Department of Nephrology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan.
Department of Nephrology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan.
ESC Heart Fail. 2021 Dec;8(6):4976-4987. doi: 10.1002/ehf2.13557. Epub 2021 Sep 23.
The prognostic significance of renal function variability has not been fully elucidated in heart failure (HF). This multicentre, prospective cohort study aimed to evaluate the usefulness of visit-to-visit variability in estimated glomerular filtration rate (eGFR) for predicting patients' outcomes in a real-world HF population.
A total of 564 patients who had survived HF hospitalization were randomly assigned with a 2:1 ratio to derivation and validation cohorts, and they were then followed after discharge. Using the data for 6 months after discharge, each patient's visit-to-visit eGFR variability (EGV) was estimated. In the derivation cohort, Cox regression analyses were performed to assess the association of EGV with a subsequent composite event (death and HF hospitalization). In the validation cohort, the predictive performance was compared among Cox regression models with EGV, those with B-type natriuretic peptide (BNP) and those with eGFR.
In the derivation cohort (376 patients), median age, left ventricular ejection fraction (LVEF), BNP and eGFR at discharge were 72 years, 53.3%, 134.8 pg/mL and 58.7 mL/min/1.73 m , respectively. During a median follow-up of 2.2 years, higher EGV was associated with an increased risk of the composite event (adjusted hazard ratio [per standard deviation increase in log-transformed EGV], 1.5; 95% confidence interval, 1.1-2.0). A similar finding was observed in a stratified analysis by LVEF. In the validation cohort (188 patients), better model fit, discrimination, reclassification and calibration were observed for EGV than for 6-month averaged BNP or eGFR for predicting the composite event when added to HF risk prediction models. Adding EGV to models with BNP or eGFR improved model discrimination and reclassification.
EGV predicts HF outcomes regardless of LVEF. Risk prediction models with EGV have good performance in real-world HF patients. The study findings highlight the clinical importance of observing visit-to-visit fluctuations in renal function in this population.
肾功能变异性的预后意义在心力衰竭（HF）中尚未完全阐明。这项多中心前瞻性队列研究旨在评估估算肾小球滤过率（eGFR）的随访间变异性对预测真实世界 HF 人群患者结局的有用性。
共纳入 564 例 HF 住院后存活的患者，按 2:1 的比例随机分配到推导队列和验证队列，并在出院后进行随访。使用出院后 6 个月的数据，估计每位患者的随访间 eGFR 变异性（EGV）。在推导队列中，进行 Cox 回归分析评估 EGV 与随后的复合事件（死亡和 HF 住院）之间的关联。在验证队列中，比较 Cox 回归模型中 EGV、B 型利钠肽（BNP）和 eGFR 的预测性能。
在推导队列（376 例患者）中，中位年龄、左心室射血分数（LVEF）、出院时的 BNP 和 eGFR 分别为 72 岁、53.3%、134.8pg/mL 和 58.7mL/min/1.73m。中位随访 2.2 年期间，较高的 EGV 与复合事件的风险增加相关（调整后的危险比[每增加 1 个标准偏差的对数转换 EGV]，1.5；95%置信区间，1.1-2.0）。在 LVEF 分层分析中也观察到了类似的结果。在验证队列（188 例患者）中，与 6 个月平均 BNP 或 eGFR 相比，在添加到 HF 风险预测模型后，EGV 对预测复合事件的模型拟合度、区分度、重新分类和校准更好。在 BNP 或 eGFR 模型中添加 EGV 可提高模型的区分度和重新分类。
EGV 可预测 HF 结局，而与 LVEF 无关。在真实世界的 HF 患者中，EGV 的风险预测模型具有良好的性能。研究结果强调了在该人群中观察肾功能随访间波动的临床重要性。
