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

ROLE OF CYTOKINE-MEDIATED MECHANISMS IN DEVELOPMENT OF POST-TRAUMATIC MANDIBULAR OSTEOMYELITIS

Osteomyelitis of the lower jaw is one of the urgent problems of modern medicine. There are many reasons for the evolvement of purulent necrotic processes of the jaw bones, including the role of disorders in the systems of innate and adaptive immunity. The aim of the study was to determine the content of TNFα, IL-17, IL-4 in serum and mixed saliva in patients with uncomplicated mandibular fractures and posttraumatic osteomyelitis to determine the possibility of using these indicators for early diagnosis of posttraumatic complications. The article presents the results of a study of tumor necrosis factor α (TNFα), interleukin-17 (IL-17) and interleukin-4 (IL-4) cytokines in serum and mixed saliva in patients with uncomplicated mandibular fracture and post-traumatic osteomyelitis at the first and tenth days of observation. By means of single-layer neural networks, binary classifiers were built, allowing patients to be stratified by the clinical form of the disease and to predict its course. The probability of uncomplicated mandibular fracture is described by the ratio P = 1/(1+e-z), where the index z is determined by the level of TNFα, IL-17, and IL-4 at the first and tenth day of observation. The simulation confirmed high prognostic significance of serum TNFα and IL-17 for early verification of posttraumatic osteomyelitis, which was confirmed by the OTC and ROC indices, which varied from 87 to 100% in different models. Models 4 and 5, where TNFα recorded on the tenth day of the study was used as predictors, and a combination of TNFα and IL-17 obtained on the first day of hospitalization, were the most accurate. Modeling the results of the study of immunological indicators in the mixed saliva showed that the predictive properties have only IL-4 and IL-17, was on the tenth day of hospitalization that distinguishes these binary classifiers from similar indexes, derive from the levels of cytokines in blood serum. The results of the study indicate the important role of disorders in the system of рro- and anti-inflammatory cytokines in pathogenesis of post-traumatic osteomyelitis

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

Algorithm for selecting predictors and prognosis of atrial fibrillation in patients with coronary artery disease after coronary artery bypass grafting

Aim. To develop an algorithm for selecting predictors and prognosis of atrial fibrillation (AF) in patients with coronary artery disease (CAD) after coronary artery bypass grafting (CABG).Material and methods. This retrospective study included 886 case histories of patients with CAD aged 35 to 81 years (median age, 63 years; 95% confidence interval [63; 64]), who underwent isolated CABG under cardiopulmonary bypass. Eighty-five patients with prior AF were excluded from the study. Two groups of persons were identified, the first of which consisted of 153 (19,1%) patients with newly recorded AF episodes, the second — 648 (80,9%) patients without cardiac arrhythmias. Preoperative clinical and functional status was assessed using 100 factors. Chi-squared, Fisher, and Mann-Whitney tests, as well as univariate logistic regression (LR) were used for data processing and analysis. Multivariate LR and artificial neural networks (ANN) were used to develop predictive models. The boundaries of significant ranges of potential predictors were determined by stepwise assessment of the odds ratio and p-value. The model accuracy was assessed using 4 metrics: area under the ROC-curve (AUC), sensitivity, specificity, and accuracy.Results. A comprehensive analysis of preoperative status of patients made it possible to identify 11 factors with the highest predictive potential, linearly and nonlinearly associated with postoperative AF (PAF). These included age (55-74 years for men and 60-78 years for women), anteroposterior and superior-inferior left atrial dimensions, transverse and longitudinal right atrial dimensions, tricuspid valve regurgitation, left ventricular end systolic dimension >49 mm, RR length of 1000-1100 ms, PQ length of 170-210 ms, QRS length of 50-80 ms, QT >420 ms for men and >440 ms for women, and heart failure with ejection fraction of 4560%. The metrics of the best predictive ANN model were as follows: AUC — 0,75, specificity — 0,73, sensitivity — 0,74, and accuracy — 0,73. These values in best model based on multivariate LR were lower (0,75; 0,7; 0,68 and 0,7, respectively).Conclusion. The developed algorithm for selecting predictors made it possible to verify significant predictive ranges and weight coefficients characterizing their influence on PAF development. The predictive model based on ANN has a higher accuracy than multivariate HR

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

СРАВНИТЕЛЬНАЯ ОЦЕНКА СИЛЫ ДЫХАТЕЛЬНЫХ МЫШЦ У БОЛЬНЫХ БРОНХИАЛЬНОЙ АСТМОЙ, ХРОНИЧЕСКОЙ ОБСТРУКТИВНОЙ БОЛЕЗНЬЮ ЛЕГКИХ И С ИХ СОЧЕТАНИЕМ

