Publications by authors named "R E Woosley"
Article Synopsis
- - The study aimed to explore how a polygenic risk score (PRS) for QT prolongation (QTc-PRS) is linked to QTc intervals and sudden cardiac death (SCD) in people with and without sleep-disordered breathing (SDB) using data from the UK Biobank.
- - Results showed that a higher QTc-PRS was connected to longer QTc intervals, with SDB significantly affecting the relationship between QTc-PRS and SCD risk; those with SDB had a much higher risk of SCD.
- - In particular, Black participants with SDB were found to have a notably high risk of sudden cardiac death, highlighting the importance of SDB as a modifier for genetic risk factors.
Introduction: The goal of this study was to evaluate the association between a polygenic risk score (PRS) for QT prolongation (QTc-PRS), QTc intervals and mortality in patients enrolled in the UK Biobank with and without sleep apnea.
Methods: The QTc-PRS was calculated using allele copy number and previously reported effect estimates for each single nuclear polymorphism SNP. Competing-risk regression models adjusting for age, sex, BMI, QT prolonging medication, race, and comorbid cardiovascular conditions were used for sudden cardiac death (SCD) analyses.
Introduction: Patients with obstructive sleep apnea (OSA) are at risk for QTc prolongation, a known risk factor for increased mortality. The pro-QTc score can help identify individuals at increased risk for mortality associated with increased QTc however, it has not been evaluated in patients with OSA. The goal of this study was to evaluate the pro-QTc score in patients with OSA.
View Article and Find Full Text PDFObjectives: To evaluate the relationship between a modified Tisdale QTc-risk score (QTc-RS) and inpatient mortality and length of stay in a broad inpatient population with an order for a medication with a known risk of torsades de pointes (TdP).
Background: Managing the risk of TdP is challenging due to the number of medications with known risk of TdP and the complexity of precipitating factors. A model to predict risk of mortality may be useful to guide treatment decisions.