Clinical Features, Socioeconomic Status, Management, and Outcomes of Acute Heart Failure: PEACE MENA Registry Phase I Results.

INTRODUCTION: PEACE MENA (Program for the Evaluation and Management of Cardiac Events in the Middle East and North Africa) is a prospective registry in Arab countries for in-patients with acute myocardial infarction (AMI) or acute heart failure (AHF). Here, we report the baseline characteristics and outcomes of in-patients with AHF who were enrolled during the first 14 months of the recruitment phase. METHODS: A prospective, multi-centre, multi-country study including patients hospitalized with AHF was conducted. Clinical characteristics, echocardiogram, BNP (B-type natriuretic peptide), socioeconomic status, management, 1-month, and 1-year outcomes are reported. RESULTS: Between April 2019 and June 2020, a total of 1258 adults with AHF from 16 Arab countries were recruited. Their mean age was 63.3 (±15) years, 56.8% were men, 65% had monthly income ≤US$ 500, and 56% had limited education. Furthermore, 55% had diabetes mellitus, 67% had hypertension; 55% had HFrEF (heart failure with reduced ejection fraction), and 19% had HFpEF (heart failure with preserved ejection fraction). At 1 year, 3.6% had a heart failure-related device (0-22%) and 7.3% used an angiotensin receptor neprilysin inhibitor (0-43%). Mortality was 4.4% per 1 month and 11.77% per 1-year post-discharge. Compared with higher-income patients, lower-income patients had a higher 1-year total heart failure hospitalization rate (45.6 vs 29.9%, p=0.001), and the 1-year mortality difference was not statistically significant (13.2 vs 8.8%, p=0.059). CONCLUSION: Most of the patients with AHF in Arab countries had a high burden of cardiac risk factors, low income, and low education status with great heterogeneity in key performance indicators of AHF management among Arab countries.

Effectively Detecting Left Bundle Branch Block False Defects in Myocardial Perfusion Imaging (MPI) with a Convolutional Neural Network (CNN).

Left bundle branch block (LBBB) is a frequent source of false positive MPI reports, in patients evaluated for coronary artery disease. PURPOSE: In this work, we evaluated the ability of a CNN-based solution, using transfer learning, to produce an expert-like judgment in recognizing LBBB false defects. METHODS: We collected retrospectively, MPI polar maps, of patients having small to large fixed anteroseptal perfusion defect. Images were divided into two groups. The LBBB group included patients where this defect was judged as false defect by two experts. The LAD group included patients where this defect was judged as a true defect by two experts. We used a transfer learning approach on a CNN (ResNet50V2) to classify the images into two groups. RESULTS: After 60 iterations, the reached accuracy plateau was 0.98, and the loss was 0.19 (the validation accuracy and loss were 0.91 and 0.25, respectively). A first test set of 23 images was used (11 LBBB, and 12 LAD). The empiric ROC (Receiver operating characteristic) Area was estimated at 0.98. A second test set (18x2 images) was collected after the final results. The ROC area was estimated again at 0.98. CONCLUSION: Artificial intelligence, using CNN and transfer learning, could reproduce an expert-like judgment in differentiating between LBBB false defects, and LAD real defects.