youth during the 4-month COVID-19 lockdown, and examined the associations between weight-gain and COVID-19 induced stress, depression and self-reported physical activity, dietary habits and sedentary time. Participants gained an average of 2.6 kg (95% CI: 2.0 to 3.2 kg) for males and 2.1 kg (1.9 to 2.4 kg) for females. An increase in overweight and obese individuals as a demographic percentage by 4.5% and 2.7% and 4.8% and 3.4% in males and females respectively (P<0.001). Weight change was independently associated with severe COVID-19 induced stress, sedentary time, and depression score. Techniques to relieve COVID-19 induced stress, reduce sedentary time through increased physical activity, and improve dietary habits in youth may be warranted to help prevent and/or reverse weight-gain associated with the lockdown period of COVID-19.Registration: Registered with ChiCTR, registration no.: ChiCTR2000033514

Abstract BackgroundThe effect of angiotensin-converting enzyme inhibitor/angiotensin receptor blockers (ACEI/ARB) on the COVID-19 remains controversial from clinic evidence. MethodsThis is a retrospective, two-center case series of 198 consecutive COVID-19 patients with a history of hypertension. ResultsAmong 198 patients, 58 (29.3%) and 16 (8.1%) were on were on ARB and ACEI, respectively. Patients who were on ARB or ACEI/ARB had a significantly lower rate of severe illness and ARDS when compared with patients treated with ACEI alone or not receiving and RAAS blocker (P＜0.05). The Kaplan-Meier survival curve showed that patients with ARB in their antihypertensive regimen had a trend towards a higher survival rate when compared with individuals without ARB (adjusted hazard ratio, 0.27; 95% CI, 0.07-1.02; P = 0.054). The Cox-regression analysis to compared ACEI vs. ARB groups showed a significantly lower mortality rate in the ARB group (adjusted hazard ratio, 0.03; 95% CI, 0.00-0.58; P = 0.02). ConclusionsUsing of ARB was associated with a reduced rate of severe illness and ARDS, indicating their potential protective impact in COVID-19.

G 4 C 2 repeat–derived poly(GR) plays a key role in mediating TDP-43 mislocalization and aggregation in vitro and in vivo.

ObjectiveNonphysician health care workers are involved in high‐risk patient care during the COVID‐19 pandemic, placing them at high risk of mental health burden. The mental health impact of COVID‐19 in this crucial population has not been studied thus far. Thus, the objective of this study is to assess the psychosocial well‐being of these providers.Study DesignNational cross‐sectional online survey (no control group).SettingAcademic otolaryngology programs in the United States.Subjects and MethodsWe distributed a survey to nonphysician health care workers in otolaryngology departments across the United States. The survey incorporated a variety of validated mental health assessment tools to measure participant burnout (Mini‐Z assessment), anxiety (Generalized Anxiety Disorder–7), distress (Impact of Event Scale), and depression (Patient Health Questionnaire–2). Multivariable logistic regression analysis was performed to determine predictive factors associated with these mental health outcomes.ResultsWe received 347 survey responses: 248 (71.5%) nurses, 63 (18.2%) administrative staff, and 36 (10.4%) advanced practice providers. A total of 104 (30.0%) respondents reported symptoms of burnout; 241 (69.5%), symptoms of anxiety; 292 (84.1%), symptoms of at least mild distress; and 79 (22.8%), symptoms of depression. Upon further analysis, development of these symptoms was associated with factors such as occupation, practice setting, and case load.ConclusionFrontline otolaryngology health care providers exhibit high rates of mental health complications, particularly anxiety and distress, in the wake of COVID‐19. Adequate support systems must be put into place to address these issues.

AbstractObjectiveTo identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.Materials and MethodsUsing EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.ResultsUsing the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.ConclusionComputationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.

Aromatase inhibitors (AIs) reduce breast cancer recurrence and prolong survival, but up to 30% of patients exhibit recurrence. Using a genome-wide association study of patients entered on MA.27, a phase III randomized trial of anastrozole versus exemestane, we identified a single nucleotide polymorphism (SNP) in CUB And Sushi multiple domains 1 ( CSMD1) associated with breast cancer–free interval, with the variant allele associated with fewer distant recurrences. Mechanistically, CSMD1 regulates CYP19 expression in an SNP- and drug-dependent fashion, and this regulation is different among 3 AIs: anastrozole, exemestane, and letrozole. Overexpression of CSMD1 sensitized AI-resistant cells to anastrozole but not to the other 2 AIs. The SNP in CSMD1 that was associated with increased CSMD1 and CYP19 expression levels increased anastrozole sensitivity, but not letrozole or exemestane sensitivity. Anastrozole degrades estrogen receptor α (ERα), especially in the presence of estradiol (E2). ER + breast cancer organoids and AI- or fulvestrant-resistant breast cancer cells were more sensitive to anastrozole plus E2 than to AI alone. Our findings suggest that the CSMD1 SNP might help to predict AI response, and anastrozole plus E2 serves as a potential new therapeutic strategy for patients with AI- or fulvestrant-resistant breast cancers.

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.