Machine learning in predicting immediate and long-term outcomes of myocardial revascularization: a systematic review

Machine learning (ML) is among the main tools of artificial intelligence and are increasingly used in population and clinical cardiology to stratify cardiovascular risk. The systematic review presents an analysis of literature on using various ML methods (artificial neural networks, random forest, stochastic gradient boosting, support vector machines, etc.) to develop predictive models determining the immediate and long-term risk of adverse events after coronary artery bypass grafting and percutaneous coronary intervention. Most of the research on this issue is focused on creation of novel forecast models with a higher predictive value. It is emphasized that the improvement of modeling technologies and the development of clinical decision support systems is one of the most promising areas of digitalizing healthcare that are in demand in everyday professional activities

Parameters of complete blood count, lipid profile and their ratios in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome

Aim. To evaluate the predictive potential of the parameters of complete blood count (CBC), lipid profile and their ratios for predicting obstructive coronary artery disease (oCAD) in patients with non-ST elevation acute coronary syndrome (NSTEACS).Material and methods. The study included 600 patients with NSTE-ACS with a median age of 62 years who underwent invasive coronary angiography (CA). Two groups were formed, the first of which consisted of 360 (60%) patients with oCAD (stenosis ≥50%), and the second — 240 (40%) with coronary stenosis <50%. The clinical and functional status of patients before CAG was assessed by 33 parameters, including parameters of CBC, lipid profile and their ratio. For statistical processing and data analysis, the Mann-Whitney, Fisher, chi-squared tests, univariate logistic regression (LR) were used, while for the creation of predictive models, multivariate LR (MLR) was used. The quality was assessed by 4 metrics: area under the ROC curve (AUC), sensitivity (Se), specificity (Sp), and accuracy (Ac).Results. CBC and lipid profile analysis made it possible to identify factors that are linearly and non-linearly associated with oCAD. Univariate LR revealed their threshold values with the highest predictive potential. The quality metrics of the best prognostic model developed using MLR were as follows: AUC — 0,80, Sp — 0,79, Ac — 0,76, Se — 0,78. Its predictors were 8 following categorical parameters: age >55 years in men and >65 years in women, lymphocyte count (LYM) <19%, hematocrit >49%, immune-inflammation index >1000, high density lipoprotein cholesterol (HDL-C) to low density lipoprotein cholesterol (LDL-C) ratio <0,3, monocyte (MON)-to-HDL-C ratio >0,8, neutrophil (NEUT)-to-HDL-C ratio >5,7 and NEUT/LYM >3. The relative contribution of individual predictors to the development of end point was determined.Conclusion. The predictive algorithm (model 9), developed on the basis of MLR, showed a better quality metrics ratio than other models. The following 3 factors had the dominant influence on the oCAD risk: HDL-C/LDL-C (38%), age of patients (31%), and MON/HDL-C (14%). The influence of other factors on the oCAD risk was less noticeable

Cardiometabolic risk factors in predicting obstructive coronary artery disease in patients with non-ST-segment elevation acute coronary syndrome

Aim. To develop predictive models of obstructive coronary artery disease (OPCA) in patients with non-ST-segment elevation acute coronary syndrome (NSTE-ACS) based on the predictive potential of cardiometabolic risk (CMR) factors.Material and methods. This prospective observational cohort study included 495 patients with NSTE-ACS (median age, 62 years; 95% confidence interval [60; 64]), who underwent invasive coronary angiography (CAG). Two groups of persons were identified, the first of which consisted of 345 (69,6%) patients with OPCA (stenosis ≥50%), and the second — 150 (30,4%) without OPCA (<50%). The clinical and functional status of patients before CAG was assessed including 29 parameters. For data processing and analysis, the Mann-Whitney, Fisher, chi-squared tests and univariate logistic regression (LR) were used. In addition, for the development of predictive models, we used multivariate LR (MLR), support vector machine (SVM) and random forest (RF). The models was assessed using 4 metrics: area under the ROC-curve (AUC), sensitivity, specificity, and accuracy.Results. A comprehensive analysis of functional and metabolic status of patients made it possible to identify the CMR factors that have linear and nonlinear association with OPCA. Their weighting coefficients and threshold values with the highest predictive potential were determined using univariate LR. The quality metrics of the best predictive algorithm based on an ensemble of 10 MLR models were as follows: AUC — 0,82, specificity and accuracy — 0,73, sensitivity — 0,75. The predictors of this model were 7 categorical (total cholesterol (CS) ≥5,9 mmol/L, low-density lipoprotein cholesterol >3,5 mmol/L, waist-to-hip ratio ≥0,9, waist-to-height ratio ≥0,69, atherogenic index ≥3,4, lipid accumulation product index ≥38,5 cm*mmol/L, uric acid ≥356 pmol/L) and 2 continuous (high density lipoprotein cholesterol and insulin resistance index) variables.Conclusion. The developed algorithm for selecting predictors made it possible to determine their significant predictive threshold values and weighting coefficients characterizing the degree of influence on endpoints. The ensemble of MLR models demonstrated the highest accuracy of OPCA prediction before the CAG. The predictive accuracy of the SVM and RF models was significantly lower

