Preview

Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice

Advanced search

Determinants of left ventricular remodelling phenotypes based on cluster analysis and the role of adherence to guideline-directed heart failure pharmacotherapy one year after acute myocardial infarction and revascularization

https://doi.org/10.37489/2588-0519-GCP-0008

EDN: IZNEWI

Abstract

Background. Left ventricular (LV) remodeling after acute myocardial infarction (AMI) is a key process that determines the risk of heart failure (HF) progression and adverse clinical outcomes in patients following revascularization. The heterogeneity of the remodeling trajectories and the limitations of conventional risk stratification approaches necessitate the implementation of advanced methods of phenotyping and assessment of HF treatment adherence.

The objective of this study was to determine the extent to which baseline left ventricular remodeling phenotypes, derived via cluster analysis, drive favorable changes in structural and functional echocardiographic parameters over 12 months after acute myocardial infarction and to assess the role of adherence to pharmacotherapy in this process.

Methods. This retrospective cohort study enrolled 105 patients after acute myocardial infarction who underwent revascularization and were followed for 12 months. Cluster analysis based on five echocardiographic parameters — left ventricular ejection fraction, end-diastolic volume, end-systolic volume, left ventricular myocardial mass index, and left atrial size — was used to define the left ventricular remodeling phenotypes at baseline and at 12 months. Medication adherence was assessed using the proportion of days covered, with adherence defined as PDC ≥80% across all prescribed drug classes.

Results. At both time points (baseline and 12 months), three left ventricular remodeling phenotypes were identified: favorable (normal ejection fraction with minimal chamber dilatation — end-diastolic and end-systolic volumes — limited hypertrophy by left ventricular mass index, and borderline left atrial size), intermediate, and unfavorable (reduced ejection fraction with marked dilatation and hypertrophy and enlarged left atrium). At 12 months, 72% of patients with a baseline favorable phenotype retained it, whereas nearly half of those with intermediate or unfavorable phenotypes transitioned toward more favorable categories. High composite adherence to heart failure pharmacotherapy (PDC comp ≥80%) was significantly more prevalent in favorable phenotypes at 12 months (p <0.001). Composite adherence emerged as a significant effect modifier associated with reverse left ventricular remodeling irrespective of the baseline phenotype.

Conclusions. Cluster analysis delineated clinically meaningful left ventricular remodeling phenotypes and enabled the tracking of their trajectories over the first year postmyocardial infarction. High adherence to guideline-directed HF pharmacotherapy substantially increased the likelihood of favorable remodeling and mitigated an initially unfavorable course. These findings support personalized monitoring and adherence support strategies for patients with heart failure after myocardial infarction.

About the Authors

S. B. Fitilev
Peoples' Friendship University of Russia named after Patrice Lumumba ; City Polyclinic No 2 of Moscow Healthcare Department
Russian Federation

Sergey B. Fitilev — Dr. Sci. (Med.), professor, Department of General and Clinical Pharmacology, Medical Institute

Moscow 


Competing Interests:

All authors declare that they have no conflict of interest regarding this publication.



I. I. Shkrebniova
Peoples' Friendship University of Russia named after Patrice Lumumba ; City Polyclinic No 2 of Moscow Healthcare Department
Russian Federation

Irina I. Shkrebniova — PhD, Cand. Sci. (Med.), associate professor, Department of General and Clinical Pharmacology, Medical Institute

Moscow 


Competing Interests:

All authors declare that they have no conflict of interest regarding this publication.



D. A. Kliuev
Peoples' Friendship University of Russia named after Patrice Lumumba
Russian Federation

Dmitry A. Kliuev — PhD, Cand. Sci. (Pharm.), assistant professor, Department of General and Clinical Pharmacology, Medical Institute

Moscow 


Competing Interests:

All authors declare that they have no conflict of interest regarding this publication.



M. I. Smirnov
Peoples' Friendship University of Russia named after Patrice Lumumba
Russian Federation

Mikhail I. Smirnov — postgraduate student, Department of General and Clinical Pharmacology, Medical Institute

Moscow 


Competing Interests:

All authors declare that they have no conflict of interest regarding this publication.



References

1. McDonagh TA, Metra M, Adamo M, et al; ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021 Sep 21;42(36):3599-3726. doi: 10.1093/eurheartj/ehab368.

2. Heidenreich PA, Bozkurt B, Aguilar D, et al; ACC/AHA Joint Committee Members. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022 May 3;145(18):e895-e1032. doi: 10.1161/CIR.0000000000001063.

3. Byrne RA, Rossello X, Coughlan JJ, et al; ESC Scientific Document Group. 2023 ESC Guidelines for the management of acute coronary syndromes. Eur Heart J. 2023 Oct 12;44(38):3720-3826. doi: 10.1093/eurheartj/ehad191.

