A primer in artificial intelligence in cardiovascular medicine

AI in interventional cardiology: Innovations and challenges

Artificial Intelligence (AI) permeates all areas of our lives. Even now, we all use AI algorithms in our daily activities, and medicine is no exception. The potential of AI technology is hard to overestimate; AI has already proven its effectiveness in many fields of science and technology. A vast number of methods have been proposed and are being implemented in various areas of medicine, including interventional cardiology. A hallmark of this discipline is the extensive use of visualization techniques not only for diagnosis but also for the treatment of patients with coronary heart disease. The implementation of instrumental AI will reduce costs, in a broad sense. In this article, we provide an overview of AI research in interventional cardiology, practical applications, as well as the problems hindering the widespread use of neural network technologies in interventional cardiology.

Artificial Intelligence in Healthcare With an Emphasis on Public Health

Artificial Intelligence (AI), since its inception, has revolutionized multiple sectors, including healthcare in the 20th century, and has applications in data interpretation, leading to advancements in diagnostics, therapeutics, and clinical decision-making. AI is referred to as the capability of a software program to accurately analyze extrinsic information and utilize it for accomplishing desired goals and objectives through appropriate flexibility. It makes use of complicated operative algorithms to excel in human learning potential with overwhelming abilities to interpret large sets of data. The scope and implications of AI are consistently amplifying and have contributed significantly in nearly all phases of human life, especially healthcare. The integration of AI-related advancements would surely ameliorate the delivery of healthcare by allowing its accessibility, affordability, and level of care provided. For instance, reading CT scans is feasible by both AI as well as a radiologist. The screening of Tuberculosis is possible through AI via Chest X-rays with comparability in performance as molecular testing and mammography scans can predict the onset of breast cancer prior to the appearance of the ocular signs. Therefore, AI has been realized as one of the core areas by researchers and the government for public health benefit. For the same reason, it is imperative to adopt an ethically sound policy framework for guiding the further development of AI-based technologies and their application in public health. AI-based interpretations themselves cannot be fully trusted for their diagnostic decisions and judgements, and hence, it is vital to assess their accountability through all phases of development and deployment in the field of health. This article emphasizes the advancements in AI-based technologies, their assistance in public healthcare delivery systems, and their merits and demerits. It also explores the various ethical directives that need to be adhered to while utilizing it for public health welfare.

Generative Artificial Intelligence: Enhancing Patient Education in Cardiovascular Imaging

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education. By combining vision and language models, generative AI enables personalized multimedia content generation through natural language interactions, benefiting patient education in cardiovascular imaging. Simulations, chat-based interactions, and voice-based interfaces can enhance accessibility, especially in resource-limited settings. Despite its potential benefits, implementing generative AI in resource-limited countries faces challenges like data quality, infrastructure limitations, and ethical considerations. Addressing these issues is crucial for successful adoption. Ethical challenges related to data privacy and accuracy must also be overcome to ensure better patient understanding, treatment adherence, and improved healthcare outcomes. Continued research, innovation, and collaboration in generative AI have the potential to revolutionize patient education. This can empower patients to make informed decisions about their cardiovascular health, ultimately improving healthcare outcomes in resource-limited settings.

Attenuated reactive hyperemia after prolonged sitting is associated with reduced local skeletal muscle metabolism: insight from artificial intelligence

Blunted post-occlusive reactive hyperemia (PORH) after prolonged sitting (PS) has been used as evidence of microvascular dysfunction. However, it has not been determined if confounding variables are responsible for the reduction in PORH after PS. Therefore, the purpose of this study was to examine the PS-mediated changes in cardiovascular and metabolic factors that affect PORH using artificial intelligence (AI). We hypothesized that calf muscle metabolic rate (MMR) is attenuated after PS, which may reduce tissue hypoxia during an arterial occlusion (i.e., oxygen deficit) and PORH. Thirty-one subjects (male = 13, female = 18) sat for 2.5 h. A rapid-inflation cuff was placed around the thigh above the knee to generate an arterial occlusion. PORH was represented by the reoxygenation rate (RR) of the near-infrared spectroscopy (NIRS) tissue oxygenation index (TOI) after 5-min of arterial occlusion. An artificial intelligence model (AI) defined the stimulus-response relationship between the oxygen deficit (i.e., ΔTOI and TOI deficit), and RR with 65 previous PORH recordings. If the AI predicts the experimental RRs, then the change in RR is related to the change in the oxygen deficit. RR (Δ -0.27 ± 0.55 lnTOI%·s-1, P = 0.001), MMR (Δ -0.46 ± 0.61 lnTOI%·s-1, P < 0.001), ΔTOI (Δ -0.34 ± 0.62 lnTOI%, P < 0.001), and the TOI deficit (Δ -0.42 ± 0.68 lnTOI%·s, P < 0.001) were reduced after PS. In addition, strong linear associations were found between MMR and the TOI deficit (r2 = 0.900, P < 0.001) and ΔTOI (r2 = 0.871, P < 0.001). Furthermore, the AI accurately predicted the RRs pre- and post-PS (P = 0.471, P = 0.328, respectively). Therefore, blunted PORH after PS may be caused by attenuated MMR and not microvascular dysfunction.NEW & NOTEWORTHY Prolonged sitting reduces lower leg skeletal muscle metabolic rate in healthy individuals. Artificial intelligence revealed that impaired post-occlusive reactive hyperemia after prolonged sitting is related to a reduced stimulus for vasodilation and may not be evidence of microvascular dysfunction. Current post-occlusive reactive hyperemia protocols may be insufficient to assess micro- and macrovascular function after prolonged sitting.

