Analysing Genetic Information Can Help Prevent Complications after Myocardial Infarction
Researchers at HSE University have developed a machine learning (ML) model capable of predicting the risk of complications—major adverse cardiac events—in patients following a myocardial infarction. For the first time, the model incorporates genetic data, enabling a more accurate assessment of the risk of long-term complications. The study has been published in Frontiers in Medicine.
Coronary artery disease (CAD), or ischaemic heart disease (IHD), is a condition characterised by insufficient blood and oxygen supply to the heart from narrowing or blockage of the coronary arteries. It is typically caused by plaques composed of fats and cholesterol that build up on the walls of blood vessels. Coronary heart disease may present as angina (chest pain), myocardial infarction (heart attack), or other problems.
According to WHO, ischaemic heart disease is the world’s biggest killer, responsible for 13% of the total deaths. Therefore, it is crucial to prescribe appropriate treatment to minimise the risks of complications and recurrences. Researchers at HSE University developed a model capable of predicting the probability of major adverse cardiac events following a myocardial infarction.
The scientists analysed data from patients admitted with myocardial infarction to the Surgut District Centre for Diagnostics and Cardiovascular Surgery between 2015 and 2024. Upon admission to the emergency department, medical researchers (cardiologists) explained the main points of the study protocol to the patients and obtained their informed consent to participate. The cardiologists then assessed the condition of the coronary arteries supplying the heart and based on their evaluation, either balloon angioplasty with stenting or coronary artery bypass grafting were performed. All patients received guideline-based therapy, including RAAS-blockers, beta-blockers, statins, and dual antiplatelet therapy. The information was documented in the patients' hospital medical records. For each patient, standard clinical measurements were taken, including blood pressure, body mass index, and cholesterol and glucose levels.
During the laboratory stage, DNA was isolated from the leukocyte rings in the collected blood samples and then frozen at −80°C for future genetic testing. The genotypes were determined based on a specific genetic variation (polymorphism) in the VEGFR-2 gene. The genetic marker VEGFR-2 is a component of the body's signalling system that regulates the growth of new blood vessels. There are three variations of the genotype—C/C, C/T, and T/T—differing in the variation of the DNA nucleotides cytosine (C) or thymine (T) in this region of the gene. Although the marker has been known for a long time, this was the first study to examine its impact on the prognosis of complications following myocardial infarction.
The authors evaluated the impact of 39 factors on the prognosis of risks such as cardiac death, recurrent acute coronary syndrome, stroke, and the need for repeat revascularisation, a procedure that helps restore blood flow in the arteries. To select the best model, the researchers trained and tested several machine learning algorithms: Gradient Boosting (CatBoost and LightGBM), Random Forest, Logistic Regression, and an AutoML approach.
The CatBoost model, a gradient boosting algorithm optimised for working with categorical data rather than numeric values, demonstrated the best performance. It makes predictions by sequentially building and training 'weak' decision trees, where each new tree corrects the errors of the previous ones. When building trees, the algorithm splits the data into two parts: the model is trained on one portion, while errors are calculated on the other. This reduces the risk of overfitting, where the model simply memorises the correct answers, and helps it identify general patterns for making predictions in new, unseen cases.
The influence of features on the model's accuracy was evaluated using the method of sequential feature addition, which assesses their contribution at each stage. The researchers selected the 9 most significant features: gender, body mass index, Charlson comorbidity index (which accounts for the presence of serious concomitant diseases), condition of the lateral wall of the left ventricle, degree of damage to the left coronary artery trunk, number of affected arteries, variant of the VEGFR-2 gene, choice between percutaneous coronary intervention or coronary artery bypass grafting, and statin dosage.
The results showed that the dose of statins, medications used to lower cholesterol levels in the blood, is the most important factor influencing the risk of complications. High doses of statins reduce this risk, particularly in patients with an unfavourable genotype. The VEGFR-2 polymorphism, specifically the presence of the T allele, was ranked fourth in terms of importance.
'Previously, genetic factors were not included in ML models, primarily because sequencing or even genotyping of individual nucleotides is not routinely performed in hospitals. In addition to standard measurements, we had access to data on polymorphism in the VEGFR-2 gene. This allowed us to compare this indicator with others and determine that the risk allele of the VEGFR-2 variant is one of the five most important factors for predicting long-term outcomes in patients with myocardial infarction,' explains Maria Poptsova, Head of the HSE International Laboratory of Bioinformatics and co-author of the paper.
