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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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Sison SDM, Lin KJ, Najafzadeh M, Ko D, Patorno E, Bessette LG, Zakoul H, Kim DH. Common non-cardiovascular multimorbidity groupings and clinical outcomes in older adults with major cardiovascular disease. J Am Geriatr Soc 2023; 71:3179-3188. [PMID: 37354026 PMCID: PMC10592495 DOI: 10.1111/jgs.18479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/25/2023]
Abstract
BACKGROUND Among older adults, non-cardiovascular multimorbidity often coexists with cardiovascular disease (CVD) but their clinical significance is uncertain. We identified common non-cardiovascular comorbidity patterns and their association with clinical outcomes in Medicare fee-for-service beneficiaries with acute myocardial infarction (AMI), congestive heart failure (CHF), or atrial fibrillation (AF). METHODS Using 2015-2016 Medicare data, we took 1% random sample to create 3 cohorts of beneficiaries diagnosed with AMI (n = 24,808), CHF (n = 57,285), and AF (n = 36,277) prior to 1/1/2016. Within each cohort, we applied latent class analysis to classify beneficiaries based on 9 non-cardiovascular comorbidities (anemia, cancer, chronic kidney disease, chronic lung disease, dementia, depression, diabetes, hypothyroidism, and musculoskeletal disease). Mortality, cardiovascular and non-cardiovascular hospitalizations, and home time lost over a 1-year follow-up period were compared across non-cardiovascular multimorbidity classes. RESULTS Similar non-cardiovascular multimorbidity classes emerged from the 3 CVD cohorts: (1) minimal, (2) depression-lung, (3) chronic kidney disease (CKD)-diabetes, and (4) multi-system class. Across CVD cohorts, multi-system class had the highest risk of mortality (hazard ratio [HR], 2.7-3.9), cardiovascular hospitalization (HR, 1.6-3.3), non-cardiovascular hospitalization (HR, 3.1-7.2), and home time lost (rate ratio, 2.7-5.4). Among those with AMI, the CKD-diabetes class was more strongly associated with all the adverse outcomes than the depression-lung class. In CHF and AF, differences in risk between the depression-lung and CKD-diabetes classes varied per outcome; and the depression-lung and multi-system classes had double the rates of non-cardiovascular hospitalizations than cardiovascular hospitalizations. CONCLUSION Four non-cardiovascular multimorbidity patterns were found among Medicare beneficiaries with CHF, AMI, or AF. Compared to the minimal class, the multi-system, CKD-diabetes, and depression-lung classes were associated with worse outcomes. Identification of these classes offers insight into specific segments of the population that may benefit from more than the usual cardiovascular care.
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Affiliation(s)
- Stephanie Denise M. Sison
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Department of Internal Medicine, University of Massachusetts Chan Medical School, Worcester, MA
| | - Kueiyu Joshua Lin
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Mehdi Najafzadeh
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Darae Ko
- Section of Cardiovascular Medicine, Boston University School of Medicine, Boston, MA
| | - Elisabetta Patorno
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Lily G. Bessette
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Heidi Zakoul
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Dae Hyun Kim
- Division of Gerontology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
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Kalyuzhin VV, Teplyakov AT, Bespalova ID, Kalyuzhina EV, Terentyeva NN, Grakova EV, Kopeva KV, Usov VY, Garganeeva NP, Pavlenko OA, Gorelova YV, Teteneva AV. Promising directions in the treatment of chronic heart failure: improving old or developing new ones? BULLETIN OF SIBERIAN MEDICINE 2022. [DOI: 10.20538/1682-0363-2022-3-181-197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Unprecedented advances of recent decades in clinical pharmacology, cardiac surgery, arrhythmology, and cardiac pacing have significantly improved the prognosis in patients with chronic heart failure (CHF). However, unfortunately, heart failure continues to be associated with high mortality. The solution to this problem consists in simultaneous comprehensive use in clinical practice of all relevant capabilities of continuously improving methods of heart failure treatment proven to be effective in randomized controlled trials (especially when confirmed by the results of studies in real clinical practice), on the one hand, and in development and implementation of innovative approaches to CHF treatment, on the other hand. This is especially relevant for CHF patients with mildly reduced and preserved left ventricular ejection fraction, as poor evidence base for the possibility of improving the prognosis in such patients cannot justify inaction and leaving them without hope of a clinical improvement in their condition. The lecture consistently covers the general principles of CHF treatment and a set of measures aimed at inotropic stimulation and unloading (neurohormonal, volumetric, hemodynamic, and immune) of the heart and outlines some promising areas of disease-modifying therapy.
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Affiliation(s)
| | - A. T. Teplyakov
- Cardiology Research Institute, Tomsk National Research Medical Center (NRMC), Russian Academy of Sciences
| | | | | | | | - E. V. Grakova
- Cardiology Research Institute, Tomsk National Research Medical Center (NRMC), Russian Academy of Sciences
| | - K. V. Kopeva
- Cardiology Research Institute, Tomsk National Research Medical Center (NRMC), Russian Academy of Sciences
| | - V. Yu. Usov
- Cardiology Research Institute, Tomsk National Research Medical Center (NRMC), Russian Academy of Sciences
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Sun J, Guo H, Wang W, Wang X, Ding J, He K, Guan X. Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review. Front Cardiovasc Med 2022; 9:895836. [PMID: 35935639 PMCID: PMC9353556 DOI: 10.3389/fcvm.2022.895836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 07/04/2022] [Indexed: 11/23/2022] Open
Abstract
Background Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice. Methods The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed. Results Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures. Conclusion This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.
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Affiliation(s)
- Jin Sun
- Medical School of Chinese PLA, Beijing, China
| | - Hua Guo
- Medical School of Chinese PLA, Beijing, China
| | - Wenjun Wang
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China
- Medical Big Data Center, Chinese PLA General Hospital, Beijing, China
| | - Xiao Wang
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China
- Medical Big Data Center, Chinese PLA General Hospital, Beijing, China
| | - Junyu Ding
- Medical School of Chinese PLA, Beijing, China
| | - Kunlun He
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China
- Medical Big Data Center, Chinese PLA General Hospital, Beijing, China
- *Correspondence: Xizhou Guan,
| | - Xizhou Guan
- Department of Pulmonary and Critical Care Medicine, The Eighth Medical Center, Chinese PLA General Hospital, Beijing, China
- Kunlun He,
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