Recent works have demonstrated a significant reduction in cholesterol levels and increased oxidative stress in patients with coronavirus disease 2019 (COVID-19). The cause of this alteration is not well known. This study aimed to comprehensively evaluate their possible association during the evolution of COVID-19. This is an observational prospective study. The primary endpoint was to analyze the association between lipid peroxidation, lipid, and inflammatory profiles in COVID-19 patients. A multivariate regression analysis was employed. The secondary endpoint included the long-term follow-up of lipid profiles. COVID-19 patients presented significantly lower values in their lipid profile (total, low, and high-density lipoprotein cholesterol) with greater oxidative stress and inflammatory response compared to the healthy controls. Lipid peroxidation was the unique oxidative parameter with a significant association with the total cholesterol (OR: 0.982; 95% CI: 0.969–0.996; p = 0.012), IL1-RA (OR: 0.999; 95% CI: 0.998–0.999; p = 0.021) IL-6 (OR: 1.062; 95% CI: 1.017–1.110; p = 0.007), IL-7 (OR: 0.653; 95% CI: 0.433–0.986; p = 0.042) and IL-17 (OR: 1.098; 95% CI: 1.010–1.193; p = 0.028). Lipid abnormalities recovered after the initial insult during long-term follow-up (IQR 514 days); however, those with high LPO levels at hospital admission had, during long-term follow-up, an atherogenic lipid profile. Our study suggests that oxidative stress in COVID-19 is associated with derangements of the lipid profile and inflammation. Survivors experienced a recovery in their lipid profiles during long-term follow-up, but those with stronger oxidative responses had an atherogenic lipid profile.

Background/Objectives: Vegetation length is a guideline-endorsed criterion for surgery in left-sided infective endocarditis (LSIE). However, its measurement is highly variable with crucial implications for decision making. A standardized measurement system would not only facilitate decision making in these patients, but also cutoff-point optimization for improving clinical guidelines. For this purpose, this work introduces an Artificial Intelligence (AI)-based system capable of extracting vegetation length from standard transesophageal echocardiography (TEE). Methods: Five echocardiographers independently measured the vegetation length of 76 vegetations from 67 consecutive patients with LSIE using offline TEE images. The AI-based system was trained on a multicenter registry with 353 patients with LSIE, comprising 282,096 echocardiographic frames annotated by an independent expert. This system was applied to measure vegetation length as an independent observer. The variability and correlation between operators and the system were assessed. Results: Lin’s concordance correlation coefficient between the AI-based system and the mean of the measurements obtained by the echocardiographers was 0.74, comparable to the coefficient obtained between the echocardiographers themselves. Bland–Altman analysis showed a mean difference of −1.0 mm between the AI-based system and the mean of the measurements obtained by the echocardiographers. Conclusions: The AI-based system for vegetation measurement demonstrates a high level of correlation and agreement with experts, similar to the concordance between human operators themselves. This level of agreement suggests that the method has the potential to reduce inter-operator variability in the measurement process. Whether AI-based vegetation size improves embolism prediction has to be further investigated.