Objective Risk stratification is crucial to optimise treatment strategies in patients with COVID-19. We aimed to evaluate the impact on mortality of an early assessment of cardiac biomarkers in patients with COVID-19. Methods Humanitas Clinical and Research Hospital (Rozzano-Milan, Lombardy, Italy) is a tertiary centre that has been converted to the management of COVID-19. Patients with confirmed COVID-19 were entered in a dedicated database for cohort observational analyses. Outcomes were stratified according to elevated levels (ie, above the upper level of normal) of high-sensitivity cardiac troponin I (hs-TnI), B-type natriuretic peptide (BNP) or both measured within 24 hours after hospital admission. The primary outcome was all-cause mortality. Results A total of 397 consecutive patients with COVID-19 were included up to 1 April 2020. At the time of hospital admission, 208 patients (52.4%) had normal values for cardiac biomarkers, 90 (22.7%) had elevated both hs-TnI and BNP, 59 (14.9%) had elevated only BNP and 40 (10.1%) had elevated only hs-TnI. The rate of mortality was higher in patients with elevated hs-TnI (22.5%, OR 4.35, 95% CI 1.72 to 11.04), BNP (33.9%, OR 7.37, 95% CI 3.53 to 16.75) or both (55.6%, OR 18.75, 95% CI 9.32 to 37.71) as compared with those without elevated cardiac biomarkers (6.25%). A multivariate analysis identified concomitant elevation of both hs-TnI and BNP as a strong independent predictor of all-cause mortality (OR 3.24, 95% CI 1.06 to 9.93). Conclusions An early detection of elevated hs-TnI and BNP predicts mortality in patients with COVID-19. Cardiac biomarkers should be systematically assessed in patients with COVID-19 at the time of hospital admission in order to optimise risk stratification.

Since its introduction in 1977, percutaneous coronary intervention has become one of the most commonly performed therapeutic procedures worldwide. Such widespread diffusion, however, would have not been possible without a concomitant evolution of the pharmacotherapies associated with this intervention. Antithrombotic agents are fundamental throughout the management of patients undergoing coronary stent implantation, starting from the procedure itself to the long-term prevention of cardiovascular events. The last 40 years of interventional cardiology have seen remarkable improvements in both drug therapies and device technologies, which largely reflected a progressive understanding of the pathophysiological mechanisms of coronary artery disease, as well as procedure- and device-related adverse events. The purpose of this article is to provide an overview of the important milestones in antithrombotic pharmacology that have shaped clinical practice of today while also providing insights into knowledge gaps and future directions.

Coronary stents have revolutionized the treatment of coronary artery disease. Compared with balloon angioplasty, bare-metal stents (BMSs) effectively prevented abrupt vessel closure but were limited by in-stent restenosis (ISR) due to smooth muscle cell proliferation and neointimal hyperplasia. The first-generation drug-eluting stent (DES), with its antiproliferative drug coating, offered substantial advantages over BMSs as it mitigated the risk of ISR. Nonetheless, they had several design limitations that increased the risk of late stent thrombosis. Significant advances in stent design, including thinner struts, enhanced polymers’ formulation, and more potent antiproliferative agents, have led to the introduction of new-generation DES with a superior safety profile. Cardiologists have over 20 different DES types to choose from, each with its unique features and characteristics. This review highlights the evolution of stent design and summarizes the clinical data on the different stent types. We conclude by discussing the clinical implications of stent design in high-risk subsets of patients.

Artificial intelligence (AI) is a broad term referring to any automated systems that need ‘intelligence’ to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools.