Department of Medicine, Hangzhou Normal University, Hangzhou, 310003, China.
British Heart Foundation Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow, G12 8TA, UK.
Clin Res Cardiol. 2021 Aug;110(8):1234-1248. doi: 10.1007/s00392-020-01786-8. Epub 2020 Dec 10.
BACKGROUND: Sudden death (SD) and pump failure death (PFD) are leading modes of death in heart failure and preserved ejection fraction (HFpEF). Risk stratification for mode-specific death may aid in patient enrichment for new device trials in HFpEF. METHODS: Models were derived in 4116 patients in the Irbesartan in Heart Failure with Preserved Ejection Fraction trial (I-Preserve), using competing risks regression analysis. A series of models were built in a stepwise manner, and were validated in the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved and Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trials. RESULTS: The clinical model for SD included older age, men, lower LVEF, higher heart rate, history of diabetes or myocardial infarction, and HF hospitalization within previous 6 months, all of which were associated with a higher SD risk. The clinical model predicting PFD included older age, men, lower LVEF or diastolic blood pressure, higher heart rate, and history of diabetes or atrial fibrillation, all for a higher PFD risk, and dyslipidaemia for a lower risk of PFD. In each model, the observed and predicted incidences were similar in each risk subgroup, suggesting good calibration. Model discrimination was good for SD and excellent for PFD with Harrell's C of 0.71 (95% CI 0.68-0.75) and 0.78 (95% CI 0.75-0.82), respectively. Both models were robust in external validation. Adding ECG and biochemical parameters, model performance improved little in the derivation cohort but decreased in validation. Including NT-proBNP substantially increased discrimination of the SD model, and simplified the PFD model with marginal increase in discrimination. CONCLUSIONS: The clinical models can predict risks for SD and PFD separately with good discrimination and calibration in HFpEF and are robust in external validation. Adding NT-proBNP further improved model performance. These models may help to identify high-risk individuals for device intervention in future trials. CLINICAL TRIAL REGISTRATION: I-Preserve: ClinicalTrials.gov NCT00095238; TOPCAT: ClinicalTrials.gov NCT00094302; CHARM-Preserved: ClinicalTrials.gov NCT00634712.
背景：心力衰竭和射血分数保留（HFpEF）患者的主要死亡模式是猝死（SD）和泵衰竭死亡（PFD）。针对特定死亡模式的风险分层可能有助于 HFpEF 新型装置试验中患者的富集。
方法：在 Irbesartan in Heart Failure with Preserved Ejection Fraction 试验（I-Preserve）中，使用竞争风险回归分析，对 4116 例患者进行了模型推导。逐步建立了一系列模型，并在 Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity（CHARM）-Preserved 和 Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist（TOPCAT）试验中进行了验证。
结果：SD 的临床模型包括年龄较大、男性、较低的 LVEF、较高的心率、既往糖尿病或心肌梗死病史，以及 6 个月内的 HF 住院史，所有这些都与较高的 SD 风险相关。预测 PFD 的临床模型包括年龄较大、男性、较低的 LVEF 或舒张压、较高的心率、既往糖尿病或心房颤动病史，所有这些都与 PFD 风险较高相关，而血脂异常与 PFD 风险较低相关。在每个模型中，观察到的和预测的发生率在每个风险亚组中相似，表明校准良好。模型的区分度对于 SD 较好，对于 PFD 极好，Harrell 的 C 值分别为 0.71（95%CI 0.68-0.75）和 0.78（95%CI 0.75-0.82）。在外部验证中，两个模型都具有稳健性。加入心电图和生化参数后，在推导队列中，模型性能改善不大，但在验证中下降。加入 NT-proBNP 可显著提高 SD 模型的区分度，并简化 PFD 模型，而区分度略有增加。
结论：在 HFpEF 中，这些临床模型可以分别对 SD 和 PFD 进行风险预测，具有良好的区分度和校准性，并且在外部验证中具有稳健性。加入 NT-proBNP 可进一步提高模型性能。这些模型可能有助于在未来的试验中识别出需要装置干预的高危个体。
临床试验注册：I-Preserve：ClinicalTrials.gov NCT00095238；TOPCAT：ClinicalTrials.gov NCT00094302；CHARM-Preserved：ClinicalTrials.gov NCT00634712。
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