Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Minneapolis Heart Institute, Minneapolis, MN, USA.
Eur J Heart Fail. 2022 Jun;24(6):988-995. doi: 10.1002/ejhf.2543. Epub 2022 May 31.
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.
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).
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.
心力衰竭（HF）是慢性肾脏病（CKD）患者心血管发病率和死亡率不断上升的主要原因之一。需要提高早期预测能力，以便在 CKD 患者中及早开始治疗。我们试图研究采用多变量生物标志物方法预测 CKD 患者发生 HF 的风险。
我们对无 HF 既往史的慢性肾不全队列研究（CRIC）中 3182 名成年人进行了研究，他们在基线检查时接受了 11 种血液生物标志物（B 型利钠肽[BNP]、CXC 趋化因子配体 12、纤维蛋白原、 fractalkine、高敏 C 反应蛋白、髓过氧化物酶、高敏肌钙蛋白 T（hsTnT）、成纤维细胞生长因子 23[FGF23]、中性粒细胞明胶酶相关脂质运载蛋白、胎球蛋白 A、醛固酮）的检测。该人群被随机分为推导（n=1629）和验证（n=1553）队列。对与 HF 相关的生物标志物进行调整后，将与已建立的 HF 危险因素相关的生物标志物合并为一个总体生物标志物评分（高于 Youden 指数截断值的生物标志物数量）。Cox 回归用于探讨生物标志物组合预测 HF 发生的作用。中位随访 7 年后，共有 411 名患者发生了 HF。在推导队列中，有 4 种生物标志物与 HF 相关（BNP、FGF23、纤维蛋白原、hsTnT）。在一个结合所有 4 种生物标志物的模型中，BNP（危险比[HR]2.96[95%置信区间 2.14-4.09]）、FGF23（HR 1.74[1.30-2.32]）、纤维蛋白原（HR 2.40[1.74-3.30]）和 hsTnT（HR 2.89[2.06-4.04]）与 HF 事件相关。HF 的发生率随着生物标志物评分的增加而增加，在推导和验证队列中均达到相似程度：从推导队列中评分 0%的 2.0%到评分 4 的 46.6%，再到验证队列中评分 0%的 2.4%到评分 4 的 43.5%。与单独的临床风险模型相比，纳入生物标志物加临床因素的模型重新分类了 601 名（19%）参与者的风险（352 名[11%]参与者风险更高，249 名[8%]参与者风险更低）（净重新分类改善 0.16）。
一个基本的四项血液生物标志物（BNP、FGF23、纤维蛋白原和 hsTnT）组合可作为单独的评分用于预测 CKD 患者的 HF 事件发生情况，从而可早期识别 HF 高危患者。将生物标志物评分加入临床风险模型可适度重新分类 HF 风险，并略微提高区分度。
