Yin Ting, Shi Shi, Zhu Xu, Cheang Iokfai, Lu Xinyi, Gao Rongrong, Zhang Haifeng, Yao Wenming, Zhou Yanli, Li Xinli
Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, People's Republic of China.
Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, 215002, People's Republic of China.
J Inflamm Res. 2022 Mar 20;15:1953-1967. doi: 10.2147/JIR.S348139. eCollection 2022.
The current study aimed to develop a convenient and accurate prognostic dynamic nomogram model for the risk of all-cause death in acute heart failure (AHF) patients that incorporates clinical characteristics including N-terminal pro-brain natriuretic peptide (NT-pro BNP) and growth stimulation expresses gene 2 protein (ST2).
We prospectively studied 537 consecutive AHF patients and derived a clinical prediction model. The least absolute shrinkage and selection operator regression model combined with clinical characteristics were used for dimensional reduction and feature selection. Multivariate Cox proportional hazard analysis and "Dynnom" package were used to build the dynamic nomogram for prediction of 1-,2-,and 5-year overall survival for AHF. With bootstrap validation, the time-dependent concordance index (C-index) and calibration curves were used to assess predictive discrimination and accuracy. The contributions of NT-pro BNP and ST2 to the nomogram were evaluated using integrated discrimination improvement (IDI) and net reclassification improvement (NRI), while decision curve analysis (DCA) was used to assess clinical value.
Patients were randomly divided into derivation (74.9%, n=402) and validation (25.1%, n=135) cohorts. Optimal independent prognostic factors for 1-,2-, and 5-year all-cause mortality were BS-ACMR (B: NT-pro BNP; S: ST2; A: age; C: complete right bundle branch block; M: mean arterial pressure; and R: red cell distribution width >14.5%); these were incorporated into the dynamic nomogram (https://bs-acmr-nom.shinyapps.io/dynnomapp/) with bootstrap validation. The C-indexes in the derivation (0.793) and validation (0.782) cohorts were consistent with comparable performance parameters. The calibration curve showed good agreement between the nomogram-predicted and actual survival. Adding NT-pro BNP and ST2 provided a significant net benefit and improved performance over other less adequate schemes in terms of DCA of survival probability compared to those neglecting either of these two factors.
The study constructed a dynamic BS-ACMR nomogram, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis in AHF patients.
本研究旨在开发一种方便且准确的预后动态列线图模型，用于预测急性心力衰竭（AHF）患者全因死亡风险，该模型纳入了包括N末端脑钠肽前体（NT-pro BNP）和生长刺激表达基因2蛋白（ST2）在内的临床特征。
我们前瞻性地研究了537例连续的AHF患者，并推导了一个临床预测模型。采用结合临床特征的最小绝对收缩和选择算子回归模型进行降维和特征选择。使用多变量Cox比例风险分析和“Dynnom”软件包构建用于预测AHF患者1年、2年和5年总生存率的动态列线图。通过自举验证，使用时间依赖性一致性指数（C指数）和校准曲线来评估预测辨别力和准确性。使用综合辨别力改善（IDI）和净重新分类改善（NRI）评估NT-pro BNP和ST2对列线图的贡献，同时使用决策曲线分析（DCA）评估临床价值。
患者被随机分为推导队列（74.9%，n = 402）和验证队列（25.1%，n = 135）。1年、2年和5年全因死亡率的最佳独立预后因素为BS-ACMR（B：NT-pro BNP；S：ST2；A：年龄；C：完全性右束支传导阻滞；M：平均动脉压；R：红细胞分布宽度>14.5%）；这些因素被纳入动态列线图（https://bs-acmr-nom.shinyapps.io/dynnomapp/）并进行自举验证。推导队列（0.793）和验证队列（0.782）中的C指数与可比的性能参数一致。校准曲线显示列线图预测的生存率与实际生存率之间具有良好的一致性。与忽略这两个因素中的任何一个的其他不太完善的方案相比，加入NT-pro BNP和ST2在生存概率的DCA方面提供了显著的净效益并改善了性能。
本研究构建了动态BS-ACMR列线图，这是一种方便、实用且有效的临床决策工具，可为AHF患者提供准确的预后。
