As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensin-converting enzyme 2 (ACE2), but whether or not renin−angiotensin−aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.

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Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Although rare, immune checkpoint inhibitor (ICI)-related myocarditis can be life-threatening, even fatal. In view of increased ICI prescription, identification of clinical risk factors for ICI-related myocarditis is of primary importance. This study aimed to assess whether pre-existing cardiovascular (CV) patient conditions are associated with the reporting of ICI-related myocarditis in VigiBase, the WHO global database of suspected adverse drug reactions (ADRs). In a (retrospective) matched case-control study, 108 cases of ICI-related myocarditis and 108 controls of ICI-related ADRs other than myocarditis were selected from VigiBase. Drugs labeled as treatment for CV conditions (used as a proxy for concomitant CV risk factors and/or CV diseases) were found to be associated more strongly with the reporting of ICI-related myocarditis than with other ICI-related ADRs (McNemar’s chi-square test of marginal homogeneity: p = 0.026, Cramer’s coefficient of effect size: Φ = 0.214). No significant association was found between pre-existing diabetes and ICI-related myocarditis reporting (McNemar’s test of marginal homogeneity: p = 0.752). These findings offer an invitation for future prospective pharmacoepidemiological studies to assess the causal relationship between pre-existing CV conditions and myocarditis onset in a cohort of cancer patients followed during ICI treatment.

Aims Myocardial injury (MINJ) in Coronavirus disease 2019 (COVID-19) identifies individuals at high mortality risk but its clinical relevance is less well established for Influenza and no comparative analyses evaluating frequency and clinical implications of MINJ among hospitalized patients with Influenza or COVID-19 are available. Methods and Results Hospitalized adults with laboratory confirmed Influenza A or B or COVID-19 underwent highly-sensitive cardiac T Troponin (hs-cTnT) measurement at admission in four regional hospitals in Canton Ticino, Switzerland. MINJ was defined as hs-cTnT >14 ng/L. Clinical, laboratory and outcome data were retrospectively collected. The primary outcome was mortality up to 28 days. Cox regression models were used to assess correlations between admission diagnosis, myocardial injury and mortality. Clinical correlates of MINJ in both viral diseases were also identified. MINJ occurred in 94 (65.5%) out of 145 patients hospitalized for Influenza and 216 (47.8%) out of 452 patients hospitalized for COVID-19. Advanced age and renal impairment were factors associated with myocardial injury in both diseases. At 28 days, 7 (4.8%) deaths occurred among Influenza and 76 deaths (16.8%) among COVID-19 patients with a hazard ratio (HR) of 3.69 (95%-CI 1.70-8.00). Adjusted Cox regression models showed admission diagnosis of COVID-19 [HR:6.41 (95%-CI 4.05-10.14)] and MINJ [HR:8.01 (95%-CI 4.64-13.82)] to be associated with mortality. Conclusions Myocardial injury is frequent among both viral diseases and increases the risk of death in both COVID-19 and Influenza. The absolute risk of death is considerably higher in patients admitted for COVID-19 as compared with Influenza.