Multi‐variable biomarker approach in identifying incident heart failure in chronic kidney disease: results from the Chronic Renal Insufficiency Cohort study. (31st May 2022)

• Record Type: Journal Article
• Authors: Janus, Scott E.; Hajjari, Jamal; Chami, Tarek; Mously, Haytham; Badhwar, Anshul K.; Karnib, Mohamad; Carneiro, Herman; Rahman, Mahboob; Al‐Kindi, Sadeer G.
• Is Part Of: European journal of heart failure. Volume 24:Number 6(2022)
• Page Start: 988
• Page End: 995
• Publication Date: 2022-05-31
• Subjects: High‐sensitivity troponin -- Heart failure -- Chronic kidney disease -- Biomarker; Heart failure -- Periodicals; Heart Failure -- Periodicals; Insuffisance cardiaque -- Périodiques; Heart failure; Periodicals; 616.129005
• DOI: 10.1002/ejhf.2543
• Languages: English
• ISSNs: 1388-9842

Abstract

Abstract : Aims: Heart failure (HF) is one of the leading causes of cardiovascular morbidity and mortality in the ever‐growing population of patients with chronic kidney disease (CKD). There is a need to enhance early prediction to initiate treatment in CKD. We sought to study the feasibility of a multi‐variable biomarker approach to predict incident HF risk in CKD. Methods and results: We examined 3182 adults enrolled in the Chronic Renal Insufficiency Cohort (CRIC) without prevalent HF who underwent serum/plasma assays for 11 blood biomarkers at baseline visit (B‐type natriuretic peptide [BNP], CXC motif chemokine ligand 12, fibrinogen, fractalkine, high‐sensitivity C‐reactive protein, myeloperoxidase, high‐sensitivity troponin T (hsTnT), fibroblast growth factor 23 [FGF23], neutrophil gelatinase‐associated lipocalin, fetuin A, aldosterone). The population was randomly divided into derivation ( n = 1629) and validation ( n = 1553) cohorts. Biomarkers that were associated with HF after adjustment for established HF risk factors were combined into an overall biomarker score (number of biomarkers above the Youden's index cut‐off value). Cox regression was used to explore the predictive role of a biomarker panel to predict incident HF. A total of 411 patients developed incident HF at a median follow‐up of 7 years. In the derivation cohort, four biomarkers were associated with HF (BNP, FGF23, fibrinogen, hsTnT). In a model combining all four biomarkers, BNP (hazard ratio [HR] 2.96 [95% confidence interval 2.14–4.09]), FGF23 (HR 1.74 [1.30–2.32]), fibrinogen (HR 2.40 [1.74–3.30]), and hsTnT (HR 2.89 [2.06–4.04]) were associated with incident HF. The incidence of HF increased with the biomarker score, to a similar degree in both derivation and validation cohorts: from 2.0% in score of 0% to 46.6% in score of 4 in the derivation cohort to 2.4% in score of 0% to 43.5% in score of 4 in the validation cohort. A model incorporating biomarkers in addition to clinical factors reclassified risk in 601 (19%) participants (352 [11%] participants to higher risk and 249 [8%] to lower risk) compared with clinical risk model alone (net reclassification improvement of 0.16). Conclusion: A basic panel of four blood biomarkers (BNP, FGF23, fibrinogen, and hsTnT) can be used as a standalone score to predict incident HF in patients with CKD allowing early identification of patients at high‐risk for HF. Addition of biomarker score to clinical risk model modestly reclassifies HF risk and slightly improves discrimination.