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Multiparametric Strategy to Predict Early Disease Decompensation in Asymptomatic Severe Aortic Regurgitation
R. Kočková, H. Línková, Z. Hlubocká, K. Mědílek, M. Tuna, J. Vojáček, I. Skalský, Š. Černý, J. Malý, J. Hlubocký, T. Mizukami, C. De Colle, M. Pěnička
Language English Country United States
Document type Multicenter Study, Journal Article
NLK
Free Medical Journals
from 2008 to 1 year ago
Open Access Digital Library
from 2008-07-01
- MeSH
- Aortic Valve Insufficiency * diagnostic imaging etiology surgery MeSH
- Adult MeSH
- Echocardiography MeSH
- Ventricular Function, Left MeSH
- Middle Aged MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Natriuretic Peptide, Brain MeSH
- Stroke Volume MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
BACKGROUND: Use of the current echocardiography-based indications for aortic regurgitation (AR) surgery might result in late valve replacement at the stage of irreversible myocardial damage. Therefore, we aimed to identify simple models combining multiple echocardiography or magnetic resonance imaging (MRI)-derived indices and natriuretic peptides (BNP [brain natriuretic peptide] or NT-proBNP [N-terminnal pro-B type natriuretic peptide]) to predict early disease decompensation in asymptomatic severe AR. METHODS: This prospective and multicenter study included asymptomatic patients with severe AR, preserved left ventricular ejection fraction (>50%), and sinus rhythm. The echocardiography and MRI images were analyzed centrally in the CoreLab. The study end point was the onset of indication for aortic valve surgery as per current guidelines. RESULTS: The derivative cohort consisted of 127 asymptomatic patients (age 45±14 years, 84% males) with 41 (32%) end points during a median follow-up of 1375 (interquartile range, 1041-1783) days. In multivariable Cox regression analysis, age, BNP, 3-dimensional vena contracta area, MRI left ventricular end-diastolic volume index, regurgitant volume, and a fraction were identified as independent predictors of end point (all P<0.05). However, a combined model including one parameter of AR assessment (MRI regurgitant volume or regurgitant fraction or 3-dimensional vena contracta area), 1 parameter of left ventricular remodeling (MRI left ventricular end-diastolic volume index or echocardiography 2-dimensional global longitudinal strain or E wave), and BNP showed significantly higher predictive accuracy (area under the curve, 0.74-0.81) than any parameter alone (area under the curve, 0.61-0.72). These findings were confirmed in the validation cohort (n=100 patients, 38 end points). CONCLUSIONS: In asymptomatic severe AR, multimodality and multiparametric model combining 2 imaging indices with natriuretic peptides, showed high accuracy to identify early disease decompensation. Further prospective studies are warranted to explore the clinical benefit of implementing these models to guide patient management. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02910349.
1st Department Medicine Cardioangiology University Hospital Hradec Králové Sokolská Czech Republic
Cardiothoracic Surgery Department Na Homolce Hospital Czech Republic
Cardiovascular Center Aalst Belgium
Department of Advanced Biomedical Sciences University Federico 2 Naples Italy
Department of Cardiac Surgery University Hospital Hradec Králové Hradec Králové Czech Republic
Department of Cardiology General University Hospital Prague Czech Republic
Department of Cardiology Royal Vinohrady University Hospital Czech Republic
Department of Cardiovascular Surgery General University Hospital Prague Czech Republic
Division of Clinical Pharmacology Department of Pharmacology Showa University Tokyo Japan
Institute for Clinical and Experimental Medicine Prague Czech Republic
References provided by Crossref.org
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- $a BACKGROUND: Use of the current echocardiography-based indications for aortic regurgitation (AR) surgery might result in late valve replacement at the stage of irreversible myocardial damage. Therefore, we aimed to identify simple models combining multiple echocardiography or magnetic resonance imaging (MRI)-derived indices and natriuretic peptides (BNP [brain natriuretic peptide] or NT-proBNP [N-terminnal pro-B type natriuretic peptide]) to predict early disease decompensation in asymptomatic severe AR. METHODS: This prospective and multicenter study included asymptomatic patients with severe AR, preserved left ventricular ejection fraction (>50%), and sinus rhythm. The echocardiography and MRI images were analyzed centrally in the CoreLab. The study end point was the onset of indication for aortic valve surgery as per current guidelines. RESULTS: The derivative cohort consisted of 127 asymptomatic patients (age 45±14 years, 84% males) with 41 (32%) end points during a median follow-up of 1375 (interquartile range, 1041-1783) days. In multivariable Cox regression analysis, age, BNP, 3-dimensional vena contracta area, MRI left ventricular end-diastolic volume index, regurgitant volume, and a fraction were identified as independent predictors of end point (all P<0.05). However, a combined model including one parameter of AR assessment (MRI regurgitant volume or regurgitant fraction or 3-dimensional vena contracta area), 1 parameter of left ventricular remodeling (MRI left ventricular end-diastolic volume index or echocardiography 2-dimensional global longitudinal strain or E wave), and BNP showed significantly higher predictive accuracy (area under the curve, 0.74-0.81) than any parameter alone (area under the curve, 0.61-0.72). These findings were confirmed in the validation cohort (n=100 patients, 38 end points). CONCLUSIONS: In asymptomatic severe AR, multimodality and multiparametric model combining 2 imaging indices with natriuretic peptides, showed high accuracy to identify early disease decompensation. Further prospective studies are warranted to explore the clinical benefit of implementing these models to guide patient management. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02910349.
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