Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.

Background: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal. Methods: Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023. Findings: ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14-2.71), 4.23 (3.74-4.78), and 11.75 (10.2-13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age. Interpretation: ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired. Funding: Anumana.

Climate change and cardiovascular disease: implications for global health.

Climate change is the greatest existential challenge to planetary and human health and is dictated by a shift in the Earth's weather and air conditions owing to anthropogenic activity. Climate change has resulted not only in extreme temperatures, but also in an increase in the frequency of droughts, wildfires, dust storms, coastal flooding, storm surges and hurricanes, as well as multiple compound and cascading events. The interactions between climate change and health outcomes are diverse and complex and include several exposure pathways that might promote the development of non-communicable diseases such as cardiovascular disease. A collaborative approach is needed to solve this climate crisis, whereby medical professionals, scientific researchers, public health officials and policymakers should work together to mitigate and limit the consequences of global warming. In this Review, we aim to provide an overview of the consequences of climate change on cardiovascular health, which result from direct exposure pathways, such as shifts in ambient temperature, air pollution, forest fires, desert (dust and sand) storms and extreme weather events. We also describe the populations that are most susceptible to the health effects caused by climate change and propose potential mitigation strategies, with an emphasis on collaboration at the scientific, governmental and policy levels.

Left Atrial Mechanics Associates With Paroxysmal Atrial Fibrillation in Light-Chain Amyloidosis Following Stem Cell Transplantation.

BACKGROUND: Atrial fibrillation (AF) during high-dose melphalan and autologous stem-cell transplantation (HDM/SCT) for light-chain (AL) amyloidosis confers significant morbidity. Traditional risk factors provide limited prediction for development of paroxysmal AF during this vulnerable period. OBJECTIVES: We sought to assess the association of clinical and echocardiographic parameters, including left atrial (LA) mechanics and development of AF in patients undergoing HDM/SCT therapy. METHODS: Baseline echocardiograms, electrocardiograms, and electronic medical records were retrospectively assessed among patients with AL amyloidosis before HDM/SCT (n = 91). LA function analysis was performed using speckle-tracking echocardiography. RESULTS: In this study, 42 patients (46%) had cardiac involvement; in the peri-transplant period, 12 (13%) developed AF (7 with cardiac involvement). No significant differences in age, sex, cardiac biomarkers, or cardiac risk factors were seen between patients with and without development of AF; one-third of patients with AF peri-transplant had previous AF. Although LA reservoir strain was reduced in patients with development of AF, time to peak strain rate indexed to R-R interval (TPSRI) (p = 0.001) was prolonged in patients with development of AF compared with sinus rhythm patients in the total cohort but also in subgroups with and without cardiac involvement. CONCLUSIONS: TPSRI, a parameter of mechanical dispersion in the early reservoir phase of LA function, is associated with development of AF among patients undergoing HDM/SCT for AL amyloidosis. These findings require validation in larger prospective cohorts.

Stabilization of Cardiac Function With Diflunisal in Transthyretin (ATTR) Cardiac Amyloidosis.

BACKGROUND: Transthyretin amyloidosis cardiomyopathy (ATTR-CM) is an underappreciated cause of heart failure that results from misfolded TTR (prealbumin) protein. Diflunisal is an approved non-steroidal anti-inflammatory drug that stabilizes TTR, with limited data available regarding effects on cardiac structure and function. METHODS AND RESULTS: ATTR-CM patients (n=81, 41% treated with 250 mg twice-daily diflunisal by clinical practice) were retrospectively identified with baseline and follow-up (median interval 1 year) serum biomarker and echocardiographic data compared, including global longitudinal strain (GLS). Chi-squared and Wilcoxon tests assessed differences between subjects, divided by treatment group, and univariable and multivariable linear regression was performed. At baseline, patients treated with diflunisal were younger (68 vs 77 years, P = .0001), with lower B-type natriuretic peptide (BNP; 249 vs 545 pg/mL, P = .009) and serum creatinine (1.1 vs 1.2 mg/dL, P = .04), but similar TTR concentration (P = .31), cardiac troponin I (P = .06), and GLS (P = .67). At follow-up, diflunisal untreated versus treated patients showed differences in TTR concentration (19 vs 33 mg/dL, P = .01) and favorable differences in left atrial volume index (+4.6 vs -1.4 mL/m2, P = .002) and cardiac troponin I (+0.03 vs -0.01 ng/mL, P = .01) for the entire cohort. Among the subset with wild-type ATTR (n=53), diflunisal treatment was associated with differences in GLS (+1.2% untreated vs +0.1% treated, P = .03). Changes in wall thickness (P = .2), left ventricular ejection fraction (P = .71), and BNP (P = .42) were similar between groups. CONCLUSIONS: In ATTR-CM, diflunisal treatment resulted in measurable differences in some parameters of cardiac structure and function after only 1 year of administration. Further longer-term analysis is warranted.