ESC Heart Failure (Feb 2025)

The chronic heart failure evolutions: Different fates and routes

  • Piergiuseppe Agostoni,
  • Mattia Chiesa,
  • Elisabetta Salvioni,
  • Michele Emdin,
  • Massimo Piepoli,
  • Gianfranco Sinagra,
  • Michele Senni,
  • Alice Bonomi,
  • Stamatis Adamopoulos,
  • Dimitris Miliopoulos,
  • Massimo Mapelli,
  • Jeness Campodonico,
  • Umberto Attanasio,
  • Anna Apostolo,
  • Emanuele Pestrin,
  • Agostino Rossoni,
  • Damiano Magrì,
  • Stefania Paolillo,
  • Ugo Corrà,
  • Rosa Raimondo,
  • Antonio Cittadini,
  • Annamaria Iorio,
  • Andrea Salzano,
  • Rocco Lagioia,
  • Carlo Vignati,
  • Roberto Badagliacca,
  • Pasquale Perrone Filardi,
  • Michele Correale,
  • Enrico Perna,
  • Marco Metra,
  • Gaia Cattadori,
  • Marco Guazzi,
  • Giuseppe Limongelli,
  • Gianfranco Parati,
  • Fabiana De Martino,
  • Maria Vittoria Matassini,
  • Francesco Bandera,
  • Maurizio Bussotti,
  • Federica Re,
  • Carlo M. Lombardi,
  • Angela B. Scardovi,
  • Susanna Sciomer,
  • Andrea Passantino,
  • Caterina Santolamazza,
  • Davide Girola,
  • Claudio Passino,
  • Marlus Karsten,
  • Savina Nodari,
  • Giulio Pompilio,
  • MECKI score research group

DOI
https://doi.org/10.1002/ehf2.14966
Journal volume & issue
Vol. 12, no. 1
pp. 418 – 433

Abstract

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Abstract Aims Individual prognostic assessment and disease evolution pathways are undefined in chronic heart failure (HF). The application of unsupervised learning methodologies could help to identify patient phenotypes and the progression in each phenotype as well as to assess adverse event risk. Methods and results From a bulk of 7948 HF patients included in the MECKI registry, we selected patients with a minimum 2‐year follow‐up. We implemented a topological data analysis (TDA), based on 43 variables derived from clinical, biochemical, cardiac ultrasound, and exercise evaluations, to identify several patients' clusters. Thereafter, we used the trajectory analysis to describe the evolution of HF states, which is able to identify bifurcation points, characterized by different follow‐up paths, as well as specific end‐stages conditions of the disease. Finally, we conducted a 5‐year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant). Findings were validated on internal (n = 527) and external (n = 777) populations. We analyzed 4876 patients (age = 63 [53–71], male gender n = 3973 (81.5%), NYHA class I–II n = 3576 (73.3%), III–IV n = 1300 (26.7%), LVEF = 33 [25.5–39.9], atrial fibrillation n = 791 (16.2%), peak VO2% pred = 54.8 [43.8–67.2]), with a minimum 2‐year follow‐up. Nineteen patient clusters were identified by TDA. Trajectory analysis revealed a path characterized by 3 bifurcation and 4 end‐stage points. Clusters survival rate varied from 44% to 100% at 2 years and from 20% to 100% at 5 years, respectively. The event frequency at 5‐year follow‐up for each study cohort cluster was successfully compared with those in the validation cohorts (R = 0.94 and R = 0.84, P < 0.001, for internal and external cohort, respectively). Finally, we conducted a 5‐year survival analysis (composite of cardiovascular death, left ventricular assist device, or urgent heart transplant observed in 22% of cases). Conclusions Each HF phenotype has a specific disease progression and prognosis. These findings allow to individualize HF patient evolutions and to tailor assessment.

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