Investigating Students' Perceptions towards Artificial Intelligence in Medical Education

Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.

Intersection of stem cell biology and engineering towards next generation in vitro models of human fibrosis

Human fibrotic diseases constitute a major health problem worldwide. Fibrosis involves significant etiological heterogeneity and encompasses a wide spectrum of diseases affecting various organs. To date, many fibrosis targeted therapeutic agents failed due to inadequate efficacy and poor prognosis. In order to dissect disease mechanisms and develop therapeutic solutions for fibrosis patients, in vitro disease models have gone a long way in terms of platform development. The introduction of engineered organ-on-a-chip platforms has brought a revolutionary dimension to the current fibrosis studies and discovery of anti-fibrotic therapeutics. Advances in human induced pluripotent stem cells and tissue engineering technologies are enabling significant progress in this field. Some of the most recent breakthroughs and emerging challenges are discussed, with an emphasis on engineering strategies for platform design, development, and application of machine learning on these models for anti-fibrotic drug discovery. In this review, we discuss engineered designs to model fibrosis and how biosensor and machine learning technologies combine to facilitate mechanistic studies of fibrosis and pre-clinical drug testing.

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

PURPOSE OF REVIEW

As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines.

Exploration of the efficacy of radiomics applied to left ventricular tomograms obtained from D-SPECT MPI for the auxiliary diagnosis of myocardial ischemia in CAD

To explore the feasibility and efficacy of radiomics with left ventricular tomograms obtained from D-SPECT myocardial perfusion imaging (MPI) for auxiliary diagnosis of myocardial ischemia in coronary artery disease (CAD). The images of 103 patients with CAD myocardial ischemia between September 2020 and April 2021 were retrospectively selected. After information desensitization processing, format conversion, annotation using the Labelme tool on an open-source platform, lesion classification, and establishment of a database, the images were cropped for analysis. The ResNet18 model was used to automate two steps (classification and segmentation) with five randomization, training and validation steps. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, negative predictive value, Youden's index, agreement rate, and kappa value were calculated as evaluation indexes of the classification results for each training-validation step; then, receiver operating characteristics (ROC) curves were drawn, and the areas under the curve (AUCs) were calculated. The Dice coefficient, intersection over union, and Hausdorff distance were calculated as evaluation indexes of the segmentation results for each training-validation step; then, the predicted images were exported. Under the existing conditions, the radiomics model used in this study had an AUC above 0.95 in identifying the presence or absence of myocardial ischemia; in the prediction of the extent of myocardial ischemia, its evaluation index distribution is also close to that of the gold standard. Radiomics can be feasibly applied to left ventricular tomograms obtained from D-SPECT MPI for auxiliary diagnosis. With more in-depth research and the development of technology, adding this method to the existing auxiliary diagnosis will likely further improve the diagnostic accuracy and efficiency, and patients will therefore benefit.

Routine Echocardiography and Artificial Intelligence Solutions

Introduction: Echocardiography is widely used because of its portability, high temporal resolution, absence of radiation, and due to the low-costs. Over the past years, echocardiography has been recommended by the European Society of Cardiology in most cardiac diseases for both diagnostic and prognostic purposes. These recommendations have led to an increase in number of performed studies each requiring diligent processing and reviewing. The standard work pattern of image analysis including quantification and reporting has become highly resource intensive and time consuming. Existence of a large number of datasets with digital echocardiography images and recent advent of AI technology have created an environment in which artificial intelligence (AI) solutions can be developed successfully to automate current manual workflow. Methods and Results: We report on published AI solutions for echocardiography analysis on methods' performance, characteristics of the used data and imaged population. Contemporary AI applications are available for automation and advent in the image acquisition, analysis, reporting and education. AI solutions have been developed for both diagnostic and predictive tasks in echocardiography. Left ventricular function assessment and quantification have been most often performed. Performance of automated image view classification, image quality enhancement, cardiac function assessment, disease classification, and cardiac event prediction was overall good but most studies lack external evaluation. Conclusion: Contemporary AI solutions for image acquisition, analysis, reporting and education are developed for relevant tasks with promising performance. In the future major benefit of AI in echocardiography is expected from improvements in automated analysis and interpretation to reduce workload and improve clinical outcome. Some of the challenges have yet to be overcome, however, none of them are insurmountable.

Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence

Intestinal obstruction is a common surgical emergency in children. However, it is challenging to seek appropriate treatment for childhood ileus since many diagnostic measures suitable for adults are not applicable to children. The rapid development of machine learning has spurred much interest in its application to medical imaging problems but little in medical text mining. In this paper, a two-layer model based on text data such as routine blood count and urine tests is proposed to provide guidance on the diagnosis and assist in clinical decision-making. The samples of this study were 526 children with intestinal obstruction. Firstly, the samples were divided into two groups according to whether they had intestinal obstruction surgery, and then, the surgery group was divided into two groups according to whether the intestinal tube was necrotic. Specifically, we combined 63 physiological indexes of each child with their corresponding label and fed them into a deep learning neural network which contains multiple fully connected layers. Subsequently, the corresponding value was obtained by activation function. The 5-fold cross-validation was performed in the first layer and demonstrated a mean accuracy (Acc) of 80.04%, and the corresponding sensitivity (Se), specificity (Sp), and MCC were 67.48%, 87.46%, and 0.57, respectively. Additionally, the second layer can also reach an accuracy of 70.4%. This study shows that the proposed algorithm has direct meaning to processing of clinical text data of childhood ileus.

Applications of Artificial Intelligence (AI) for cardiology during COVID-19 pandemic

Background and aims

Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart.

Methods

We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure.

Results

AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed.

Conclusions

Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.

Atopic dermatitis severity during exposure to air pollutants and weather changes with an Artificial Neural Network (ANN) analysis

BACKGROUND

Epidemiological studies have shown an association between global warming, air pollution, and allergic diseases. Several air pollutants, including volatile organic compounds, formaldehyde, toluene, nitrogen dioxide (NO₂ ), and particulate matter, act as risk factors for the development or aggravation of atopic dermatitis (AD). We evaluated the impact of air pollutants and weather changes on AD patients.

MATERIALS AND METHODS

Sixty AD patients ≥5 years of age (mean age: 23.5 ± 12.5 years), living in the Campania Region (Southern Italy), were followed for 18 months. The primary outcome was the effect of atmospheric and climatic factors on signs and symptoms of AD, assessed using the SCORAD (SCORing Atopic Dermatitis) index. We measured mean daily temperature (TOD), outdoor relative humidity (RH), diurnal temperature range (DTR), precipitation, particulate with aerodynamic diameter ≤ 10 μm (PM₁₀ ), NO₂ , tropospheric ozone (O₃ ), and total pollen count (TPC). A multivariate logistic regression analysis was used to examine the associations of AD signs and symptoms with these factors. An artificial neural network (ANN) analysis investigated the relationships between weather changes, environmental pollutants, and AD severity.

RESULTS

The severity of AD symptoms was positively correlated with outdoor temperatures (TOD, DTR), RH, precipitation, PM₁₀ , NO₂ , O₃ , and TPC. The ANN analysis also showed a good discrimination performance (75.46%) in predicting disease severity based on environmental pollution data, but weather-related factors were less predictive.

CONCLUSION

The results of the present study provide evidence that weather changes and air pollutions have a significant impact on skin reactivity and symptoms in AD patients, increasing the severity of the dermatitis. The knowledge of the single variables proportion on AD severity symptoms is important to propose alerts for exacerbations in patients with AD of each age. This finding represents a good starting point for further future research in an area of increasingly growing interest.

The history of transcatheter aortic valve implantation: The role and contribution of an early believer and adopter, the Netherlands

This paper describes the history of transcatheter aortic valve implantation (TAVI) from its preclinical phase during which visionary pioneers developed its concept and prototype valves against strong head wind to first application in clinical practice (2002) and the clinical and scientific role of an early believer and adopter, the Netherlands (2005).

Clinical applications of artificial intelligence in cardiology on the verge of the decade

Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.

Neural Network-Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study

Background

Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma.

Objective

The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma.

Methods

A genetic algorithm–modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy.

Results

The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm–modified neural network predicted the level of effect with high accuracy for anti–heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV₁), forced vital capacity (FVC), the ratio of FEV₁/FVC, and forced expiratory flow (FEF_25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV₁: 98.1%; FVC: 97.5%; FEV₁/FVC ratio: 97%; and FEF_25%-75%: 96.7%, respectively).

