Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray

Aims. To develop a deep-learning based system for recognition of subclinical atherosclerosis on a plain frontal chest x-ray. Methods and Results. A deep-learning algorithm to predict coronary artery calcium (CAC) score (the AI-CAC model) was developed on 460 chest x-ray (80% training cohort, 20% internal validation cohort) of primary prevention patients (58.4% male, median age 63 [51-74] years) with available paired chest x-ray and chest computed tomography (CT) indicated for any clinical reason and performed within 3 months. The CAC score calculated on chest CT was used as ground truth. The model was validated on an temporally-independent cohort of 90 patients from the same institution (external validation). The diagnostic accuracy of the AI-CAC model assessed by the area under the curve (AUC) was the primary outcome. Overall, median AI-CAC score was 35 (0-388) and 28.9% patients had no AI-CAC. AUC of the AI-CAC model to identify a CAC>0 was 0.90 in the internal validation cohort and 0.77 in the external validation cohort. Sensitivity was consistently above 92% in both cohorts. In the overall cohort (n=540), among patients with AI-CAC=0, a single ASCVD event occurred, after 4.3 years. Patients with AI-CAC>0 had significantly higher Kaplan Meier estimates for ASCVD events (13.5% vs. 3.4%, log-rank=0.013). Conclusion. The AI-CAC model seems to accurately detect subclinical atherosclerosis on chest x-ray with elevated sensitivity, and to predict ASCVD events with elevated negative predictive value. Adoption of the AI-CAC model to refine CV risk stratification or as an opportunistic screening tool requires prospective evaluation.Comment: Submitted to European Heart Journal - Cardiovascular Imaging Added also the additional material 44 pages (30 main paper, 14 additional material), 14 figures (5 main manuscript, 9 additional material

Reduced Rate of Hospital Admissions for ACS during Covid-19 Outbreak in Northern Italy

To address the coronavirus (Covid-19) pandemic,1 strict social containment measures have been adopted worldwide, and health care systems have been reorganized to cope with the enormous increase in the numbers of acutely ill patients.2,3 During this same period, some changes in the pattern of hospital admissions for other conditions have been noted. The aim of the present analysis is to investigate the rate of hospital admissions for acute coronary syndrome (ACS) during the early days of the Covid-19 outbreak

Appraising the pathophysiologic impact of coronary collaterals as measured by fractional flow reserve on symptoms and signs of myocardial ischemia

Background The purpose of coronary revascularization in stable patients is anginal relief, yet there is no linear relationship between stenosis severity and clinical significance. A major factor in this complex lesion myocardium interaction is collateral flow. We aimed to define which collateral flow cut-offs separate asymptornatic from symptomatic patients during coronary occlusion. Methods Patients undergoing percutaneous transluminal coronary angioplasty for a single stenotic lesion were selected, collaterals were appraised angiographically, and fractional flow reserve was used during prolonged balloon occlusion to measure collateral flow index (FFRcoll). Changes in anginal symptoms, ST-T segment, and left ventricular wall motion were appraised before and during/ shortly after balloon dilation. Receiver-operating-characteristic curves and area under the curve were computed to identify the most appropriate FFRcoll cut-offs. Results Twenty consecutive patients were enrolled. At baseline, 10 patients had angiographic evidence of collaterals, whereas 10 had no angiographic evidence of collateral flow distal to the target lesion. FFRcoll had an excellent discriminatory performance for the presence of angiographic collaterals (area under the curve = 0.90, P = 0.003), a good discriminatory performance for the occurrence of angina (area under the curve = 0.80, P = 0.025), and a trend toward a good discriminatory performance for the occurrence of asynergy (area under the curve = 0.81, P = 0.06). On the basis of receiver-operating-characteristic curves, an FFRcoll cut-off greater than 0.26 could reliably distinguish patients with adequate collaterals (sensitivity = 0.90, specificity = 0.80), whereas a greater than 0.41 cut-off distinguished patients having angina or wall motion abnormalities from those remaining asymptomatic. Conclusion This study shows that distal collateral pressure greater than 41% of proximal perfusion pressure protects from anginal symptoms or regional systolic dysfunction during coronary occlusion, whereas a greater than 26% cutoff is more appropriate to identify angiographically evident collaterals ensuring distal myocardial viability. J Cardiovasc Med 9:1120-1126 (C) 2008 Italian Federation of Cardiology