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Projects

AFISBIO

Atrial Fibrillation in Relationship to Sleep Quality and Plasma Biomarkers.

APAF ESUS

Apelin in Prediction of Atrial Fibrillation after Embolic Stroke of Undetermined Source.

BioGuard

BIOGUARD is a unique project in the field of preventive cardiovascular medicine aiming to provide a widely available and simple patient-friendly method capable of identifying patients with high risk of coronary heart disease.

Artificial intelligence in medicine

We were always fascinated by the artificial intelligence and its great potential in the medical field. We have teamed up with the Slovak Technical University machine learning team in Trnava.

The effect of melatonin on blood clotting

Heart attacks occur more frequently during day than night. We wondered why and the curiosity led us to an experiment. We identified melatonin as the possible culprit and so a new area of our research begun.

Radiofrequency ablation and plasma biomarkers

Radiofrequency ablation is an invasive procedure that removes cardiac arrhythmia (atrial fibrillation). Although this procedure is effective, arrhythmias recur in more than 30% of the patients.

Precision medicine

Precision medicine is a new stage in the development of medicine. It is about linking a maximally personalized approach to the patient with genomics, proteomics, metabolomics and digital technologies such as artificial intelligence, telemonitoring, mHealth, virtual reality and others. Our goal is to bring these technologies to Slovakia and implement them into the medical practice.

InfoMAX

Web-based case report form (CRF) system developed by our IT department led by medical researchers. We were focusing on the most fluent workflow possible and intuitive user experience having in mind many inexperienced users and a usual data complexity.

BioGuard

BioGuard is a unique project in the field of preventive cardiovascular medicine aiming to provide a widely available and simple patient-friendly method capable of identifying patients with high risk of coronary heart disease.

Ischemic heart disease remains one of the most common causes of death in the world, despite substantial advances in diagnostics and therapy. Current preventive methods are mainly due to high time and material demands unable to detect every patient at risk and unfortunately, the first manifestation may often be myocardial infarction, heart failure or sudden death.

The main goal of this project is to create a smartphone application which, on the basis of a simple measurement, will enable the identification of high-risk patients who can then benefit from targeted diagnostic and therapy process. The cornerstones of this project are the use of machine learning and a unique algorithm for pulse wave photoplethysmographic detection, using only a smartphone camera. BioGuard is being developed in cooperation with Dutch company Happitech, creators of currently world’s first CE Certified heart rhythm Software Development Kit (SDK).

Data needed for training of the machine learning algorithms will be obtained from patients from several centres indicated for ergometric testing, a test designated for identification of ischemic heart disease by exercise-induced stress on special treadmill or bicycle. Patient enrolment is starting in the near future.

Afisbio

Atrial Fibrillation in Relationship to Sleep Quality and Plasma Biomarkers (AFISBIO) is a multicenter, observational study.

AFISBIO I

AFISBIO II

Atrial fibrillation is the most common sustained arrhythmia, inherently associated with an increased risk of stroke. This risk can be effectively reduced with appropriate long-term oral anticoagulation therapy however, the diagnostic process of atrial fibrillation is quite challenging. Continuous ECG monitoring is the most efficient diagnostic method today, but is often impractical due to its high cost and inconvenience for the patient. Furthermore, it is not an optimal method for screening.

A reliable, cost-effective and accurate diagnostic tool would therefore be a major turning point in management of patients with this arrhythmia. We see the potential in plasmatic biomarkers and finding a way how to diagnose atrial fibrillation from blood is the most important goal of AFISBIO research project.

Afisbio II

Following encouraging results of AFISBIO I, an extention of study has been launched: Atrial Fibrillation Onset in Relationship to Plasma Biomarkers - AFISBIO II.

Protocol design was optimazed in order to allow more participants to be enrolled and new markers tested. Extention phase will only include patients without history of AF, but with high risk for ischemic stroke and AF.

You can learn all about AFISBIO II here.

Afisbio I

Atrial Fibrillation onset in relationship to Sleep quality and plasma Biomarkers (AFISBIO) is a multicenter study aimed to clarify the relationship between various circulating substances in blood and a form of heart arrhythmia called atrial fibrillation.

Clinical objective of our study is to identify plasmatic biomarkers capable of assessing the risk of atrial fibrillation (AF). Originality of the project lies, among other things, in the analysis of circulating microRNAs.

AFISBIO I study was designed to include high-risk patients with AF and several cardiovascular comorbidities. The reason for such design was to clarify whether these biomarkers were specific only to atrial fibrillation or whether they reflected the pathophysiological mechanisms present in other cardiovascular diseases. We therefore chose to compare the group of patients with AF to a control group. The only difference between them was the presence of AF (propensity matching design).

