Body Mass Index (BMI) has a significant impact on Coronavirus disease (COVID-19) patient outcomes; however, major adverse cardiac and cerebrovascular outcomes in patients with severe sepsis have been poorly understood. Our study aims to explore and provide insight into its association. This is an observational study looking at the impact of BMI on COVID-19-severe sepsis hospitalizations. The primary outcomes are adjusted odds of all-cause in-hospital mortality, respiratory failure, and major adverse cardiac and cerebrovascular events (MACCE), which include acute myocardial infarction, cardiac arrest, and acute ischemic stroke. The secondary outcome was healthcare resource utilization. Coexisting comorbidities and patient features were adjusted with multivariable regression analyses. Of 51,740 patients with severe COVID-19-sepsis admissions, 11.4% were overweight, 24.8% had Class I obesity (BMI 30–34.9), 19.8% had Class II obesity (BMI 35–39.9), and 43.9% had the categorization of Class III obesity (BMI >40) cohorts with age>18 years. The odds of MACCE in patients with class II obesity and class III obesity (OR 1.09 and 1.54; 95CI 0.93–1.29 and 1.33–1.79) were significantly higher than in overweight (p < 0.001). Class I, Class II, and Class III patients with obesity revealed lower odds of respiratory failure compared to overweight (OR 0.89, 0.82, and 0.82; 95CI 0.75–1.05, 0.69–0.97, and 0.70–0.97), but failed to achieve statistical significance (p = 0.079). On multivariable regression analysis, all-cause in-hospital mortality revealed significantly higher odds in patients with Class III obesity, Class II, and Class I (OR 1.56, 1.17, and 1.06; 95CI 1.34–1.81, 0.99–1.38, and 0.91–1.24) vs. overweight patients (p < 0.001). Patients with Class II and Class III obesity had significantly higher odds of MACCE and in-hospital mortality in COVID-19-severe sepsis admissions.

Cardiovascular diseases (CVDs) remain the leading cause of mortality. Traditional approaches to diagnosing and treating CVDs often involve interpretation, which can lead to errors. The emergence of Artificial Intelligence (AI) and Machine Learning aids the management of CVDs. AI examines amounts of data and identifies patterns offering solutions for disease detection, risk prediction, and patient care. The American Heart Association's 2023 Scientific Sessions (AHA23) showcased studies that highlight AIs potential to revolutionize cardiac healthcare. This article presents a review of these cutting-edge studies exploring how AI is applied in health, the outcomes of these applications, and their implications for clinical practice. The insights gained from this review will contribute to the discussion on AI's role in healthcare and its capacity to enhance outcomes.