BrainMap

Mr. Cristian Rasvan Baicus

PhD

Professor - UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA"

Other affiliations

Professor - SPITALUL CLINIC "COLENTINA" BUCURESTI (Romania)

Researcher | Teaching staff

I am a physician, Professor of Internal Medicine and Clinical Research at Carol Davila University of Medicine and Pharmacy, and a senior clinician at Colentina Clinical Hospital in Bucharest. My academic work focuses on clinical epidemiology, evidence-based medicine, research methodology, autoimmune and systemic diseases, and multimorbidity. I have extensive experience in clinical research, systematic reviews, doctoral supervision, medical education, and the critical appraisal of research projects and scientific publications. I also serve as Deputy Editor-in-Chief of the Romanian Journal of Internal Medicine and regularly serve as a peer reviewer and research project evaluator. My professional goal is to connect rigorous clinical research with meaningful improvements in medical practice, education, and patient care.

Web of Science ResearcherID: C-3355-2008

Curriculum Vitae (09/05/2024)

Expertise & keywords

Developing machine learning assisted diagnostic and risk stratification tools for genetic dilated cardiomyopathies in the young Call name: P 5.1 - Proiecte de Cercetare Exploratorie - Competiția 2023
PN-IV-P1-PCE-2023-0990 2025 - 2027

Role in this project:

Coordinating institution: UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA"

Project partners: UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA" (RO)

Affiliation:

Project website: http://www.umfcd.ro

Abstract:

Dilated cardiomyopathy (DCM) is defined as left ventricular systolic dysfunction and/or dilatation, with broad etiological diversity and gene-environment interactions, holding an unfavourable prognosis related to the high-burden of life-threatening ventricular arrhythmias (VA). Several gene variants are associated with highly arrhythmic forms of DCM, some being relevant for therapeutic decisions. Genotype-phenotype correlations are of paramount importance for identification of risk markers for VA and sudden cardiac death (SCD). Using artificial intelligence for ECG and ultrasound analysis in predicting cardiac magnetic resonance (CMR) and genetic characteristics in DCM is the first aim of our study. The project will focus on generation of big data in 300 young DCM patients through deep phenotyping, development of machine learning based tools for predicting the genetic background, and development of a risk algorithm tool for VA and SCD. Using a phenotyping protocol, ECG, echocardiography, and demographics will be used as input for the data-driven model performing the prediction of CMR characteristics and genetic elements in DCM. Deep neural networks will be used for data learning. This study will allow us to obtain a description of phenotypic clustering in DCM, towards the second aim of the study consisting in the development of a prognostic algorithm in young DCM patients.

Dynamic assessment of cardiac function in acutely decompensated cirrhosis Call name: P 1 - SP 1.1 - Proiecte de cercetare Postdoctorală - PD-2021
PN-III-P1-1.1-PD-2021-0180 2022 - 2024

Role in this project:

Coordinating institution: SPITALUL CLINIC "COLENTINA" BUCURESTI

Project partners: SPITALUL CLINIC "COLENTINA" BUCURESTI (RO)

Affiliation:

Project website: https://dynacord.spitalulcolentina.ro/

Abstract:

This project aims to investigate cardiac function in patients with cirrhosis in the acute setting. Acute decompensation and acute-on-chronic liver failure are major events in the life of a patient as they herald disease progression and negative prognosis. Although systemic inflammation and organ failure are essential to understanding the evolution of the disease, so far there have been no investigations into the role cardiac function plays in this setting. This project will investigate the dynamic changes in cardiac function through echocardiography, electrocardiography, serum markers of inflammation (leukocytes, C-reactive protein, interleukin-6 and 8, soluble mannose receptor) and cardiac injury (troponin T, pro-brain natriuretic peptide) at different time-points (enrollment, 5-7 days after admission, 30 and 90 days) in a prospective, cohort, follow-up study of patients with cirrhosis admitted for acute decompensation. An analysis of variables derived from echocardiographic, biological and clinical data will be performed in order to identify significant associations between parameters of cardiac function, markers of inflammation, and severity of liver disease as assessed by specific scores (CLIF-C). We expect to show that cardiac function is related to inflammation and that it plays an independent, significant role in the development of organ failure and negative outcome (repeated decompensation, hospitalization, worsening of disease or death during follow-up).

Integrative approach for arrhythmic risk stratification in hypertrophic cardiomyopathy: an advanced cardiac imaging and artificial intelligence study. Call name: P 4 - Proiecte de Cercetare Exploratorie, 2020
PN-III-P4-ID-PCE-2020-2438 2021 - 2023

Role in this project:

Coordinating institution: UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA"

Project partners: UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA" (RO)

Affiliation:

Project website: https://clinicadecardiologiefundeni.ro

Abstract:

Hypertrophic cardiomyopathy (HCM), the most common inherited cardiomyopathy, is a challenging disease, often having an unpredictable clinical course. Atrial and ventricular arrhythmias contribute directly to the mortality and morbidity of HCM patients (pts). Because current risk stratification strategies for prevention of sudden death are imperfect, the identification of new parameters able to better reveal the arrhythmogenic substrate and risk of arrhythmias is of interest. The main objective of our study is to find new parameters to identify HCM pts at risk for arrhythmias - ventricular arrhythmias (VA), atrial fibrillation (AF). We will use an approach based on advanced imaging assessment of cardiac mechanics and dynamic left ventricular outflow tract obstruction, both at rest and during exercise. We aim to develop a machine-learning (ML) model able to improve the identification of high-risk cardiac phenotypes on speckle echocardiography data, using as ground truth information extracted from cardiac magnetic resonance (CMR). We intend to demonstrate that ML models, generated by the integration of clinical, ECG, CMR, rest and exercise echocardiographic data, have better ability in detecting and predicting arrhythmias compared to conventional methods. An integrative approach for arrhythmic risk stratification provided by imaging and artificial intelligence may become a decision-making tool able to reduce the VA- and AF-related morbidity and mortality in pts with HCM.

CONFIRMATION OF A PANEL OF NOVEL CANDIDATE PROTEOMIC BIOMARKERS IN SYSTEMIC SCLEROSIS PATIENTS Call name: P 1 - SP 1.1 - Proiecte de cercetare Postdoctorală
PN-III-P1-1.1-PD-2019-0118 2020 - 2022

Role in this project:

Coordinating institution: UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA"

Project partners: UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA" (RO)

Affiliation: UNIVERSITATEA DE MEDICINA SI FARMACIE "CAROL DAVILA" (RO)

Project website: https://umfcd.ro/cercetare-si-dezvoltare/proiecte/proiecte-nationale/pn-iii-p1-11-pd-2019-0118-confirmarea-unui-panel-de-biomarkeri-proteomici-candidat-la-pac-cu-sclerodermie-biossc/

Abstract:

Rationale: Systemic sclerosis (Ssc) is an autoimmune disease with incompletely known physiopathology being a challenge for both clinicians and researchers. Research efforts are focusing upon identifying novel and relevant biomarkers that have good predictive values for prognosis, stratification and monitoring therapeutic response of Ssc patients. Studies using high throughput genomic/proteomic platforms have identified numerous candidate biomarkers for Ssc. Nevertheless, independent studies and confirmation of all these candidate biomarkers are needed to be conducted in the clinical setting in order to translate the tremendous information gathered into clinical practice.

Aims: The aim of the project is confirmation of novel promising proteomic biomarkers identified using mass-spectrometry studies already performed on Ssc patients. The outcome of the project will be a panel of promising, confirmed biomarkers associated with clinical characteristics of Ssc that will be useful for clinical practice after validation on Ssc cohort studies.

Foreseen impact: The project has an important impact in translating basic knowledge of Ssc biomarkers into clinical use by conducting a confirmation and validation study of promising biomarkers in a specific clinical setting. It is an important intermediate but mandatory step. The project proposal is in line with the international quest for reliable blood biomarkers for disease/therapy monitoring in Ssc patients, pinpointing and confirming proteomic signature from candidate biomarkers identified with mass spectrometry studies. The project will be able to transform basic research results into clinical practice.

Methodology: In order to confirm selected biomarkers (members of chaperon group of proteins but also other promising candidates) a case control study is envisaged. Patients with Ssc will be selected and samples and data from the available biobank will be included in analysis. The project is divided into 4 specific Work packages.

An integrated approach to cardiovascular function for outcome prediction in severe aortic stenosis: the role of exercise echocardiography Call name: Exploratory Research Projects - PCE-2012 call
PN-II-ID-PCE-2012-4-0560 2013 - 2016

Role in this project: Key expert

Coordinating institution: UNIV.DE MEDICINA SI FARMACIE - CAROL DAVILA

Project partners: UNIV.DE MEDICINA SI FARMACIE - CAROL DAVILA (RO)

Affiliation: UNIV.DE MEDICINA SI FARMACIE - CAROL DAVILA (RO)

Project website: http://www.euroecolab.ro/Proiecte/Proiecte.html

Abstract:

Calcific aortic stenosis (AS) is the most common valvular heart disease, with an increasing prevalence due to the ageing population. Management of asymptomatic patients (pts) with severe AS is still under debate due to difficulties in accurately balancing the risk of intervention versus that of watchful waiting. Based on pathophysiological data we hypothesised that a comprehensive assessment of arterial stiffness, left ventricular (LV) and left atrial (LA) function at rest and during exercise could identify markers of increased risk in asymptomatic severe AS.

Using exercise echocardiography, we aim to assess for the first time the predictive value of exercise-induced response of parameters of LV systolic and diastolic function (LV myocardial deformation parameters, LV untwisting, E/E’ ratio, BNP serum level) and LA functional reserve in asymptomatic pts with severe AS. We also intend to perform a comparative assessment of vascular and cardiac function in asymptomatic versus symptomatic pts with severe AS and preserved LV ejection fraction and to assess the impact of aortic valve replacement on different parameters of cardiac function in symptomatic pts with severe AS evaluated at rest by state-of-the-art imaging techniques (carotid e-tracking, tissue Doppler/speckle tracking echocardiography, 3D-echocardiography). Incorporating these new parameters into a risk score for outcome prediction in asymptomatic severe AS may provide an useful tool for clinical decision making.

FILE DESCRIPTION	DOCUMENT
List of research grants as project coordinator	Download (170.7 kb) 14/04/2016
List of research grants as partner team leader	Download (109.02 kb) 14/04/2016
List of research grants as project coordinator or partner team leader
Significant R&D projects for enterprises, as project manager
R&D activities in enterprises
Peer-review activity for international programs/projects	Download (49.21 kb) 05/11/2017