Electrocardiographic, echocardiographic and lipid parameters in predicting obstructive coronary artery disease in patients with non-ST elevation acute coronary syndrome

Aim. To assess the predictive potential of electrocardiographic (ECG), echocardiographic, and lipid parameters for predicting obstructive coronary artery disease (oCAD) in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) prior to invasive coronary angiography (CA).Material and methods. This prospective observational cohort study included 525 patients with NSTE-ACS with a median age of 62 years who underwent invasive coronary angiography. Two groups were distinguished, the first of which consisted of 351 (67%) patients with oCAD (stenosis 50%), and the second — 174 (33%) without oCAD (<50%). Clinical and functional status of patients before CAG was assessed by 40 indicators. Mann-Whitney, Fisher, chi-squared, univariate logistic regression (LR) methods were used for data processing and analysis, while miltivariate LR (MLR), gradient boosting (XGBoost) and artificial neural networks (ANN) were used to develop predictive models. The quality of the models was assessed using 4 following metrics: area under the ROC curve (AUC), sensitivity (Se), specificity (Sp), and accuracy (Ac).Results. A comprehensive analysis of ECG, echocardiography and lipid profile parameters made it possible to identify factors that had linear and non-linear association with oCAD. LR were used to determine their weight coefficients and threshold values with the highest predictive potential. The quality metrics of the best predictive algorithm based on MLR were 0,81 for AUC, 0,74 for Sp and Ac, and 0,75 for Se. The predictors of this model were 4 categorical parameters (left ventricular (LV) ejection fraction of 42-60%, global LV longitudinal systolic strain <19%, low-density lipoprotein cholesterol >3,5 mmol/l, age >55 years in men and >65 years for women).Conclusion. The prognostic model developed on the basis of MLR made it possible to verify oCAD with high accuracy in patients with NSTE-ACS before invasive CA. Models based on XGBoost and ANN had less predictive value

Machine learning for assessing the pretest probability of obstructive and non-obstructive coronary artery disease

The review presents an analysis of publications on use of machine learning (ML) to assess the pretest probability of obstructive and non-obstructive coronary artery disease (CAD). Data on the high prevalence of non-obstructive CAD among patients referred for coronary angiography are presented, which served as a reason for the development of ML-based models for pretest assessment of coronary anatomy. The use of modern modeling technologies has great potential in verification of obstructive and non-obstructive CAD. It is emphasized that the improvement of prognostic models and their practical implementation is an important element of medical decision making and should be carried out with interdisciplinary cooperation of clinicians and information technology specialists

Machine learning as a tool for diagnostic and prognostic research in coronary artery disease

Machine learning (ML) are the central tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of large data, reveal hidden or non-obvious patterns and learn a new knowledge. The review presents an analysis of literature on the use of ML for diagnosing and predicting the clinical course of coronary artery disease. We provided information on reference databases, the use of which allows to develop models and validate them (European ST-T Database, Cleveland Heart Disease database, Multi-Ethnic Study of Atherosclerosis, etc.). The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. It is assumed that the improvement of ML-based models and their introduction into clinical practice will help support medical decision-making, increase the effectiveness of treatment and optimize health care costs

Vasospastic angina: pathophysiology and clinical significance

The review discusses an analysis of the literature on various aspects of the pathogenesis, diagnosis and treatment of vasospastic angina (VA). Data on the prevalence of coronary artery spasm (CAS) in various populations, as well as risk factors and triggers, are presented. We considered pathophysiological mechanisms of CAS, including hyperreactivity of coronary smooth muscle cells, endothelial dysfunction, nonspecific inflammation, oxidative stress, magnesium deficiency, autonomic imbalance, etc. The relationship of CAS with coronary atherosclerosis and thrombosis is emphasized. Modern recommendations for the diagnosis and treatment of VA are presented. Invasive verification of CAS is performed by pharmacological provocation tests with certain contraindications. Calcium antagonists and their combination with long-acting nitrates play a key role in the treatment of VA. Medications with a prospect for use in VA are Rho-kinase inhibitors, ATP-sensitive potassium channel activators, alpha-1 blockers. The management of patients with refractory VA and the prospects for endovascular treatment are discussed. It was noted that patients with multi-vessel VA are more likely to develop life-threatening arrhythmias and sudden death

Examination of pathological process pecularities caused by A/duck/Altai/469/14 H5N1 isolate of avian influenza virus in chickens

Data on examination of chickens intranasally infected with А/duck/Altai/469/14 H5N1 isolate of avian influenza virus for clinical signs and microscopic and gross pathomorphological lesions in their organs are given in the paper. The virus antigen was identified in chicken tissue lesions by immunohistochemical method

Study of avian influenza virus circulation in uvs-nuur migration area in Republic of Tyva

The paper demonstrates results of long-term monitoring research performed by the FGBI "ARRIAH" in the Russian part of Uvs-Nuur Lake water area in the Republic of Tyva. The monitoring was aimed at the detection of influenza A virus circulation in the population of wild migratory birds. Ecological and biological significance of the lake coastal area in the arrangement of epidemiological investigations was determined. Highly pathogenic avian influenza virus circulation was identified in populations of wild avifauna in Uvs-Nuur migration area