The researchers emphasise that analysing genetic data contributes to creating more accurate and personalised models for predicting the risk of major adverse cardiovascular events in patients following a myocardial infarction.
'Cardiovascular diseases require resources for diagnosis, treatment, rehabilitation, and prevention, and therefore place a significant burden on the healthcare system. The introduction of such models into clinical practice could reduce mortality and the frequency of recurrent infarctions, optimise treatment, and ease the burden on healthcare professionals,' according to Alexander Kirdeev, Research Assistant at the International Laboratory of Bioinformatics and lead author of the paper.
The study was carried out in the framework of HSE University's 'Mirror Laboratories' project.
See also:
A New Tool Designed to Assess AI Ethics in Medicine Developed at HSE University
A team of researchers at the HSE AI Research Centre has created an index to evaluate the ethical standards of artificial intelligence (AI) systems used in medicine. This tool is designed to minimise potential risks and promote safer development and implementation of AI technologies in medical practice.
Smoking Habit Affects Response to False Feedback
A team of scientists at HSE University, in collaboration with the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, studied how people respond to deception when under stress and cognitive load. The study revealed that smoking habits interfere with performance on cognitive tasks involving memory and attention and impairs a person’s ability to detect deception. The study findings have been published in Frontiers in Neuroscience.
Russian Physicists Determine Indices Enabling Prediction of Laser Behaviour
Russian scientists, including researchers at HSE University, examined the features of fibre laser generation and identified universal critical indices for calculating their characteristics and operating regimes. The study findings will help predict and optimise laser parameters for high-speed communication systems, spectroscopy, and other areas of optical technology. The paper has been published in Optics & Laser Technology.
Children with Autism Process Auditory Information Differently
A team of scientists, including researchers from the HSE Centre for Language and Brain, examined specific aspects of auditory perception in children with autism. The scientists observed atypical alpha rhythm activity both during sound perception and at rest. This suggests that these children experience abnormalities in the early stages of sound processing in the brain's auditory cortex. Over time, these abnormalities can result in language difficulties. The study findings have been published in Brain Structure and Function.
Smartphones Not Used for Digital Learning among Russian School Students
Despite the widespread use of smartphones, teachers have not fully integrated them into the teaching and learning process, including for developing students' digital skills. Irina Dvoretskaya, Research Fellow at the HSE Institute of Education, has examined the patterns of mobile device use for learning among students in grades 9 to 11.
Working while Studying Can Increase Salary and Chances of Success
Research shows that working while studying increases the likelihood of employment after graduation by 19% and boosts salary by 14%. One in two students has worked for at least a month while studying full time. The greatest benefits come from being employed during the final years of study, when students have the opportunity to begin working in their chosen field. These findings come from a team of authors at the HSE Faculty of Economic Sciences.
Beauty in Details: HSE University and AIRI Scientists Develop a Method for High-Quality Image Editing
Researchers from theHSE AI Research Centre, AIRI, and the University of Bremen have developed a new image editing method based on deep learning—StyleFeatureEditor. This tool allows for precise reproduction of even the smallest details in an image while preserving them during the editing process. With its help, users can easily change hair colour or facial expressions without sacrificing image quality. The results of this three-party collaboration were published at the highly-cited computer vision conference CVPR 2024.
HSE Scientists Have Examined Potential Impact of Nuclear Power on Sustainable Development
Researchers at HSE University have developed a set of mathematical models to predict the impact of nuclear power on the Sustainable Development Index. If the share of nuclear power in the global energy mix increases to between 20% and 25%, the global Sustainable Development Index (SDI) is projected to grow by one-third by 2050. In scenarios where the share of nuclear power grows more slowly, the increase in the SDI is found to be lower. The study has been published in Nuclear Energy and Technology.
‘We Bring Together the Best Russian Scientists and AI Researchers at HSE University Site’
On October 25–26, 2024, HSE University’s AI and Digital Science Institute and the AI Research Centre hold the Fall into ML 2024 conference in Moscow. This year’s event will focus on the prospects in development of fundamental artificial intelligence, with SBER as its conference title partner.
HSE Scientists Have Developed a New Model of Electric Double Layer
This new model accounts for a wide range of ion-electrode interactions and predicts a device's ability to store electric charge. The model's theoretical predictions align with the experimental results. Data on the behaviour of the electric double layer (EDL) can aid in the development of more efficient supercapacitors for portable electronics and electric vehicles. The study has been published in ChemPhysChem.