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de Bakker M, Scholte NTB, Oemrawsingh RM, Umans VA, Kietselaer B, Schotborgh C, Ronner E, Lenderink T, Aksoy I, van der Harst P, Asselbergs FW, Maas A, Oude Ophuis AJ, Krenning B, de Winter RJ, The SHK, Wardeh AJ, Hermans W, Cramer GE, van Schaik RH, de Rijke YB, Akkerhuis KM, Kardys I, Boersma E. Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk. J Am Heart Assoc 2024; 13:e031646. [PMID: 38214281 PMCID: PMC10926784 DOI: 10.1161/jaha.123.031646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/22/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk. METHODS AND RESULTS BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high-frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker-based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all-cause death were evaluated using accelerated failure time models (median follow-up, 9.1 years; 141 deaths). Three biomarker-based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long-term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44-0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39-1.32; P=0.281) compared with patients with a repeat ACS. CONCLUSIONS Patients with subphenotypes of post-ACS with different all-cause mortality risks during long-term follow-up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.
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Affiliation(s)
- Marie de Bakker
- Department of CardiologyErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
| | - Niels T. B. Scholte
- Department of CardiologyErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
| | | | - Victor A. Umans
- Department of CardiologyNoordwest ZiekenhuisgroepAlkmaarThe Netherlands
| | | | - Carl Schotborgh
- Department of CardiologyHagaZiekenhuisDen HaagThe Netherlands
| | - Eelko Ronner
- Department of CardiologyReinier de Graaf HospitalDelftThe Netherlands
| | - Timo Lenderink
- Department of CardiologyZuyderland HospitalHeerlenThe Netherlands
| | - Ismail Aksoy
- Department of CardiologyAdmiraal de Ruyter HospitalGoesThe Netherlands
| | - Pim van der Harst
- Department of CardiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Folkert W. Asselbergs
- Amsterdam University Medical Centers, Department of CardiologyUniversity of AmsterdamAmsterdamThe Netherlands
- Health Data Research UK and Institute of Health InformaticsUniversity College LondonLondonUnited Kingdom
| | - Arthur Maas
- Department of CardiologyGelre HospitalZutphenThe Netherlands
| | | | - Boudewijn Krenning
- Department of CardiologyErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
- Department of CardiologyFranciscus Gasthuis & VlietlandRotterdamThe Netherlands
| | - Robbert J. de Winter
- Amsterdam University Medical Centers, Department of CardiologyUniversity of AmsterdamAmsterdamThe Netherlands
| | - S. Hong Kie The
- Department of CardiologyTreant ZorggroepEmmenThe Netherlands
| | | | - Walter Hermans
- Department of CardiologyElizabeth‐Tweesteden HospitalTilburgThe Netherlands
| | - G. Etienne Cramer
- Department of CardiologyRadboud University Medical Center NijmegenNijmegenThe Netherlands
| | - Ron H. van Schaik
- Department of Clinical ChemistryErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
| | - Yolanda B. de Rijke
- Department of Clinical ChemistryErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
| | - K. Martijn Akkerhuis
- Department of CardiologyErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
| | - Isabella Kardys
- Department of CardiologyErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
| | - Eric Boersma
- Department of CardiologyErasmus MC, University Medical Center RotterdamRotterdamThe Netherlands
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Meijs C, Handoko ML, Savarese G, Vernooij RWM, Vaartjes I, Banerjee A, Koudstaal S, Brugts JJ, Asselbergs FW, Uijl A. Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review. Curr Heart Fail Rep 2023; 20:333-349. [PMID: 37477803 PMCID: PMC10589200 DOI: 10.1007/s11897-023-00615-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/27/2023] [Indexed: 07/22/2023]
Abstract
REVIEW PURPOSE This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. FINDINGS 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.
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Affiliation(s)
- Claartje Meijs
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Helmholtz Zentrum München GmbH - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - M Louis Handoko
- Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Gianluigi Savarese
- Division of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Robin W M Vernooij
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Department of Nephrology and Hypertension, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ilonca Vaartjes
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Amitava Banerjee
- Health Data Research UK London, Institute for Health Informatics, University College London, London, UK
| | - Stefan Koudstaal
- Department of Cardiology, Green Heart Hospital, Gouda, the Netherlands
| | - Jasper J Brugts
- Department of Cardiology, Thoraxcenter, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Folkert W Asselbergs
- Health Data Research UK London, Institute for Health Informatics, University College London, London, UK
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Alicia Uijl
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands.
- Division of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands.
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