Frantisek Sabovcik
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Explore the profile of Frantisek Sabovcik including associated specialties, affiliations and a list of published articles.
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18
Citations
69
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Recent Articles
1.
Ntalianis E, Cauwenberghs N, Sabovcik F, Santana E, Haddad F, Claes J, et al.
iScience
. 2024 Sep;
27(9):110792.
PMID: 39286486
Nowadays cardiorespiratory fitness (CRF) is assessed using summary indexes of cardiopulmonary exercise tests (CPETs). Yet, raw time-series CPET recordings may hold additional information with clinical relevance. Therefore, we investigated whether...
2.
Kuznetsova T, Daels Y, Ntalianis E, Santana E, Sabovcik F, Haddad F, et al.
Echocardiography
. 2024 Feb;
41(2):e15780.
PMID: 38372342
Purpose: There is a need for better understanding the factors that modulate left atrial (LA) dysfunction. Therefore, we determined associations of clinical and biochemical biomarkers with serial changes in echocardiographic...
3.
Ntalianis E, Cauwenberghs N, Sabovcik F, Santana E, Haddad F, Claus P, et al.
Front Cardiovasc Med
. 2023 Dec;
10:1263301.
PMID: 38099222
Objective: Identifying individuals with subclinical cardiovascular (CV) disease could improve monitoring and risk stratification. While peak left ventricular (LV) systolic strain has emerged as a strong prognostic factor, few studies...
4.
Cauwenberghs N, Verheyen A, Sabovcik F, Ntalianis E, Vanassche T, Brguljan J, et al.
Atherosclerosis
. 2023 Oct;
385:117331.
PMID: 37879154
Background And Aims: Circulating proteins reflecting subclinical vascular disease may improve prediction of atherosclerotic cardiovascular disease (ASCVD). We applied feature selection and unsupervised clustering on proteomic data to identify proteins...
5.
Cauwenberghs N, Sente J, Van Criekinge H, Sabovcik F, Ntalianis E, Haddad F, et al.
Diagnostics (Basel)
. 2023 Jun;
13(12).
PMID: 37370946
Integrative interpretation of cardiopulmonary exercise tests (CPETs) may improve assessment of cardiovascular (CV) risk. Here, we identified patient phenogroups based on CPET summary metrics and evaluated their predictive value for...
6.
Cauwenberghs N, Sente J, Sabovcik F, Ntalianis E, Hedman K, Claes J, et al.
Clin Physiol Funct Imaging
. 2023 Jun;
43(6):441-452.
PMID: 37317062
Background: Interpretation of cardiopulmonary exercise testing (CPET) results requires thorough understanding of test confounders such as anthropometrics, comorbidities and medication. Here, we comprehensively assessed the clinical determinants of cardiorespiratory fitness...
7.
Ichimura K, Santana E, Kuznetsova T, Cauwenberghs N, Sabovcik F, Chun L, et al.
Pulm Circ
. 2023 Apr;
13(2):e12216.
PMID: 37063750
Ventricular interdependence plays an important role in pulmonary arterial hypertension (PAH). It can decrease left ventricular (LV) longitudinal strain (LVLS) and lead to a leftward displacement ("transverse shortening") of the...
8.
Ntalianis E, Sabovcik F, Cauwenberghs N, Kouznetsov D, Daels Y, Claus P, et al.
J Am Soc Echocardiogr
. 2023 Mar;
36(7):778-787.
PMID: 36958709
Background: Early identification of individuals at high risk for developing cardiovascular (CV) events is of paramount importance for efficient risk management. Here, the authors investigated whether using unsupervised machine learning...
9.
Sabovcik F, Cauwenberghs N, Vens C, Kuznetsova T
Eur Heart J Digit Health
. 2023 Jan;
2(3):390-400.
PMID: 36713600
Aims: There is a need for better phenotypic characterization of the asymptomatic stages of cardiac maladaptation. We tested the hypothesis that an unsupervised clustering analysis utilizing echocardiographic indexes reflecting left...
10.
Sabovcik F, Ntalianis E, Cauwenberghs N, Kuznetsova T
Front Cardiovasc Med
. 2022 Nov;
9:1011071.
PMID: 36330000
Objective: To mitigate the burden associated with heart failure (HF), primary prevention is of the utmost importance. To improve early risk stratification, advanced computational methods such as machine learning (ML)...