The Profile and All-Cause In-Hospital Mortality Dynamics of St-Segment Elevation Myocardial Infarction Patients during the Two Years of the COVID-19 Pandemic.

During the coronavirus pandemic 2019 (COVID-19), some studies showed differences in the profile of subjects presenting with acute coronary syndromes as well as in overall mortality due to the delay of presentation and other complications. The purpose of this study was to compare the profile and outcomes, with emphasis on all-cause in-hospital mortality, of ST-elevation myocardial infarction (STEMI) subjects presenting to the emergency department during the pandemic period compared with a control group from the previous year, 2019. The study enrolled 2011 STEMI cases, which were divided into two groups-pre-pandemic (2019-2020) and pandemic period (2020-2022). Hospital admissions for a STEMI diagnosis sharply decreased during the COVID-19 period by 30.26% during the first year and 25.4% in the second year. This trend was paralleled by a significant increase in all-cause in-hospital mortality: 11.5% in the pandemic period versus 8.1% in the previous year. There was a significant association between SARS-CoV-2 positivity and all-cause in-hospital mortality, but no correlation was found between COVID-19 diagnosis and the type of revascularization. However, the profile of subjects presenting with STEMI did not change over time during the pandemic; their demographic and comorbid characteristics remained similar.

Artificial intelligence and cloud based platform for fully automated PCI guidance from coronary angiography-study protocol.

Ischemic heart disease represent a heavy burden for the medical systems irrespective of the methods used for diagnosis and treatment of such patients in the daily medical routine. The present paper depicts the protocol of a study whose main aim is to develop, implement and test an artificial intelligence algorithm and cloud based platform for fully automated PCI guidance using coronary angiography images. We propose the utilisation of multiple artificial intelligence based models to produce three-dimensional coronary anatomy reconstruction and assess function- post-PCI FFR computation- for developing an extensive report describing and motivating the optimal PCI strategy selection. All the relevant artificial intelligence model outputs (anatomical and functional assessment-pre- and post-PCI) are presented to the clinician via a cloud platform, who can then take the utmost treatment decision. The physician will be provided with multiple scenarios and treatment possibilities for the same case allowing a real-time evaluation of the most appropriate PCI strategy planning and follow-up. The artificial intelligence algorithms and cloud based PCI selection workflow will be verified and validated in a pilot clinical study including subjects prospectively to compare the artificial intelligence services and results against annotations and invasive measurements.

Microvascular (dys)Function in Stable Coronary Artery Disease: Cross Talk with Epicardial Segments.

Myocardial ischemia is the consequence of an unbalance between coronary flow that can be achieved and myocardial metabolic needs. Pathological state of both epicardial and intramyocardial vessels may be responsible for inducing ischemia. However, revascularization decision should be based on the severity of each epicardial lesion that is evaluated. There are different diagnostic tools that may help for the evaluation of each compartment which is based on the measurement of coronary hemodynamics. Pressure-derived indices are recommended by current guidelines for evaluation of epicardial stenosis significance. We assess the complex interaction between hemodynamic parameters in order to understand how different parameters are influenced in the settings of microvascular dysfunction.

New cardiovascular risk factors and their use for an accurate cardiovascular risk assessment in hypertensive patients.

OBJECTIVES: To analyze the predictive value of new cardiovascular (CV) risk factors for CV risk assessment in the adult Romanian hypertensive (HT) population. METHODS: Hypertensive adults aged between 40-65 years of age, identified in national representative SEPHAR II survey were evaluated by anthropometric, BP and arterial stiffness measurements: aortic pulse wave velocity (PWVao), aortic augmentation index (AIXao), revers time (RT) and central systolic blood pressure (SBPao), 12 lead ECGs and laboratory workup. Values above the 4th quartile of mean SBP' standard deviation (s.d.) defined increased BP variability. Log(TG/HDL-cholesterol) defined atherogenic index of plasma (AIP). Serum uric acid levels above 5.70 mg/dl for women and 7.0 mg/dl for males defined hyperuricemia (HUA). CV risk was assessed based on SCORE chart for high CV risk countries. Binary logistic regression using a stepwise likelihood ratio method (adjustments for major confounders and colliniarity analysis) was used in order to validate predictors of high and very high CV risk class. RESULTS: The mean SBP value of the study group was 148.46±19.61 mmHg. Over forty percent of hypertensives had a high and very high CV risk. Predictors of high/very high CV risk category validated by regression analysis were: increased visit-to-visit BP variability (OR: 2.49; 95%CI: 1.67-3.73), PWVao (OR: 1.12; 95%CI: 1.02-1.22), RT (OR: 0.95; 95% CI: 0.93-0.98), SBPao (OR: 1.01; 95%CI: 1.01-1.03) and AIP (OR: 7.08; 95%CI: 3.91-12.82). CONCLUSION: The results of our study suggests that the new CV risk factors such as increased BP variability, arterial stiffness indices and AIP are useful tools for a more accurate identification of hypertensives patients at high and very high CV risk.