Cluster approach to identifying the 5-year prognosis of patients with chronic heart failure of ischemic etiology

Cover Page


Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

BACKGROUND: The phenotypic and pathophysiological heterogeneity of patients with chronic heart failure increases the interest of researchers in grouping according to similar clinical and genetic characteristics based on cluster analysis.

AIM: To identify phenotypic subgroups in a multivariate cohort of patients with chronic heart failure secondary to coronary artery disease using uncontrolled cluster analysis of clinical, instrumental and genetic components.

MATERIAL AND METHODS: 470 patients with chronic heart failure of functional class I–IV, stable course, ischemic etiology of both sexes at the age of 66.4±10.4 years were examined. A clinical study was conducted, genotyping single nucleotide polymorphisms rs10927875 of the ZBTB17 gene, rs247616 of the CETP gene, rs1143634 of the IL-1β gene, rs1800629 of the TNF gene, rs1800795 of the IL-6 gene was carried out, and patient outcomes were assessed for 5 years. Quantitative data were presented as mean and standard deviation or median and interquartile range; categorical — as frequencies and percentages. Categorical intergroup differences were tested using the χ2 test, and quantitative differences were tested using the Student/Mann–Whitney test. Hierarchical clustering was carried out according to 44 demographic, clinical, genetic variables, time to event was analyzed by the Kaplan–Meier method, risk ratio — by Cox regression. Statistical processing was carried out in the R4.3.1 program.

RESULTS: Two clusters of patients with heart failure were identified. Cluster 1 (66%) included older patients of both sexes, predominantly functional class III–IV chronic heart failure, with enlarged heart chambers, reduced left ventricular ejection fraction, higher heart rate, atrial fibrillation and left ventricular hypertrophy. In this cluster, more carriers of the GG genotype of the rs1800795 polymorphism of the IL-6 gene (p <0.001) and the CT genotype of the rs247616 polymorphism of the CETP gene (p=0.014) were identified. Cluster 2 (34%) was represented predominantly by younger women, with a higher metabolic index, a history of myocardial infarction and coronary intervention, smokers, and a larger proportion of the TT genotype of the rs247616 polymorphism of the CETP gene (p=0.029).

CONCLUSION: 2 clusters of patients with chronic heart failure, characterized by a different set of 44 variables that determine the risk of death from all causes, were identified.

Full Text

Restricted Access

About the authors

Elena V. Khazova

Kazan State Medical University; Kazan (Volga Region) Federal University

Author for correspondence.
Email: hazova_elena@mail.ru
ORCID iD: 0000-0001-8050-2892
SPIN-code: 7013-4320
Scopus Author ID: 57205153574
ResearcherId: O-2336-2016

MD, Cand. Sci. (Med.), Assoc. Prof., Depart of Propaedeutics of Internal Diseases Named after Professor S.S. Zimnitsky, Kazan State Medical University; Research, UNIL “New Professional Competences in Health Preservation”, Institute of Fundamental Medicine and Biology