The main goals of our study were:

1. To identify plasmatic biomarker with sufficient sensitivity and specificity for atrial fibrillation.

2. To assess screening ability of a 7-day ECG Holter compared to ECG event recorder.

Intermittent, short ECG recording appears to be more effective in detecting paroxysmal AF than a 24-hour Holter ECG monitoring. However, it is not known whether it is more effective than a 7-day ECG Holter monitoring. Thus, the fibrillation-free control group was screened with a 7-day ECG Holter as well as an ECG event recorder.

3. To compare the quality of sleep between the cohort with and without AF.

We hypothesized that the quality of sleep in patients with paroxysmal AF was worse, based on available literature. The quality of sleep was assessed through the use of a questionnaire.

Several candidate markers were tested:

- Commonly studied D-dimer, Fibrinogen, Troponin, NT-proBNP, CRP

- Oxidative stress markers (AGEs, RAGE, esRAGE) & Malondialdehyd (MDA)

- Apelin

- Matrix metalloproteinases (MMPs)

- Circulating microRNAs

Although a complete analysis and interpretation of results are still ahead of us, several biomarkers including advanced glycation end-products (AGEs), endogenous secretory receptor for advanced glycation end-products (esRAGE), microRNAs and Apelin show great promise as potential biomarkers of atrial fibrillation.

Artificial intelligence in medicine

We were always fascinated by the artificial intelligence and its great potential in the medical field. That’s why we have teamed up with the Slovak Technical University Machine Learning Team in Trnava. In 2017 we developed first predictive models capable of determining patient prognosis after a heart attack using machine learning algorithms. We are proud to be able to regularly contribute to this new and very promising area of medicine with our research.

PREPO AF – Prediction of Postoperative Atrial Fibrillation

STOP SHOCK – Score to predict shock

PREPO AF – Prediction of Postoperative Atrial Fibrillation

Atrial fibrillation is the most common arrhythmia following cardiac surgery. Despite contemporary improvements in cardiac surgery, incidence of postoperative atrial fibrillation (POAF) remained to a large extent unchanged, affecting 25% to 50% of patients depending on the type of procedure. POAF is associated with increased incidence of sternal and respiratory tract infections, renal and gastrointestinal dysfunction, higher rates of strokes, as well as increased short- and long-term mortality.

Medication capable of reducing POAF is available, however it is associated with significant adverse effects. Treating every patient poses a great risk due to the adverse effects of antiarrhythmic therapy. On the other hand, leaving patients untreated greatly increases incidence of POAF. The optimal solution to this problem is proper patient selection.

There were several attempts to stratify these patients and thus provide treatment guidance (e.g. the POAF score). However, the results are not sufficient for a clear recommendation.

The aim of this study is to develop a predictive model for POAF based on machine learning algorithms and to compare its predictive power with existing scoring systems on a large population of patients post cardiac surgery.

STOP SHOCK – Score to predict shock

Shock is a serious life-threatening condition resulting from insufficient blood flow to tissues. When untreated, it can rapidly progress to collapse of circulation and sudden death. Depending on the underlying cause, there are several types of shock. Among these, cardiogenic shock is associated with the worst prognosis. Despite contemporary improvements in diagnostic and treatment options, mortality remains incredibly high, reaching nearly 50%. Meaning that nearly every second patient affected by cardiogenic shock dies.

Cardiogenic shock is caused by inability of heart to pump sufficient amount of blood to body. At present, there are options available to completely replace the function of the heart and/or lungs, but the development of shock, rapidly damages all other organ systems and even the replacement of heart function (thereby essentially eliminating the primary cause), often does not reverse this condition.

The ability to identify high risk patients prior to developement of shock would allow to take preemptive measures and thus prevent the development of shock.

The aim of this study is to develop a predictive model for cardiogenic shock based on machine learning algorithms and to compare its predictive power with existing scoring systems on a large population of patients.

The effect of melatonin on blood clotting

In this study we focused on patients with type 2 diabetes mellitus, because the incidence of acute myocardial infarctions does not show normally observed circadian variation with a peak incidence during morning hours and a decline at night. We hypothesized that the possible explanation was the effect of melatonin on platelet aggregation. The goal of our experiment was therefore to determine whether melatonin, added to the blood of patients with diabetes mellitus, suppresses platelet aggregation as well as in healthy individuals.

Patients with diabetes and healthy participants were included in the study. A blood sample was taken from both groups to assess induced platelet aggregation after the addition of melatonin. These results were also compared to controls with added saline solution.

The results of our research show that in healthy individuals melatonin significantly inhibits platelet aggregation, whereas in type 2 diabetic patients, this inhibition does not occur and the antiplatelet effect of melatonin is significantly attenuated. These findings could provide the basis for answering the question concerning the absence of circadian variation of myocardial infarctions incidence in patients with diabetes mellitus. It is worth considering the extent to which melatonin contributes to the etiopathogenesis of the prothrombotic state in diabetic patients. Further research is however necessary.