4. Mitic V, Stojanovic D, Deljanin-Ilic M, et al. Biomarker Phenotypes in Heart Failure with Preserved Ejection Fraction Using Hierarchical Clustering-A Pilot Study. Med Princ Pract. 2023 Sep 21;32(4-5):297–307. doi: 10.1159/000534155.

5. van de Veerdonk MC, Savarese G, Handoko ML, et al. Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies. Curr Heart Fail Rep. 2023 Oct;20(5):461-470. doi: 10.1007/s11897-023-00626-w.

6. Meijs C, Handoko ML, Savarese G, et al. Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review. Curr Heart Fail Rep. 2023 Oct;20(5):333-349. doi: 10.1007/s11897-023-00615-z.

7. Shah SJ, Katz DH, Selvaraj S, et al. Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation. 2015 Jan 20;131(3):269-79. doi: 10.1161/CIRCULATIONAHA.114.010637.

8. Pieske B, Tschöpe C, de Boer RA, et al. How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: a consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Eur Heart J. 2019 Oct 21;40(40):3297-3317. doi: 10.1093/eurheartj/ehz641.

9. El-Zein RS, Mohammed M, Nguyen DD, et al. Association of Medication Adherence and Health Status in Heart Failure With Reduced Ejection Fraction: Insights From the CHAMP-HF Registry. Circ Cardiovasc Qual Outcomes. 2024 Sep;17(9):e010211. doi: 10.1161/CIRCOUTCOMES.123.010211.

10. Greene SJ, Butler J, Albert NM, et al. Medical Therapy for Heart Failure With Reduced Ejection Fraction: The CHAMP-HF Registry. J Am Coll Cardiol. 2018 Jul 24;72(4):351-366. doi: 10.1016/j.jacc.2018.04.070.

11. Fitilev SB, Kliuev DA, Shkrebniova II, et al. Methodology for calculating the "proportion of days covered" to determine adherence to pharmacotherapy using data from the accounting of implemented electronic prescriptions of the EMIAS. Kachestvennaya klinicheskaya praktika = Good Clinical Practice. 2024;(4):70-81. (In Russ.). doi: 10.37489/2588-0519-2024-4-70-81. EDN: ZPUZCZ.

12. Teichholz LE, Kreulen T, Herman MV, Gorlin R. Problems in echocardiographic volume determinations: echocardiographic-angiographic correlations in the presence of absence of asynergy. Am J Cardiol. 1976 Jan;37(1):7-11. doi: 10.1016/0002-9149(76)90491-4.

13. Du Bois D, Du Bois EF. A formula to estimate the approximate surface area if height and weight be known. 1916. Nutrition. 1989 SepOct;5(5):303-11; discussion 312-3.

14. Brian S. Everitt, Sabine Landau, Morven Leese, Daniel Stahl. Cluster Analysis. 5th Edition. Wiley; 2011. ISBN: 978-0-470-97844-3. doi: 10.1002/9780470977811.

15. Brunton-O'Sullivan MM, Holley AS, Shi B, Harding SA, Larsen PD. Cluster analysis of extracellular matrix biomarkers predicts the development of impaired systolic function within 1 year of acute myocardial infarction. Heart Vessels. 2022 Dec;37(12):2029-2038. doi: 10.1007/s00380-022-02118-8.

16. Ernande L, Audureau E, Jellis CL, et al. Clinical Implications of Echocardiographic Phenotypes of Patients With Diabetes Mellitus. J Am Coll Cardiol. 2017 Oct 3;70(14):1704-1716. doi: 10.1016/j.jacc.2017.07.792.

17. Kyodo A, Kanaoka K, Keshi A, et al. Heart failure with preserved ejection fraction phenogroup classification using machine learning. ESC Heart Fail. 2023 Jun;10(3):2019-2030. doi: 10.1002/ehf2.14368.

18. Rabkin SW. Evaluating the adverse outcome of subtypes of heart failure with preserved ejection fraction defined by machine learning: A systematic review focused on defining high risk phenogroups. EXCLI J. 2022 Feb 22;21:487-518. doi: 10.17179/excli2021-4572.

19. Fitilev SB, Shkrebneva II, Klyuev DA, Smirnov MI. Left ventricle remodeling phenotypes and treatment adherence in patients with heart failure after acute myocardial infarction: cluster analysis of real-life clinical data. Therapy. 2025;11(6):17-25. (In Russ.). doi: 10.18565/therapy.2025.6.17-25.


Review

For citations:


Fitilev S.B., Shkrebniova I.I., Kliuev D.A., Smirnov M.I. Determinants of left ventricular remodelling phenotypes based on cluster analysis and the role of adherence to guideline-directed heart failure pharmacotherapy one year after acute myocardial infarction and revascularization. Kachestvennaya Klinicheskaya Praktika = Good Clinical Practice. 2025;(4):78-89. (In Russ.) https://doi.org/10.37489/2588-0519-GCP-0008. EDN: IZNEWI

Views: 41


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2588-0519 (Print)
ISSN 2618-8473 (Online)