BMC Nephrology (Jun 2025)

Using vascular biomarkers to assess heart failure event risk in hospitalized patients with and without AKI

  • Audrey A. Shi,
  • Anna Simone Andrawis,
  • Aditya Biswas,
  • Francis P. Wilson,
  • Wassim Obeid,
  • Heather Thiessen Philbrook,
  • Alan S. Go,
  • T. Alp Ikizler,
  • Edward D. Siew,
  • Vernon M. Chinchilli,
  • Chi-Yuan Hsu,
  • Amit X. Garg,
  • W. Brian Reeves,
  • David K. Prince,
  • Pavan Bhatraju,
  • Steve G. Coca,
  • Kathleen D. Liu,
  • Paul L. Kimmel,
  • James S. Kaufman,
  • Mark W. Wurfel,
  • Jonathan Himmelfarb,
  • Chirag R. Parikh,
  • Sherry G. Mansour,
  • for the ASSESS-AKI Consortium*

DOI
https://doi.org/10.1186/s12882-025-04169-1
Journal volume & issue
Vol. 26, no. 1
pp. 1 – 13

Abstract

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Abstract Background Patients with AKI experience higher rates of heart failure (HF). This study seeks to identify criteria to assess the risk of heart failure post-hospitalization, with a special focus on AKI patients. We hypothesized that the combined use of 9 vascular biomarkers would predict future heart failure events after AKI. Using a study of 1497 hospitalized patients with and without AKI, we found that these 9 vascular biomarkers successfully stratified patients into different risk groups for HF, and were able to improve prediction of HF when added to routine clinical variables. Methods Using the ASSESS-AKI cohort, we performed an unsupervised spectral cluster analysis with 9 plasma biomarkers measured at 3 months post-hospitalization [Angiopoietin (angpt)-1, angpt-2, vascular endothelial growth factor (VEGF)-A, VEGF-C, VEGF-d, VEGF receptor 1 (R1), solubleTie-2 (sTie-2), placental growth factor (PlGF), and basic fibroblast growth factor (bFGF)] in 1,497 patients, half of whom had AKI. We used a Cox regression analysis to evaluate the associations between the clusters and HF. Models were adjusted for demographics, cardiovascular disease risk factors, medications, ICU status, lung disease, sepsis, clinical center, and 3-month post-discharge serum creatinine and proteinuria. We calculated change in the area under the curve (AUC) for the prediction of HF or death at 3 years by adding the biomarkers to a clinical model selected by a penalized regression with LASSO. We also calculated a net reclassification index for the addition of the biomarkers to the clinical model. Results Three biomarker-derived clusters were identified: Cluster 1 [n = 302, Vascular Injury (Injury) Phenotype] had higher levels of injury markers, whereas Cluster 2 [n = 728, Vascular Repair (Repair) Phenotype] had higher levels of repair markers. Cluster 3 (n = 467) had lower levels of all markers (Dormant Phenotype). Across the entire cohort, those with the Injury Phenotype had twofold higher risk of a HF event compared to the Repair Phenotype [aHR 2.24 (95% CI: 1.57–3.19)] and noted in both participants with AKI [aHR 2.12 (95% CI: 1.35–3.34)] and without AKI [aHR 2.94 (95%CI: 1.57–5.50)]. The Dormant Phenotype was associated with higher risk of HF events only in participants without AKI. The AUC for the prediction of HF event or death at 3 years by the biomarkers was 0.76 (95% CI: 0.73–0.80), 0.77 (95% CI: 0.73–0.80) for the clinical model, and 0.80 (95% CI: 0.77–0.83) for the combined model. The addition of the biomarkers significantly improved reclassification of HF event or death. Conclusions Vascular biomarkers can be used to derive phenotypes capable of stratifying future risk of HF events in recently hospitalized patients with or without AKI.

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