AIM: To assess the impact of the first wave of the COVID-19 pandemic on acute coronary syndromes and on the delay from symptom onset to first medical contact among patients presenting with ST-segment elevation myocardial infarction (STEMI), as well as to investigate whether there were patient-related reasons related to COVID-19 for delaying first medical contact. METHODS AND RESULTS: All patients undergoing percutaneous coronary intervention (PCI) at the Geneva University Hospitals for acute coronary syndromes (ACS) during the first COVID-19 wave were compared with a control group consisting of all ACS patients who underwent PCI during the same period in 2019 and those treated in the period immediately preceding the pandemic. The primary outcome measure was the difference in the delay from symptom onset to first medical contact in the setting of STEMI between the COVID-19 period and the control period. Secondary outcome measures were the difference in ACS incidence and the impact of the COVID-19 pandemic on patients' decisions to call the emergency services, assessed using a questionnaire. Delay from symptom onset to first medical contact was longer among patients suffering from STEMI in the COVID-19 period compared with the control period (112 min vs 60 min, p = 0.049). The incidence rate of ACS was lower during the COVID-19 period (incidence rate ratio 0.6, 95% confidence interval [CI] 0.449-0.905). ACS patients delayed their call to the emergency services mainly because of fear of contracting or spreading COVID-19 following hospital admission, as well as of adding burden to the healthcare system. CONCLUSION: We observed prolonged delays from symptom onset to first medical contact and a decline in overall ACS incidence during the first wave of the COVID-19 pandemic, with a higher threshold to call for help among ACS patients.

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ObjectiveHistory of cardiovascular diseases (CVDs) may influence the prognosis of patients hospitalised for COVID-19. We investigated whether patients with previous CVD have increased risk of death and major adverse cardiovascular event (MACE) when hospitalised for COVID-19.MethodsWe included 839 patients with COVID-19 hospitalised at the University Hospitals of Geneva. Demographic characteristics, medical history, laboratory values, ECG at admission and medications at admission were collected based on electronic medical records. The primary outcome was a composite of in-hospital mortality or MACE.ResultsMedian age was 67 years, 453 (54%) were males and 277 (33%) had history of CVD. In total, 152 (18%) died and 687 (82%) were discharged, including 72 (9%) who survived a MACE. Patients with previous CVD were more at risk of composite outcomes 141/277 (51%) compared with those without CVD 83/562 (15%) (OR=6.0 (95% CI 4.3 to 8.4), p<0.001). Multivariate analyses showed that history of CVD remained an independent risk factor of in-hospital death or MACE (OR=2.4; (95% CI 1.6 to 3.5)), as did age (OR for a 10-year increase=2.2 (95% CI 1.9 to 2.6)), male gender (OR=1.6 (95% CI 1.1 to 2.3)), chronic obstructive pulmonary disease (OR=2.1 (95% CI 1.0 to 4.2)) and lung infiltration associated with COVID-19 at CT scan (OR=1.9 (95% CI 1.2 to 3.0)). History of CVD (OR=2.9 (95% CI 1.7 to 5)), age (OR=2.5 (95% CI 2.0 to 3.2)), male gender (OR=1.6 (95% CI 0.98 to 2.6)) and elevated C reactive protein (CRP) levels on admission (OR for a 10 mg/L increase=1.1 (95% CI 1.1 to 1.2)) were independent risk factors for mortality.ConclusionHistory of CVD is associated with higher in-hospital mortality and MACE in hospitalised patients with COVID-19. Other factors associated with higher in-hospital mortality are older age, male sex and elevated CRP on admission.

VCs following TF-TAVI are frequent. Major but not minor VCs are associated with increased mortality. Percutaneous management of VCs is feasible and safe, and surgery is rarely needed.

Driven by recent innovations and technological progress, the increasing quality and amount of biomedical data coupled with the advances in computing power allowed for much progress in artificial intelligence (AI) approaches for health and biomedical research. In interventional cardiology, the hope is for AI to provide automated analysis and deeper interpretation of data from electrocardiography, computed tomography, magnetic resonance imaging, and electronic health records, among others. Furthermore, high-performance predictive models supporting decision-making hold the potential to improve safety, diagnostic and prognostic prediction in patients undergoing interventional cardiology procedures. These applications include robotic-assisted percutaneous coronary intervention procedures and automatic assessment of coronary stenosis during diagnostic coronary angiograms. Machine learning (ML) has been used in these innovations that have improved the field of interventional cardiology, and more recently, deep Learning (DL) has emerged as one of the most successful branches of ML in many applications. It remains to be seen if DL approaches will have a major impact on current and future practice. DL-based predictive systems also have several limitations, including lack of interpretability and lack of generalizability due to cohort heterogeneity and low sample sizes. There are also challenges for the clinical implementation of these systems, such as ethical limits and data privacy. This review is intended to bring the attention of health practitioners and interventional cardiologists to the broad and helpful applications of ML and DL algorithms to date in the field. Their implementation challenges in daily practice and future applications in the field of interventional cardiology are also discussed.