AIMS Heart failure (HF) patients are at high-risk of cardiovascular (CV) events, including CV death. Nonetheless, a substantial proportion of these patients die from non-CV causes. Identifying patients at higher risk for each individual event may help selecting patients for clinical trials and tailoring cardiovascular therapies. The aims of the present study are to: (i) characterize patients according to CV vs. non-CV death; (ii) develop models for the prediction of the respective events; (iii) assess the models' performance to differentiate CV from non-CV death. METHODS AND RESULTS This study included 2309 patients with HF from the BIOSTAT-CHF (a systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Competing-risk models were used to assess the best combination of variables associated with each cause-specific death. Results were validated in an independent cohort of 1738 HF patients. The best model to predict CV death included low blood pressure, estimated glomerular filtration rate ≤ 60 mL/min, peripheral oedema, previous HF hospitalization, ischaemic HF, chronic obstructive pulmonary disease, elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP), and troponin (c-index = 0.73). The non-CV death model incorporated age > 75 years, anaemia and elevated NT-proBNP (c-index = 0.71). Both CV and non-CV death rose by quintiles of the risk scores; yet these models allowed the identification of patients in whom absolute CV death rates clearly outweigh non-CV death ones. These findings were externally replicated, but performed worse in a less severely diseased population. CONCLUSIONS Risk models for predicting CV and non-CV death allowed the identification of patients at higher absolute risk of dying from CV causes (vs. non-CV ones). Troponin helped in predicting CV death only, whereas NT-proBNP helped in the prediction of both CV and non-CV death. These findings can be useful both for tailoring therapies and for patient selection in HF trials in order to attain CV event enrichment.

中文翻译：

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心血管和非心血管死亡的区别：肌钙蛋白的作用超出了N端前B型利尿钠肽。来自BIOSTAT-CHF研究的结果。

AIMS心力衰竭（HF）患者处于心血管（CV）事件的高风险中，包括CV死亡。但是，这些患者中有很大一部分是死于非CV原因。识别每个事件中较高风险的患者可能有助于选择患者进行临床试验和调整心血管疗法。本研究的目的是：（i）根据CV与非CV死亡特征描述患者特征；（ii）开发用于预测各个事件的模型；（iii）评估模型的性能以区分CV和非CV死亡。方法和结果该研究包括来自BIOSTAT-CHF的2309例HF患者（一项针对慢性心力衰竭的泰洛雷德疗法的系统生物学研究）。使用竞争风险模型评估与每个特定原因死亡相关的变量的最佳组合。在1738名HF患者的独立队列中验证了结果。预测CV死亡的最佳模型包括低血压，估计的肾小球滤过率≤60 mL / min，周围性水肿，先前的HF住院治疗，缺血性HF，慢性阻塞性肺疾病，N端前B型利尿钠肽（NT）升高-proBNP）和肌钙蛋白（c-index = 0.73）。非CV死亡模型纳入年龄> 75岁，贫血和NT-proBNP升高（c指数= 0.71）。心血管疾病和非心血管疾病的死亡率均上升了风险分数的五分之一。然而，这些模型可以识别绝对CV死亡率明显超过非CV死亡率的患者。这些发现从外部复制，但在病情较轻的人群中表现较差。结论预测CV和非CV死亡的风险模型可以确定死于CV病因的绝对风险较高的患者（相对于非CV病因）。肌钙蛋白仅有助于预测CV死亡，而NT-proBNP有助于预测CV和非CV死亡。这些发现对于定制疗法和在HF试验中选择患者以实现CV事件富集都可能是有用的。