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IntroductionWe explored sex and race differences in the prognostic implications of QRS prolongation among healthy adults.MethodsParticipants from the Dallas Heart Study (DHS) free of cardiovascular (CV) disease who underwent ECG testing and cMRI evaluation were included. Multivariable linear regression was used to examine the cross-sectional association of QRS duration with left ventricular (LV) mass, LV ejection fraction (LVEF), and LV end diastolic volume (LVEDV). Association of QRS duration with risk of MACE was evaluated using Cox models. Interaction testing was performed between QRS duration and sex/race respectively for each outcome of interest. QRS duration was log transformed.ResultsThe study included 2,785 participants. Longer QRS duration was associated with higher LV mass, lower LVEF, and higher LVEDV, independent of CV risk factors ([β: 0.21, PDiscussionIn healthy adults, QRS duration is differentially associated with abnormalities in LV structure and function. These findings inform the use of QRS duration in identifying subgroups at risk for CV disease, and caution against using QRS duration cut offs uniformly for clinical decision making.What is known?QRS prolongation in healthy adults is associated with higher risk of death, cardiovascular disease, and left ventricular hypertrophy.What the study adds?QRS prolongation may reflect a higher degree of underlying LV hypertrophy in Blacks compared to Whites. Longer QRS interval may reflect higher risk of adverse cardiac events, driven by prevalent cardiovascular risk factors.Graphic AbstractRisk of underlying left ventricular hypertrophy in demographic groups based on QRS prolongation

Background Emerging evidence links acute kidney injury (AKI) in patients with COVID‐19 with higher mortality and respiratory morbidity, but the relationship of AKI with cardiovascular disease outcomes has not been reported in this population. We sought to evaluate associations between chronic kidney disease (CKD), AKI, and mortality and cardiovascular outcomes in patients hospitalized with COVID‐19. Methods and Results In a large multicenter registry including 8574 patients with COVID‐19 from 88 US hospitals, data were collected on baseline characteristics and serial laboratory data during index hospitalization. Primary exposure variables were CKD (categorized as no CKD, CKD, and end‐stage kidney disease) and AKI (classified into no AKI or stages 1, 2, or 3 using a modification of the Kidney Disease Improving Global Outcomes guideline definition). The primary outcome was all‐cause mortality. The key secondary outcome was major adverse cardiac events, defined as cardiovascular death, nonfatal stroke, nonfatal myocardial infarction, new‐onset nonfatal heart failure, and nonfatal cardiogenic shock. CKD and end‐stage kidney disease were not associated with mortality or major adverse cardiac events after multivariate adjustment. In contrast, AKI was significantly associated with mortality (stage 1 hazard ratio [HR], 1.72 [95% CI, 1.46–2.03]; stage 2 HR, 1.83 [95% CI, 1.52–2.20]; stage 3 HR, 1.69 [95% CI, 1.44–1.98]; versus no AKI) and major adverse cardiac events (stage 1 HR, 2.17 [95% CI, 1.74–2.71]; stage 2 HR, 2.70 [95% CI, 2.07–3.51]; stage 3 HR, 3.06 [95% CI, 2.52–3.72]; versus no AKI). Conclusions This large study demonstrates a significant association between AKI and all‐cause mortality and, for the first time, major adverse cardiovascular events in patients hospitalized with COVID‐19.

Supplemental Digital Content is available in the text., Background: Coronavirus disease 2019 (COVID-19) has led to over 1 million deaths worldwide and has been associated with cardiac complications including cardiac arrhythmias. The incidence and pathophysiology of these manifestations remain elusive. In this worldwide survey of patients hospitalized with COVID-19 who developed cardiac arrhythmias, we describe clinical characteristics associated with various arrhythmias, as well as global differences in modulations of routine electrophysiology practice during the pandemic. Methods: We conducted a retrospective analysis of patients hospitalized with COVID-19 infection worldwide with and without incident cardiac arrhythmias. Patients with documented atrial fibrillation, atrial flutter, supraventricular tachycardia, nonsustained or sustained ventricular tachycardia, ventricular fibrillation, atrioventricular block, or marked sinus bradycardia (heart rate

