External Validation of the American Heart Association PREVENT Cardiovascular Disease Risk Equations.

Importance: The American Heart Association's Predicting Risk of Cardiovascular Disease Events (PREVENT) equations were developed to extend and improve on previous cardiovascular disease (CVD) risk assessments for the purpose of treatment initiation and patient-clinician communication. Objective: To assess prognostic capabilities, calibration, and discrimination of the PREVENT equations in a study sample representative of the noninstitutionalized, US general population. Design, Setting, and Participants: This prognostic study used data from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2010 data cycles. Participants included adults for whom 10-year follow-up data were available. Data curation and analyses took place from December 2023 through May 2024. Main Outcomes and Measures: Primary measures were risk estimated by the PREVENT equations, as well as risk estimates from the previous Pooled Cohort Equations (PCEs). The primary outcome was composite CVD-related mortality at 10 years of follow-up. Additional analyses compared the PREVENT equations against the PCEs. Model discrimination was assessed with receiver-operator characteristic curves and Harrell C statistic from proportional hazard regression; model calibration was determined as the slope of predicted versus observed risk. Results: The study cohort, accounting for NHANES complex survey design, consisted of 172.9 million participants (mean age, 45.0 years [95% CI, 44.6-45.4 years]; 52.1% women [95% CI, 51.5%-52.6%]). In analyses adjusted for the NHANES survey design, a 1% increase in PREVENT risk estimates was statistically significantly associated with increased CVD mortality risk (hazard ratio, 1.090; 95% CI, 1.087-1.094). PREVENT risk scores demonstrated excellent discrimination (C statistic, 0.890; 95% CI, 0.881-0.898) but moderate underfitting of the model (calibration slope, 1.13; 95% CI, 1.06-1.21). PREVENT risk models performed statistically significantly better than the PCEs, as assessed by the net reclassification index (0.093; 95% CI, 0.073-0.115). Conclusions and Relevance: In this prognostic study of the PREVENT equations, PREVENT risk estimates demonstrated excellent discrimination and only modest discrepancies in calibration. These findings provided evidence supporting utilization of the PREVENT equations for application in the intended population as suggested by the American Heart Association.

A Case Series of Diverse Cardiac Abnormalities in Collegiate Athlete with COVID-19: Role for Multimodality Imaging.

Introduction: Since the COVID-19 pandemic there is concern for subclinical cardiac pathology in the absence of clinical symptoms in collegiate athletes, we present 4 cases of abnormal left ventricular global longitudinal strain (LVGLS), a "red-flag" for potential COVID-19 myocardial disease, following diagnosis with diverse abnormalities reported via multimodality imaging weeks into recovery. Methods: Cardiac imaging studies consisting of transthoracic echocardiography (TTE) and cardiovascular magnetic resonance imaging (CMR) were performed 10 days post-COVID-19 diagnosis and several weeks into recovery. Results: Initial TTE revealed abnormal left ventricular global longitudinal strain (LVGLS), an identified "red-flag" for potential COVID-19 myocardial disease. Further CMR imaging revealed potential recent/prior myocarditis in 1 athlete. Follow-up TTE several weeks later revealed a return to normal LVGLS. Conversely, 2 cases with normal CMR imaging had a LVGLS that remained abnormal >30 days into recovery. Conclusions: These individual cases highlight the substantial differences in echocardiographic and CMR abnormalities between athletes with confirmed COVID-19.