Conclusions

The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. In order to improve and optimize CVD outcomes, artificial intelligence techniques have the potential to radically change the way we practice cardiology, especially in imaging, offering us novel tools to interpret data and make clinical decisions. AI techniques such as machine learning and deep learning can also improve medical knowledge due to the increase of the volume and complexity of the data, unlocking clinically relevant information. Likewise, the use of emerging communication and information technologies is becoming pivotal to create a pervasive healthcare service through which elderly and chronic disease patients can receive medical care at their home, reducing hospitalizations and improving quality of life. The aim of this review is to describe the contemporary state of artificial intelligence and digital health applied to cardiovascular medicine as well as to provide physicians with their potential not only in cardiac imaging but most of all in clinical practice.

Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction: A Machine Learning Study

BACKGROUND

OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks.

RESEARCH QUESTION

Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI?

STUDY DESIGN AND METHODS

This is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently.

RESULTS

There were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P < .01).

INTERPRETATION

The integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event.

Application of artificial intelligence in cardiac CT: From basics to clinical practice

Research into the possibilities of AI in cardiac CT has been growing rapidly in the last decade. With the rise of publicly available databases and AI algorithms, many researchers and clinicians have started investigations into the use of AI in the clinical workflow. This review is a comprehensive overview on the types of tasks and applications in which AI can aid the clinician in cardiac CT, and can be used as a primer for medical researchers starting in the field of AI. The applications of AI algorithms are explained and recent examples in cardiac CT of these algorithms are further elaborated on. The critical factors for implementation in the future are discussed.

Epigenetic-sensitive pathways in personalized therapy of major cardiovascular diseases

The complex pathobiology underlying cardiovascular diseases (CVDs) has yet to be explained. Aberrant epigenetic changes may result from alterations in enzymatic activities, which are responsible for putting in and/or out the covalent groups, altering the epigenome and then modulating gene expression. The identification of novel individual epigenetic-sensitive trajectories at single cell level might provide additional opportunities to establish predictive, diagnostic and prognostic biomarkers as well as drug targets in CVDs. To date, most of studies investigated DNA methylation mechanism and miRNA regulation as epigenetics marks. During atherogenesis, big epigenetic changes in DNA methylation and different ncRNAs, such as miR-93, miR-340, miR-433, miR-765, CHROME, were identified into endothelial cells, smooth muscle cells, and macrophages. During man development, lipid metabolism, inflammation and homocysteine homeostasis, alter vascular transcriptional mechanism of fundamental genes such as ABCA1, SREBP2, NOS, HIF1. At histone level, increased HDAC9 was associated with matrix metalloproteinase 1 (MMP1) and MMP2 expression in pro-inflammatory macrophages of human carotid plaque other than to have a positive effect on toll like receptor signaling and innate immunity. HDAC9 deficiency promoted inflammation resolution and reverse cholesterol transport, which might block atherosclerosis progression and promote lesion regression. Here, we describe main human epigenetic mechanisms involved in atherosclerosis, coronary heart disease, ischemic stroke, peripheral artery disease; cardiomyopathy and heart failure. Different epigenetics mechanisms are activated, such as regulation by circular RNAs, as MICRA, and epitranscriptomics at RNA level. Moreover, in order to open new frontiers for precision medicine and personalized therapy, we offer a panoramic view on the most innovative bioinformatic tools designed to identify putative genes and molecular networks underlying CVDs in man.

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

BACKGROUND

Cardiovascular diseases kill approximately 17 million people globally every year, and they mainly exhibit as myocardial infarctions and heart failures. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the needs of the body.Available electronic medical records of patients quantify symptoms, body features, and clinical laboratory test values, which can be used to perform biostatistics analysis aimed at highlighting patterns and correlations otherwise undetectable by medical doctors. Machine learning, in particular, can predict patients' survival from their data and can individuate the most important features among those included in their medical records.

METHODS

In this paper, we analyze a dataset of 299 patients with heart failure collected in 2015. We apply several machine learning classifiers to both predict the patients survival, and rank the features corresponding to the most important risk factors. We also perform an alternative feature ranking analysis by employing traditional biostatistics tests, and compare these results with those provided by the machine learning algorithms. Since both feature ranking approaches clearly identify serum creatinine and ejection fraction as the two most relevant features, we then build the machine learning survival prediction models on these two factors alone.

RESULTS

Our results of these two-feature models show not only that serum creatinine and ejection fraction are sufficient to predict survival of heart failure patients from medical records, but also that using these two features alone can lead to more accurate predictions than using the original dataset features in its entirety. We also carry out an analysis including the follow-up month of each patient: even in this case, serum creatinine and ejection fraction are the most predictive clinical features of the dataset, and are sufficient to predict patients' survival.

CONCLUSIONS

This discovery has the potential to impact on clinical practice, becoming a new supporting tool for physicians when predicting if a heart failure patient will survive or not. Indeed, medical doctors aiming at understanding if a patient will survive after heart failure may focus mainly on serum creatinine and ejection fraction.