Background: Obesity may contribute to adverse outcomes in coronavirus disease 2019 (COVID-19). However, studies of large, broadly generalizable patient populations are lacking, and the effect of body mass index (BMI) on COVID-19 outcomes— particularly in younger adults—remains uncertain. Methods: We analyzed data from patients hospitalized with COVID-19 at 88 US hospitals enrolled in the American Heart Association’s COVID-19 Cardiovascular Disease Registry with data collection through July 22, 2020. BMI was stratified by World Health Organization obesity class, with normal weight prespecified as the reference group. Results: Obesity, and, in particular, class III obesity, was overrepresented in the registry in comparison with the US population, with the largest differences among adults ≤50 years. Among 7606 patients, in-hospital death or mechanical ventilation occurred in 2109 (27.7%), in-hospital death in 1302 (17.1%), and mechanical ventilation in 1602 (21.1%). After multivariable adjustment, classes I to III obesity were associated with higher risks of in-hospital death or mechanical ventilation (odds ratio, 1.28 [95% CI, 1.09–1.51], 1.57 [1.29–1.91], 1.80 [1.47–2.20], respectively), and class III obesity was associated with a higher risk of in-hospital death (hazard ratio, 1.26 [95% CI, 1.00–1.58]). Overweight and class I to III obese individuals were at higher risk for mechanical ventilation (odds ratio, 1.28 [95% CI, 1.09–1.51], 1.54 [1.29–1.84], 1.88 [1.52–2.32], and 2.08 [1.68–2.58], respectively). Significant BMI by age interactions were seen for all primary end points ( P -interaction70 years. Severe obesity (BMI ≥40 kg/m 2 ) was associated with an increased risk of in-hospital death only in those ≤50 years (hazard ratio, 1.36 [1.01–1.84]). In adjusted analyses, higher BMI was associated with dialysis initiation and with venous thromboembolism but not with major adverse cardiac events. Conclusions: Obese patients are more likely to be hospitalized with COVID-19, and are at higher risk of in-hospital death or mechanical ventilation, in particular, if young (age ≤50 years). Obese patients are also at higher risk for venous thromboembolism and dialysis. These observations support clear public health messaging and rigorous adherence to COVID-19 prevention strategies in all obese individuals regardless of age.

Background: The capacity of telemetry monitoring in hospitalized patients is constrained due to limited equipment and personnel. Electing for telemetry monitoring may pose a bottleneck for patients admitted through the Emergency Department (ED). This can result in increased length of stays in the ED, a parameter that is correlated with decreased quality of care due to crowding and delay in treatment. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to analyze system performance before and after an expansion of telemetry capacity. Objective: To retrospectively analyze the impact of telemetry capacity on ED wait times using enterprise data warehouse (EDW) data and queuing theory. Methods: Unused telemetry boxes were used as a proxy metric for available cardiac telemetry monitoring. The number of telemetry boxes in use at a large urban academic medical center was determined by daily manual count. The number of telemetry admissions, average length of telemetry use, and length of stay in the ED were obtained from EDW data. Using queuing theory, system parameters including expected wait time and percent of patients who will wait were calculated for scenarios to simulate before and after the expansion of telemetry capacity. These modeled system behaviors were compared to observed data from before and after the intervention to understand the utility of the mathematical model. Results: Over the study period of 365 days, a total of 13,515 inpatient non-critical care hospital encounters had orders for telemetry monitoring. Prior to the increase in telemetry capacity, there were 144 telemetry boxes, which were utilized at 96.7% of capacity. Using these parameters, the model predicts a 5.8-hour average wait time and a 42.1% chance that a patient will have to wait for a telemetry box. Increasing telemetry capacity modestly by 10 boxes (144 to154 boxes) results in a dramatic system improvement, reducing utilization to 88.6%, average wait time for a telemetry bed to 34 minutes, and percent of patients waiting for a telemetry slot to 9.4%. These predicted results compared well to the observed data. After the intervention, there was a decrease in utilization to 86.9%, and a statistically significant reduction in time waiting in the ED for an inpatient bed by 1.24 (p Conclusions: Queuing theory allows for modeling of healthcare system behavior to predict needed clinical capacity for achieving system goals. Our simplified model demonstrates that a modest increase in capacity can drive significant system improvements. Utilizing queuing theory and understanding the behavior of the healthcare system can improve the predicted impact of interventions on cost-effectiveness and quality of care.