The objective of the study: to compare the strength of respiratory muscle in the patients with asthma, chronic obstructive pulmonary disease (COPD) and the combination of these two conditions, to assure the informativeness of the indicators for verification of these diseases.Subjects and methods. In the in-patient unit, 130 patients with a severe course of asthma, COPD and combination of asthma + COPD were examined. The strength expiratory (MEP) and inspiratory (MIP, SNIP) indicators of respiratory muscles were registered using MicroRPM (CareFusion, United Kingdom), their due values were calculated. The logistic regression models were used to assure informativeness of MEP, MIP, and SNIP for verification of certain forms of bronchial obstruction.Results. The reduction in the strength of expiratory and inspiratory muscles was observed in the patients from all groups. Dysfunction of expiratory muscle dominated in asthma patients, while in COPD patients and those with both conditions it was diaphragm dysfunction. The correlation analysis demonstrated the dependence of respiratory muscle strength on the intensity of bronchial obstruction and lung hyperinflation, skeletal muscles mass, body mass index, respiratory discomfort, and functional state of the patients. It was found out that the ratio of MEP/MIP had a high prognostic value and significantly improved the accuracy of the models of stratification of those examined by nosologic groups.Conclusion. Testing respiratory muscle strength makes an informative tool for comprehensive assessment of respiratory functions in patients with different clinical variants of bronchial obstruction.Цель исследования: сравнительная оценка силы дыхательных мышц (ДМ) у больных бронхиальной астмой (БА), хронической обструктивной болезнью легких (ХОБЛ) и с их сочетанием, определение информативности ее индикаторов для верификации этих состояний.Материалы и методы. В стационаре обследовано 130 больных с тяжелым течением БА, ХОБЛ и с сочетанием БА + ХОБЛ. Регистрировали силовые индикаторы экспираторных (MEP) и инспираторных (MIP, SNIP) ДМ на аппарате MicroRPM (CareFusion, Великобритания), рассчитывали их должные величины. Информативность показателей MEP, MIP и SNIP для верификации отдельных форм бронхиальной обструкции определяли с помощью моделей логистической регрессии.Результаты. У больных всех групп зафиксировано снижение силы экспираторных и инспираторных ДМ. Дисфункция экспираторных мышц доминировала при БА, а диафрагмы – при ХОБЛ и сочетании БА + ХОБЛ. Корреляционный анализ показал зависимость силы ДМ от выраженности бронхиальной обструкции и гиперинфляции легких, массы скелетной мускулатуры, индекса массы тела, респираторного дискомфорта и функционального статуса больных. Установлено, что отношение MEP/MIP обладает высоким прогностическим потенциалом и значительно повышает точность моделей стратификации обследованных по нозологическим группам.Заключение. Исследование силы ДМ является информативным инструментом в комплексной оценке респираторных функций у больных с различными клиническими вариантами бронхиальной обструкции

COMPARATIVE ASSESSMENT OF RESPIRATORY MUSCLE STRENGTH IN THOSE WITH ASTHMA, CHRONIC OBSTRUCTIVE PULMONARY DISEASE AND COMBINATION OF THESE TWO CONDITIONS

The objective of the study: to compare the strength of respiratory muscle in the patients with asthma, chronic obstructive pulmonary disease (COPD) and the combination of these two conditions, to assure the informativeness of the indicators for verification of these diseases.Subjects and methods. In the in-patient unit, 130 patients with a severe course of asthma, COPD and combination of asthma + COPD were examined. The strength expiratory (MEP) and inspiratory (MIP, SNIP) indicators of respiratory muscles were registered using MicroRPM (CareFusion, United Kingdom), their due values were calculated. The logistic regression models were used to assure informativeness of MEP, MIP, and SNIP for verification of certain forms of bronchial obstruction.Results. The reduction in the strength of expiratory and inspiratory muscles was observed in the patients from all groups. Dysfunction of expiratory muscle dominated in asthma patients, while in COPD patients and those with both conditions it was diaphragm dysfunction. The correlation analysis demonstrated the dependence of respiratory muscle strength on the intensity of bronchial obstruction and lung hyperinflation, skeletal muscles mass, body mass index, respiratory discomfort, and functional state of the patients. It was found out that the ratio of MEP/MIP had a high prognostic value and significantly improved the accuracy of the models of stratification of those examined by nosologic groups.Conclusion. Testing respiratory muscle strength makes an informative tool for comprehensive assessment of respiratory functions in patients with different clinical variants of bronchial obstruction

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