Department of Cardiology, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.
Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America.
PLoS One. 2018 Nov 28;13(11):e0206380. doi: 10.1371/journal.pone.0206380. eCollection 2018.
In clinical practice, a risk prediction model is an effective solitary program to predict prognosis in particular patient groups. B-type natriuretic peptide (BNP)and N-terminal pro-b-type natriuretic peptide (NT-proBNP) are widely recognized outcome-predicting factors for patients with heart failure (HF).This study derived external validation of a risk score to predict 1-year mortality after discharge in hospitalized patients with HF using the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC)program data. We also assessed the effect of adding BNP or NT-proBNP to this risk score model in a Korean HF registry population.
We included 5625 patients from the Korean acute heart failure registry (KorAHF) and excluded those who died in hospital. The MAGGIC constructed a risk score to predict mortality in patients with HF by using 13 routinely available patient characteristics (age, gender, diabetes, chronic obstructive pulmonary disorder (COPD), HF diagnosed within the last 18 months, current smoker, NYHA class, use of beta blocker, ACEI or ARB, body mass index, systolic blood pressure, creatinine, and EF). We added BNP or NT-proBNP, which are the most important biomarkers, to the MAGGIC risk scoring system in patients with HF. The outcome measure was 1-year mortality. In multivariable analysis, BNP or NT-proBNP independently predicted death. The risk score was significantly varied between alive and dead groups (30.61 ± 6.32 vs. 24.80 ± 6.81, p < 0.001). After the conjoint use of BNP or NT-proBNP and MAGGIC risk score in patients with HF, a significant difference in risk score was noted (31.23 ± 6.46 vs. 25.25 ± 6.96, p < 0.001).The discrimination abilities of the risk score model with and without biomarker showed minimal improvement (C index of 0.734 for MAGGIC risk score and 0.736 for MAGGIC risk score plus BNP or NT-proBNP, p = 0.0502) and the calibration was found good. However, we achieved a significant improvement in net reclassification and integrated discrimination for mortality (NRI of 33.4%,p < 0.0001 and IDI of 0.002, p < 0.0001).
In the KorAHF, the MAGGIC project HF risk score performed well in a large nationwide contemporary external validation cohort. Furthermore, the addition of BNP or NT-proBNPto the MAGGIC risk score was beneficial in predicting more death in hospitalized patients with HF.
在临床实践中，风险预测模型是预测特定患者群体预后的有效单一方案。B 型利钠肽（BNP）和氨基末端 B 型利钠肽前体（NT-proBNP）被广泛认为是心力衰竭（HF）患者的预后预测因素。本研究使用 Meta 分析全球慢性心力衰竭（MAGGIC）计划数据，对住院 HF 患者出院后 1 年死亡率的风险评分进行了外部验证。我们还评估了在韩国 HF 注册人群中，将 BNP 或 NT-proBNP 添加到该风险评分模型中的效果。
我们纳入了韩国急性心力衰竭注册（KorAHF）中的 5625 例患者，并排除了住院期间死亡的患者。MAGGIC 通过使用 13 个常规可用的患者特征（年龄、性别、糖尿病、慢性阻塞性肺疾病（COPD）、HF 在过去 18 个月内诊断、当前吸烟者、纽约心脏协会（NYHA）心功能分级、使用β受体阻滞剂、ACEI 或 ARB、体重指数、收缩压、肌酐和 EF）构建了一个预测 HF 患者死亡率的风险评分。我们将 BNP 或 NT-proBNP（最重要的生物标志物）添加到 HF 患者的 MAGGIC 风险评分系统中。主要结局是 1 年死亡率。多变量分析显示，BNP 或 NT-proBNP 独立预测死亡。存活组和死亡组之间的风险评分差异显著（30.61±6.32 与 24.80±6.81，p<0.001）。在 HF 患者联合使用 BNP 或 NT-proBNP 和 MAGGIC 风险评分后，风险评分差异有统计学意义（31.23±6.46 与 25.25±6.96，p<0.001）。风险评分模型有无生物标志物的区分能力均有轻微改善（MAGGIC 风险评分的 C 指数为 0.734，MAGGIC 风险评分加 BNP 或 NT-proBNP 的 C 指数为 0.736，p=0.0502），且校准良好。然而，我们在死亡率的净重新分类和综合判别方面取得了显著的改善（33.4%的 NRI，p<0.0001 和 0.002 的 IDI，p<0.0001）。
在 KorAHF 中，MAGGIC 项目 HF 风险评分在大型全国当代外部验证队列中表现良好。此外，在 MAGGIC 风险评分中添加 BNP 或 NT-proBNP 有助于预测住院 HF 患者更多的死亡。
