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Updated: Sep 19, 2025

Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
Using vascular biomarkers to assess heart failure event risk in hospitalized patients with and without AKI
Audrey A Shi1, Anna Simone Andrawis1, Aditya Biswas1
1Department of Internal Medicine, Section of Nephrology, Yale School of Medicine, New Haven, CT, USA.
Insights
Nine vascular biomarkers can identify distinct patient phenotypes, improving prediction of heart failure (HF) risk after hospitalization, particularly for those with acute kidney injury (AKI). These markers enhance risk stratification beyond standard clinical factors.
Area of Science:
- Cardiovascular Medicine
- Nephrology
- Biomarker Discovery
Background:
- Patients with acute kidney injury (AKI) face elevated risks of heart failure (HF).
- Identifying reliable predictors of post-hospitalization HF is crucial, especially for AKI survivors.
- Existing clinical variables may not fully capture HF risk in this population.
Purpose of the Study:
- To investigate the predictive value of nine vascular biomarkers for future HF events.
- To determine if these biomarkers can stratify risk in hospitalized patients, including those with AKI.
- To assess if biomarkers improve HF prediction when combined with clinical data.
Main Methods:
- Unsupervised spectral clustering of 9 plasma biomarkers (angiopoietin-1, angpt-2, VEGF-A, VEGF-C, VEGF-d, VEGFR1, sTie-2, PlGF, bFGF) in 1,497 patients (half with AKI) at 3 months post-hospitalization.
- Cox regression analysis to associate biomarker-derived clusters (Vascular Injury, Vascular Repair, Dormant Phenotypes) with HF events.
- Area Under the Curve (AUC) and net reclassification index calculations to evaluate biomarker addition to a clinical prediction model for HF or death at 3 years.
Main Results:
- Three distinct phenotypes were identified: Vascular Injury, Vascular Repair, and Dormant.
- The Vascular Injury Phenotype showed a twofold higher risk of HF events compared to the Repair Phenotype, in both AKI and non-AKI patients.
- The combined biomarker and clinical model demonstrated superior prediction of HF or death (AUC 0.80) compared to the clinical model alone (AUC 0.77), significantly improving reclassification.
Conclusions:
- Vascular biomarkers effectively define patient phenotypes.
- These phenotypes stratify risk for future heart failure events in recently hospitalized individuals.
- Biomarker incorporation enhances the prediction of HF risk post-hospitalization, especially in AKI patients.
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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