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:
Men Behind the Wheel: Three Times More Violations and Accidents than Women
Men are three times more likely than women to commit traffic violations while driving and to be involved in accidents. Moreover, they are more likely to create situations on the road that are highly dangerous to others. Men are also twice as likely to drive under the influence and nearly one-third more likely to receive a prison sentence for reckless driving. Perhaps it comes down to cultural norms and the different attitudes men and women have toward driving. These are the conclusions reached by Anton Kazun, Assistant Professor at the HSE Faculty of Economic Sciences, and Research Assistant Mikhail Belov.
HSE Scientists Discover How to Predict Charitable Behaviour Through Physiological Reactions
Researchers at the HSE Institute for Cognitive Neuroscience have investigated how the emotional impact of advertising affects the amount people willing to donate to support animal welfare. To accomplish this, the researchers measured physiological responses such as heart rate, electrodermal activity, and facial expressions in individuals viewing various photos of dogs. The findings indicate that willingness to donate is most accurately predicted by heart rate and facial muscle activation. The study has been published in Social Psychology.
'We Are Creating the Medicine of the Future'
Dr Gerwin Schalk is a professor at Fudan University in Shanghai and a partner of the HSE Centre for Language and Brain within the framework of the strategic project 'Human Brain Resilience.' Dr Schalk is known as the creator of BCI2000, a non-commercial general-purpose brain-computer interface system. In this interview, he discusses modern neural interfaces, methods for post-stroke rehabilitation, a novel approach to neurosurgery, and shares his vision for the future of neurotechnology.
First Successful Attempt in 55 years: Physicists in Russia and Germany Confirm 1969 Experiment Results
A team of researchers, with the participation of physicists from HSE University, replicated the 1969 experiment on superconductivity and its properties. The scientists induced superconductivity by deliberately deteriorating the interfaces between the layers of superconductors and ferromagnets in the system, resulting in better performance of spin valves compared to the classical version, where the interfaces between the layers are ideal. This approach could lead to the development of more efficient devices for data storage and computing. The study findings have been published in the Beilstein Journal of Nanotechnology.
Healthy Nutrition Saves Public Funds: Strategies to Reduce Healthcare Costs in Russia
In Russia, the annual cost of treating type 2 diabetes alone exceeds 500 billion roubles. Promoting healthy nutrition programmes can ease the burden on the healthcare system and increase life expectancy. This was the conclusion reached by economists at HSE University after analysing global experiences with government involvement in promoting a healthy lifestyle.
Conscientious Individuals Live Longer
Personality traits such as conscientiousness, emotional stability, and an internal locus of control significantly influence one's lifestyle and longevity. Not only can personality traits influence health through beneficial and harmful habits but can also have a direct effect on mortality. Higher conscientiousness reduces the risk of premature death by 20 percentage points, while higher neuroticism increases it by 12 percentage points. These are the findings from a new study by Ksenia Rozhkova, Junior Research Fellow at the Laboratory for Labour Market Studies of the HSE Faculty of Economic Sciences.
Esports Players Play Better Online
In competitions, esports players, like other athletes, face stress and show worse results due to pressure. A substantial decrease takes place in the performance of esports players during overtime. This effect, however, is significantly mitigated in online competitions compared to live events—the difference can reach 30%. A study by a team of authors from HSE University’s Moscow and Perm campuses and European University Viadrina (Germany) explores the phenomenon of choking under pressure within the context of esports. The study was published in the Journal of Economic Behavior & Organization.
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.