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

Chronic obstructive pulmonary disease and cerebrovascular diseases: functional and clinical aspect of comorbidity

Literature data of chronic obstructive pulmonary disease (COPD) and cerebrovascular diseases (CVD) comorbidity are represented in this review. Key aspects of this interaction and its importance for clinical medicine have been considered. CVD and COPD are the main mortality factors in adults, which contribute to great economic wastes. The incidence of chronic cerebral ischemia for COPD patients is almost three times as high as for general population. The incidence of ischemic stroke for COPD patients is 1,2 times higher than in general population. For hemorrhagic stroke and subarachnoid haemorrhages, this figures are 1,3 and 1,46 respectively. Chronic systemic inflammation, tissue hypoxia and oxidative stress play the crucial role in respiratory and cerebrovascular comorbidity. Metabolites of these processes (especially proinflammatory cytokines, reactive oxygen species, C-reactive protein and some neurotrophins) increase the permeability of blood-brain barrier, destroy brain cells and activate atherogenesis in pre - and intracerebral arteries. Endothelial dysfunction affects autoregulation of cerebral circulation. Systemic symptoms of COPD are closely associated with different structural-functional disorders of the brain such as reduction in white matter integrity, grey matter volume reduction and cerebral microbleeds. Also, venous encephalopathy is developed as a result of intrathoracic pressure elevation and stasis in superior vena cava system. These processes result in neurological symptomatology. The intensity of symptoms depends on COPD severity. The occurrence of cognitive impairment, psychic tension, depression, panic disorders also increases. However COPD and CVD comorbidity is an important problem of modern medicine, pathophysiologic mechanisms and clinic aspects of this problem remain unresolved. Understanding of their role opens perspectives for rational pharmacotherapy

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

EVALUATION OF CYTOKINE-MEDIATED MECHANISMS INVOLVED IN DEVELOPMENT OF RESPIRATORY MUSCLE DYSFUNCTION IN THE PATIENTS WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Respiratory muscle (RM) strength was studied in 85 men with exacerbations of chronic obstructive pulmonary disease (COPD). The strength indicators of expiratory (MEP) and inspiratory pressure (MIP, SNIP) in oral cavity were registered by means of the MicroRPM device (CareFusion, UK), as well as intranasal pressure levels by SNIP test. The measured MEP, MIP и SNIP values were compared to the proper indices. Serum concentrations of cytokines (IL-4, IL-6, IL-10, IL-17A, IL-21, TNFα, IFNγ and TGF-β) were determined. The results of the study were processed by means of canonical analysis and by clustering methods. Expiratory RM dysfunction was recorded in mild COPD, expiratory-inspiratory RM dysfunction was recorded in moderate COPD and the diaphragm dysfunction was recorded in severe COPD. Three groups of patients with different combinations of RM strength indicators and immune parameters were identified by means of cluster analysis. The cytokine profile in the first cluster was characterized by maximal concentrations of IL-17A, IL-21, TNFα and TGF-β, whereas RM strength indexes showed minimal values. In the second cluster, a decrease of RM strength indicators by 25-40% against control was associated with a sharp rise of IL-6, along with moderate increase of IL-21 and TGF-βconcentrations. In the third cluster, maximal levels of IL-6, IL-10 and IFNγwere registered, along with low levels of IL-17A, IL-21 and TGF-β concentrations, whereas MEP, MIP и SNIP values did not sufficiently differ from their levels in second cluster. The results of canonical and correlation analysis indicated to interconnections between either certain cytokines, or their pool with the RM strength indicators, dyspnea severity and functional state of COPD patients, thus suggesting involvement of cytokine-mediated mechanisms in pathogenesis of the respiratory muscle dysfunction

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

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

Использование функциональных проб в оценке артериальной ригидности у больных бронхиальной астмой

The purpose of the research was to study mechanical properties of the aorta using functional tests in patients with asthma. We examined 54 asthmatic patients and 25 healthy volunteers using noninvasive arteriography (arteriograph TensioClinic TL1, TensioMed, Hungary), exercise (PE) and nitroglycerine (NG) tests. Transitory increase in aortal stiffness was observed in patients with exacerbation of severe and moderate asthma. The degree of this increase closely correlated to severity of the disease, ventilation disorders, and hypoxemia. These results suggest higher cardiovascular risk in such patients. Patients with asthma demonstrated amplified or weakened reaction of aortic pulse wave velocity (aPWV) to the PE depending on baseline values of these parameters: higher aPWV corresponded to weaker reaction to PE, and vise versa. As a whole, reduction in arterial stiffness during the NG test in asthma patients was significantly less than in healthy persons. The aPWV reaction to NG was more prominent during exacerbations of severe asthma. Therefore, functional tests investigating mechanical properties of aorta substantially supplemented traditional arteriography. Pathological reaction to PE was seen in some patients with stable asthma and normal aPWV values.Целью исследования было изучение механических свойств аорты у пациентов с бронхиальной астмой (БА) с функциональными пробами. Обследовано 54 больных БА и 25 здоровых добровольцев соответствующего возраста методом неинвазивной артериографии (артериограф TensioClinic TL1 (TensioMed, Венгрия)) с пробами с физической нагрузкой (ФН) и нитроглицерином (НТГ). У больных среднетяжелой и тяжелой БА в период обострения наблюдается транзиторное повышение жесткости аорты. Степень ее увеличения тесно коррелирует с тяжестью заболевания, выраженностью вентиляционных нарушений и уровнем гипоксемии. Эти изменения свидетельствуют об повышении кардиоваскулярного риска у данной категории больных. При проведении пробы с ФН среди пациентов с БА выявляются лица с усиленной и ослабленной реакцией аортальной скорости пульсовой волны (СПВА) на нагрузку. Выраженность реакции СПВА на ФН зависит от исходных значений этих показателей: более высоким базальным значениям СПВА соответствует низкая реакция на ФН и наоборот. В целом в группе больных БА снижение артериальной ригидности после приема НТГ достоверно меньше, чем у здоровых лиц. При обострении тяжелой БА реакция СПВА на НТГ усилена. Степень снижения артериальной ригидности в ответ на НТГ связана с уровнем гипоксемии и длительностью течения заболевания. Использование функциональных тестов для исследования механических свойств аорты существенно расширяет возможности традиционного артериографического исследования. Так, среди больных БА с нормальными значениями СПВА в стадии ремиссии при ФН выявляются лица с патологически измененной реакцией на нагрузку