Variability and Reproducibility of the SYNTAX Score for Triple-Vessel Disease.

BACKGROUND: The SYNTAX score (SX) is an angiographic grading system to determine the burden and complexity of coronary artery disease (CAD) and to guide operators as to the appropriateness of percutaneous coronary intervention (PCI) vs coronary artery bypass grafting (CABG). However, variability of the SX may exist since the assessment relies on individual clinicians to visually interpret lesion severity and characteristics. We therefore aimed to assess SX variability and reproducibility among interpreting physicians. METHODS: Fifty patient angiograms were randomly selected from a registry of patients with multi-vessel CAD (treated with PCI or CABG) completed at our institution during the years 2011-2018. Each angiogram was evaluated by 6 clinicians on 2 separate occasions (minimum 8 weeks between occasions) for a total of 600 SX. Our goal was to evaluate both inter- and intra- observer reliability of SX scores. Variation in both raw score as well as risk classification (low, intermediate or high SX) was observed. Inter- and intra-observer reliability were assessed using the intra-class correlation coefficient (ICC), Cohen's weighted Kappa, and Fleiss' Kappa. RESULTS: SYNTAX scores on both assessments and across all 6 cardiologists had a mean score of 25.3. On the first assessment, the ICC for the inter-observer reliability of SX scores was 0.61 (95% CI: 0.50, 0.73). Across the 6 observers, only 16% of angiograms were classified in the same risk classification by all observers. 34% of angiograms had less than a majority agreement (3 or less observers) on risk classification. The weighted Kappa for intra-observer reliability of risk classification scores ranged from 0.30 to 0.81. Across the 6 observers, the proportion of angiograms classified as the same risk classification between each observer's 1st and 2nd assessment ranged from 46% to 84%. CONCLUSION: This study shows a wide inter- and intra- user variability in calculating SX. Our data indicates a significant limitation in using the SX to guide revascularization strategies. Further studies are needed to determine more reliable ways to quantitate burden of CAD.

Applications of Machine Learning in Cardiology.

In this digital era, artificial intelligence (AI) is establishing a strong foothold in commercial industry and the field of technology. These effects are trickling into the healthcare industry, especially in the clinical arena of cardiology. Machine learning (ML) algorithms are making substantial progress in various subspecialties of cardiology. This will have a positive impact on patient care and move the field towards precision medicine. In this review article, we explore the progress of ML in cardiovascular imaging, electrophysiology, heart failure, and interventional cardiology.

Sex-Based Differences in COVID-19 Outcomes.

Background: Smaller studies suggest lower morbidity and mortality associated with coronavirus disease 2019 (COVID-19) in women. Our aim is to assess the impact of female sex on outcomes in a large cohort of patients hospitalized with COVID-19. Materials and Methods: This is a retrospective observational cohort study of 10,630 adult patients hospitalized with a confirmed COVID-19 polymerase chain reaction between March 1, 2020 and April 27, 2020, with follow-up conducted through June 4, 2020. Logistic regression was used to examine the relationship between sex and the primary outcomes, including length of stay, admission to intensive care unit (ICU), need for mechanical ventilation, pressor requirement, and all-cause mortality as well as major adverse events and in-hospital COVID-19 treatments. Results: In the multivariable analysis, women had 27% lower odds of in-hospital mortality (odds ratio [OR] = 0.73, 95% confidence interval [CI] 0.66-0.81; p < 0.001), 24% lower odds of ICU admission (OR = 0.76, 95% CI 0.69-0.84; p < 0.001), 26% lower odds of mechanical ventilation (OR = 0.74, 95% CI 0.66-0.82; p < 0.001), and 25% lower odds of vasopressor requirement (OR = 0.75, 95% CI 0.67-0.84; p < 0.001). Women had 34% less odds of having acute cardiac injury (OR = 0.66, 95% CI 0.59-0.74; p < 0.001; n = 7,289), 16% less odds of acute kidney injury (OR = 0.84, 95% CI 0.76-0.92; p < 0.001; n = 9,840), and 27% less odds of venous thromboembolism (OR = 0.73, 95% CI 0.56-0.96; p < 0.02; c-statistic 0.85, n = 9,407). Conclusions: Female sex is associated with lower odds of in-hospital outcomes, major adverse events, and all-cause mortality. There may be protective mechanisms inherent to female sex, which explain differences in COVID-19 outcomes.

Oral health and atherosclerotic cardiovascular disease: A review.

Periodontal disease (PD) is common in the US and globally. Evidence suggests that poor oral health is associated with atherosclerotic cardiovascular disease (ASCVD); however, this relationship has not been a major focus in clinical cardiology. This manuscript will review the growing evidence linking PD to ASCVD, including pathophysiologic mechanisms and coexistent risk factors. Public health considerations with a focus on disparities, social determinants, preventive strategies, and a call to action to reduce the burden of coincident ASCVD and PD are also reviewed.