Publications by authors named "Anna Rosenblatt"
Article Synopsis
- This study analyzed how QRS prolongation (a specific measurement in ECG) affects heart health differently based on sex and race in healthy adults participating in the Dallas Heart Study.
- Researchers found that longer QRS durations were linked to higher left ventricular mass and volume and lower ejection fraction, with differences seen between men and women and Black and White individuals.
- Although longer QRS durations were associated with a higher risk of major adverse cardiac events (MACE) in women, the link was less clear for men and not significant for either race after adjusting for cardiovascular risk factors.
Background: New-onset atrial fibrillation (AF) in patients hospitalized with COVID-19 has been reported and associated with poor clinical outcomes. We aimed to understand the incidence of and outcomes associated with new-onset AF in a diverse and representative US cohort of patients hospitalized with COVID-19.
Methods: We used data from the American Heart Association COVID-19 Cardiovascular Disease Registry.
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.
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View Article and Find Full Text PDFBackground: 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.
Background: The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone.
Methods And Results: We created 3 data sets from patients with at least one AF billing code from 2010 to 2017: a training set (n=886), an internal validation set from site no.