The use of artificial intelligence for predicting postinfarction myocardial viability in echocardiographic images.

BACKGROUND: Evaluation of standard echocardiographic examination with artificial intelligence may help in the diagnosis of myocardial viability and function recovery after acute coronary syndrome. METHODS: Sixty-one consecutive patients with acute coronary syndrome were enrolled in the present study (43 men, mean age 61 ± 9 years). All patients underwent percutaneous coronary intervention (PCI). 533 segments of the heart echo images were used. After 12 ± 1 months of follow-up, patients had an echocardiographic evaluation. After PCI each patient underwent cardiac magnetic resonance (CMR) with late enhancement and low-dose dobutamine echocardiographic examination. For texture analysis, custom software was used (MaZda 5.20, Institute of Electronics).Linear and non-linear (neural network) discriminative analyses were performed to identify the optimal analytic method correlating with CMR regarding the necrosis extent and viability prediction after follow-up. Texture parameters were analyzed using machine learning techniques: Artificial Neural Networks, Namely Multilayer Perceptron, Nonlinear Discriminant Analysis, Support Vector Machine, and Adaboost algorithm. RESULTS: The mean concordance between the CMR definition of viability and three classification models in Artificial Neural Networks varied from 42% to 76%. Echo-based detection of non-viable tissue was more sensitive in the segments with the highest relative transmural scar thickness: 51-75% and 76-99%. The best results have been obtained for images with contrast for red and grey components (74% of proper classification). In dobutamine echocardiography, the results of appropriate prediction were 67% for monochromatic images. CONCLUSIONS: Detection and semi-quantification of scar transmurality are feasible in echocardiographic images analyzed with artificial intelligence. Selected analytic methods yielded similar accuracy, and contrast enhancement contributed to the prediction accuracy of myocardial viability after myocardial infarction in 12 months of follow-up.

Improved outcomes in survivors of cardiac arrest qualified for early coronary angiography: A single tertiary center study.

INTRODUCTION: Most cardiac arrests in adults are related to coronary artery disease (CAD), and the role of early invasive cardiology procedures remains unclear. AIMS: We investigated the prognosis for patients hospitalized for out-of-hospital cardiac arrest (OHCA) or in-hospital cardiac arrest (IHCA) who were referred within 24 hours to a tertiary cardiology department, with a focus on the role of early coronary angiography (CA) and percutaneous coronary intervention (PCI). METHODS: This was an observational, single-center study using retrospective and prospective cohorts. Consecutive patients hospitalized for OHCA or IHCA and referred within 24 hours to a cardiology department were included in the study. Survival until hospital discharge was the primary outcome. RESULTS: One hundred and forty-eight patients aged 71 (14) years were included, 68 hospitalized for OHCA, and 80 patients after IHCA. Overall, in-hospital survival in the study group was 45% (66/148). In a multivariable logistic regression model, independent predictors of death were ejection fraction (EF) ≤30% (odds ratio [OR], 4.1; 95% confidence interval [CI], 1.69-10.03), blood oxygen saturation (SpO2) ≤90% (OR, 2.77; 95% CI, 1.19-6.46), non-ST-segement elevation myocardial infarction (NSTEMI) (OR, 2.71; 95% CI, 1.02-7.21). The risk of death was lower in patients who underwent early CA (OR, 0.28; 95% CI, 0.1-0.74) or received at least one defibrillation (OR, 0.11; 95% CI, 0.05-0.27), even after adjustment for other factors. CONCLUSIONS: In this series from a tertiary cardiac center, patients who underwent early CA had improved outcomes after cardiac arrest. In the multivariable logistic regression model, lower SpO2, lower EF, and NSTEMI were independent risk factors of death, whereas early CA and initial shockable rhythm improved survival.

A head-to-head comparison of wall motion score index, force, strain, and ejection fraction for the prediction of SYNTAX and Gensini coronary scores by dobutamine stress echocardiography.

BACKGROUND: Predicting the severity of coronary artery disease (CAD) may be possible during dobutamine stress echocardiography (DSE) with various indices of left ventricular function. AIMS: We assessed the relative value of ejection fraction (EF), force, global longitudinal strain (GLS), and wall motion score index (WMSI) in predicting SYNTAX and Gensini scores in patients with known or suspected CAD. METHODS: We prospectively enrolled 223 patients (120 men; mean [SD] age, 62 [9] years) and assessed the following indices: 1) EF (with triplane imaging); 2) force, calculated as the ratio of systolic blood pressure to left ventricular end­systolic volume; 3) GLS; 4) WMSI. All patients underwent coronary angiography within 12 weeks with SYNTAX and Gensini scores evaluation. RESULTS: The correlation of SYNTAX and Gensini scores was highest with peak WMSI (SYNTAX, rho = 0.591; Gensini, rho = 0.612; P <0.001), intermediate with peak force (SYNTAX, rho = -0.346; Gensini, rho = -0.377; P <0.001) and GLS (SYNTAX, rho = -0.205; P = 0.002 and Gensini rho = -0.216; P = 0.001), and the weakest for EF (SYNTAX, rho = -0.149; P = 0.03 and Gensini, rho = -0.191; P = 0.006). The similar hierarchy of variables was detected for changes during DSE. In the subgroup after myocardial infarction (n = 66 [27%]), GLS outperformed the force. CONCLUSIONS: In patients with CAD at DSE peak, theWMSI and force were better predictors of the coronary SYNTAX and Gensini scores and CAD severity than GLS or EF. However, in patients after myocardial infarction, the GLS correlation with coronary scores improved and got closer to the visual assessment.