Cardiovascular disease risk: it is complicated, but race and ethnicity are key, a Bayesian network analysis.

Background: Cardiovascular diseases are the leading cause of morbidity and mortality in the United States. Despite the complexity of cardiovascular disease etiology, we do not fully comprehend the interactions between non-modifiable factors (e.g., age, sex, and race) and modifiable risk factors (e.g., health behaviors and occupational exposures). Objective: We examined proximal and distal drivers of cardiovascular disease and elucidated the interactions between modifiable and non-modifiable risk factors. Methods: We used a machine learning approach on four cohorts (2005-2012) of the National Health and Nutrition Examination Survey data to examine the effects of risk factors on cardiovascular risk quantified by the Framingham Risk Score (FRS) and the Pooled Cohort Equations (PCE). We estimated a network of risk factors, computed their strength centrality, closeness, and betweenness centrality, and computed a Bayesian network embodied in a directed acyclic graph. Results: In addition to traditional factors such as body mass index and physical activity, race and ethnicity and exposure to heavy metals are the most adjacent drivers of PCE. In addition to the factors directly affecting PCE, sleep complaints had an immediate adverse effect on FRS. Exposure to heavy metals is the link between race and ethnicity and FRS. Conclusion: Heavy metal exposures and race/ethnicity have similar proximal effects on cardiovascular disease risk as traditional clinical and lifestyle risk factors, such as physical activity and body mass. Our findings support the inclusion of diverse racial and ethnic groups in all cardiovascular research and the consideration of the social environment in clinical decision-making.

Impact of COVID-19 pandemic on the volume, cost, and outcomes of cardiac electrophysiology procedures in the United States.

BACKGROUND: During the COVID-19 pandemic, professional societies recommended deferral of elective procedures for optimal resource utilization. OBJECTIVE: We sought to assess changes in procedural trends and outcomes of electrophysiology (EP) procedures during the pandemic. METHODS: National Inpatient Sample databases were used to identify all EP procedures performed in the United States (2016-2020) by International Classification of Diseases, Tenth Revision codes. We evaluated trends in utilization, cost/revenue, and outcomes from EP procedures performed. RESULTS: An estimated 1.35 million EP procedures (82% devices and 18% catheter ablations) were performed (2016-2020) with significant yearly uptrend. During the pandemic, there was a substantial decline in EP procedure utilization from a 5-year peak of 298 cases/million population in the second quarter of 2019 to a nadir of 220 cases in the second quarter of 2020. In 2020, the pandemic was associated with the loss of 50,233 projected EP procedures (39,337 devices and 10,896 ablations) with subsequent revenue loss of $7.06 billion. This deficit was driven by revenue deficit from dual-chamber permanent pacemaker (PPM) utilization ($2.88 billion, 49.3% of lost cases), ablation procedures ($1.84 billion, 21.7% of lost cases), and implantable cardioverter-defibrillator implantation ($1.36 billion, 12.0% of lost cases). To the contrary, there was a 9.4% increase in the utilization of leadless PPM. EP device implantation during the pandemic was associated with higher adverse in-hospital events (9.4% vs 8.0%; P < .001). CONCLUSION: In the United States, the significant decline in EP procedures during the pandemic was primarily driven by the reduction in dual-chamber PPM utilization, followed by arrhythmia ablation and implantable cardioverter-defibrillator implantation. There was a substantial increase in leadless PPM utilization during the pandemic.