Radiofrequency ablation and plasma biomarkers

Radiofrequency ablation is an invasive procedure for treatment of the most common cardiac arrhythmia - atrial fibrillation. Although this procedure is effective, arrhythmias recur in more than 30% of the patients. In this project we are trying to identify causes of the recurrence and to improve patient selection for this procedure by analyzing various biomarkers in blood samples.

Oxidative stress is an important contributor to the etiology of atrial fibrillation. Our aim was to study oxidative stress biomarkers in patients undergoing radiofrequency catheter ablation for paroxysmal atrial fibrillation and to highlight the potential of biomarkers in predicting long-term outcome of this procedure.

Patients diagnosed with paroxysmal atrial fibrillation (terminates spontaneously in less than seven days) and implanted ECG loop recorder who underwent catheter radiofrequency ablation were included in the study. Before the procedure, we determined the values of selected plasma markers from patients' blood samples.

After ablation, patients were divided into two groups based on the "atrial fibrillation burden" (time spent in fibrillation). Atrial fibrillation burden was recorded by the ECG loop recorder. The first group consisted of patients who "responded" to the procedure optimally, the so-called optimal responders. The second group consisted of patients characterized as sub-optimal responders.

Analysis of aquired data showed that advanced glycation end products (AGEs) were significantly higher in sub-optimal responders compared to optimal responders. Our results indicate a link between markers of oxidative tissue damage and the long-term outcome of radiofrequency ablation, and also the potential of AGEs to predict long-term outcome of ablation in patients with paroxysmal atrial fibrillation.

Precision medicine

Precision medicine is a new stage in the evolution of medicine. It is about a maximal personalization of health care achieved via genomics, proteomics, metabolomics and digital technologies such as artificial intelligence, telemonitoring, mHealth, virtual reality etc.

Premedix Academy provides research and technological background for the sister organization Premedix Clinic. We are testing new digital medical devices and building a tele-health infrastructure for patients. Furthermore, we are doing intensive research in genomics to improve genotyping reports and even better personalize the preventive and therapeutic strategies delivered in Premedix Clinic.

APAF ESUS

Apelin in Prediction of Atrial Fibrillation after Embolic Stroke of Undetermined Source (APAF ESUS) is a multicenter, observational, cohort study in patients after an embolic stroke of an unknown source (ESUS).

Goals

Our main goal is to study the ability of plasma apelin to predict atrial fibrillation (AF) in patients after an embolic stroke of unknown source (ESUS) who will be monitored using an implantable loop recorder.

Primary goal is to determine predictive value, specificity and sensitivity of apelin in the diagnosis of AF in patients after ESUS.

Secondary goal is to develop a scoring system based on patient history and plasma apelin levels in order to more accurately determine the risk of AF after ESUS.

Study population

The study will include at least 300 adult participants hospitalized for ischemic stroke of unclear etiology meeting ESUS criteria.

Patients will receive an implantable loop recorder and undergo annual follow-up.

Rationale

Atrial fibrillation, a type of cardiac arrhythmia, is often diagnosed in patients only in conjunction with other serious diseases, such as stroke. Anticoagulation therapy is one of the most effective preventive steps, but it must be prescribed early on. This treatment is of particular importance in high-risk patients who would benefit significantly from screening testing. Commonly used prolonged ECG monitoring is costly, inconvenient and often unavailable for and is therefore not an optimal strategy. The solution could be detection using a plasma biomarker, such as apelin.

Apelin is an endogenous peptide. APJ receptor for apelin is detectable in many central and peripheral tissues. APJ-apelin complex has a wide range of effects on cardiovascular system, including vasomotor tone regulation, angiogenesis, cardiac contractility, heart rate and the renin-angiotensin system. Apelin also plays an important role in the development and repair processes, prevention of ischemia–reperfusion injury, apoptosis, fibrotic changes and cardiac remodelling. Many of these processes are directly or indirectly linked to atrial fibrillation.

InfoMAX

Infomax is a web-based multiuser case report form (CRF) system developed by our IT department in cooperation with our medical researchers. The main goal was to provide the most fluent workflow and intuitive user experience possible considering the data complexity associated with clinical research, even for inexperienced users. This system allows simple collection of clinical data, thus providing solid grounds for quality research. Every clinical research has its own specifics making it difficult to address this problem in general. Our system was designed with reusable building blocks allowing us to deliver precisely tailored solutions for each project. InfoMax offers many unique features such as powerful reporting abilities, first-class interactive dashboards, document storage, automatic user notifications and many more. Privacy protection and data security is ensured by state of the art fine-grained mechanism.