Russian Federation, Kazan; Kazan

References

  1. Heinzel FR, Shah SJ. The future of heart failure with preserved ejection fraction: Deep phenotyping for targeted therapeutics. Herz. 2022;47(4):308–323. doi: 10.1007/s00059-022-05124-8
  2. Cleland JG, Pellicori P, Dierckx R. Clinical trials in patients with heart failure and preserved left ventricular ejection fraction. Heart Fail Clin. 2014;10(3):511–523. doi: 10.1016/j.hfc.2014.04.011
  3. Potabashniy VA. The phenotypes of chronic heart failure in patients with ischemic heart disease combined with chronic obstructive pulmonary disease. Medicni perspektivi. 2018;23(3):161–171. doi: 10.26641/2307-0404.2018.3(part1).142364
  4. Polunina EA, Voronina LP, Popov EA, Belyakova IS, Polunina OS, Tarasochkina DS. Prognostic algorithms for the progression of chronic heart failure depending on the clinical phenotype. Cardiovascular Therapy and Prevention. 2019;18(3):41–47. (In Russ.) doi: 10.15829/1728-8800-2019-3-41-47
  5. Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, Bonow RO, Huang CC, Deo RC. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015;131(3):269–279. doi: 10.1161/ CIRCULATIONAHA.114.010637
  6. 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. doi: 10.3389/fcvm.2022.895836
  7. Cohen JB, Schrauben SJ, Zhao L, Basso MD, Cvijic ME, Li Z, Yarde M, Wang Z, Bhattacharya PT, Chirinos DA, Prenner S, Zamani P, Seiffert DA, Car BD, Gordon DA, Margulies K, Cappola T, Chirinos JA. Clinical phenogroups in heart failure with preserved ejection fraction: Detailed phenotypes. Prognosis, and Response to Spironolactone. JACC Hear Fail. 2020;8:172–184. doi: 10.1016/j.jchf.2019.09.009
  8. Woolley RJ, Ceelen D, Ouwerkerk W, Tromp J, Figarska SM, Anker SD, Dickstein K, Filippatos G, Zannad F, Marco M, Ng L, Samani N, van Veldhuisen DJ, Lang C, Lam CS, Voors AA. Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction. Eur J Heart Fail. 2021;23:983–991. doi: 10.1002/ejhf.2144
  9. Tsygankova OV, Veretyuk VV. Phenotypic clusters in heart failure with preserved and mid-range ejection fraction: new data and perspectives. Russian Journal of Cardiology. 2021;26(4):4436. (In Russ.) doi: 10.15829/1560-4071-2021-4436
  10. Mareev VYu, Fomin IV, Ageev FT, Begrambekova YuL, Vasyuk YuA, Garganeeva AA, Gendlin GE, Glezer MG, Gautier SV, Dovzhenko TV, Kobalava ZD, Koziolova NA, Koroteev AV, Mareev YuV, Ovchinnikov AG, Perepech NB, Tarlovskaya EI, Chesnikova AI, Shevchenko AO, Arutyunov GP, Belenkov YuN, Galyavich AS, Gilyarevsky SR, Drapkina OM, Duplyakov DV, Lopatin YuM, Sitnikova MYu, Skibitsky VV, Shlyakhto EV. Russian Heart Failure Society, Russian Society of Cardiology. Russian Scientific Medical Society of Internal Medicine Guidelines for Heart failure: Chronic (CHF) and acute decompensated (ADHF). Diagnosis, prevention and treatment. Kardiologiia. 2018;58(6S):8–158. (In Russ.) doi: 10.18087/cardio.2475
  11. Devereux RB, Alonso DR, Lutas EM, Gottlieb GJ, Campo E, Sachs I, Reichek N. Echocardiographic assessment of left ventricular hypertrophy: Comparison to necropsy findings. Am J Cardiol. 1986;57(6):450–458. doi: 10.1016/0002-9149(86)90771-x
  12. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. doi: 10.1093/clinchem/18.6.499
  13. Roytberg GE, Dorosh ZhV, Sharkhun OO, Ushakova TI, Trubino EA. New metabolic index use potentialities in evaluation of insulin resistance in clinical practice. Ratsional'naya farmakoterapiya v kardiologii. 2014;10(3):264–274. (In Russ.) EDN: SHBHZB
  14. Smirnov AV, Shilov EM, Dobronravov VA, Kayukov IG, Bobkova IN, Shvetsov MYu, Tsygin AN, Shutov AM. National recommendations. Chronic kidney disease: basic principles of screening, diagnosis, prevention and treatment approaches. Nefrologiya. 2012;16(1):89–115. (In Russ.) EDN: NJWAGE
  15. Segar MW, Patel KV, Ayers C, Basit M, Tang WHW, Willett D, Berry J, Grodin JL, Pandey A. Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis. Eur J Heart Fail. 2020;22(1):148–158. doi: 10.1002/ejhf.1621
  16. Kyodo A, Kanaoka K, Keshi A, Nogi M, Nogi K, Ishihara S, Kamon D, Hashimoto Y, Nakada Y, Ueda T, Seno A, Nishida T, Onoue K, Soeda T, Kawakami R, Watanabe M, Nagai T, Anzai T, Saito Y. Heart failure with preserved ejection fraction phenogroup classification using machine learning. ESC Heart Fail. 2023;10(3):2019–2030. doi: 10.1002/ehf2.14368
  17. Stienen S, Ferreira JP, Kobayashi M, Preud'homme G, Dobre D, Machu JL, Duarte K, Bresso E, Devignes MD, López N, Girerd N, Aakhus S, Ambrosio G, Brunner-La Rocca HP, Fontes-Carvalho R, Fraser AG, van Heerebeek L, Heymans S, de Keulenaer G, Marino P, McDonald K, Mebazaa A, Papp Z, Raddino R, Tschöpe C, Paulus WJ, Zannad F, Rossignol P. Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: Insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure). Biomarkers. 2020;25(2):201–211. doi: 10.1080/1354750X.2020.1727015

Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. Results of hierarchical clustering of patients with chronic heart failure

Download (105KB)
3. Fig. 2. Overall survival in identified patient clusters

Download (24KB)
4. Fig. 3. Cardiovascular survival in identified patient clusters

Download (21KB)
5. Fig. 4. Event-free survival in identified patient clusters

Download (23KB)

© 2024 Eco-Vector





This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies