Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC.
Department of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC.
J Chin Med Assoc. 2020 Nov;83(11):1008-1013. doi: 10.1097/JCMA.0000000000000403.
Acute heart failure (AHF) is a major and rapidly growing health problem responsible for millions of hospitalizations annually. Due to a high proportion of in-hospital mortality and postdischarge rehospitalization and mortality, a prompt strategy for risk stratification and subsequently tailored therapy is desirable to help improve clinical outcomes. The AHEAD (A: atrial fibrillation; H: hemoglobin; E: elderly; A: abnormal renal parameters; D: diabetes mellitus) and AHEAD-U (A: atrial fibrillation; H: hemoglobin; E: elderly; A: abnormal renal parameters; D: diabetes mellitus, U: uric acid) are popular prognostic scoring systems. However, only a specific follow-up period is considered in these systems, and whether their predictive capability is still accurate in a significantly shorter or longer follow-up period is not known.
In this research, we adapted extensive statistical approaches based on the Cox model to explore consistent risk factors in various follow-up durations. Results showed that six factors, namely, hemoglobin level, age, sodium level, blood urea nitrogen level, atrial fibrillation, and high-density lipoprotein level could be used to establish a new prognostic model, which was referred to as HANBAH. For a simple clinical application, the HANBAH scoring system, with scores from 0 to 6, was developed using several statistical models.
Based on an evaluation using the conventional statistical approaches, such as the Akaike information criterion, concordance statistic, and Cox area under the curve, the HANBAH scoring system consistently outperformed other strategies in predicting short- and long-term mortality. Notably, an independent replication study also revealed similar results. In addition, a modern machine learning technique using the support vector machine confirmed its superior performance.
The use of the HANBAH scoring system, which is a clinically friendly tool, was proposed, and its efficacy in predicting the mortality rates of patients with AHF regardless of the follow-up duration was independently validated.
急性心力衰竭（AHF）是一个主要且快速增长的健康问题，每年导致数百万人住院。由于住院死亡率和出院后再住院率和死亡率高，需要一种快速的风险分层策略和随后的个体化治疗方法，以帮助改善临床结局。AHEAD（A：心房颤动；H：血红蛋白；E：老年；A：异常肾功能参数；D：糖尿病）和 AHEAD-U（A：心房颤动；H：血红蛋白；E：老年；A：异常肾功能参数；D：糖尿病，U：尿酸）是流行的预后评分系统。然而，这些系统仅考虑特定的随访期，并且其预测能力在明显较短或较长的随访期内是否仍然准确尚不清楚。
在这项研究中，我们基于 Cox 模型采用了广泛的统计方法来探索不同随访期内的一致危险因素。结果表明，血红蛋白水平、年龄、钠水平、血尿素氮水平、心房颤动和高密度脂蛋白水平这 6 个因素可用于建立新的预后模型，称为 HANBAH。为了便于临床应用，采用几种统计模型开发了 HANBAH 评分系统，评分范围为 0 至 6。
基于常规统计方法的评估，如赤池信息量准则、一致性统计和 Cox 曲线下面积，HANBAH 评分系统在预测短期和长期死亡率方面始终优于其他策略。值得注意的是，一项独立的复制研究也显示出类似的结果。此外，使用支持向量机的现代机器学习技术也证实了其优越的性能。
提出了使用 HANBAH 评分系统，这是一种临床友好的工具，并独立验证了其在预测 AHF 患者死亡率方面的疗效，无论随访时间如何。
