Abstract

Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care.

Lay summary

Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion.

Graphical abstract

中文翻译：

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用于识别和分类心力衰竭恶化的机器学习方法

摘要

众所周知，家庭管理不足和对心力衰竭 (HF) 恶化的自我意识是仅在美国就有超过 100 万例与 HF 相关的住院治疗的主要原因。大多数当前的家庭 HF 管理协议包括纸质指南或探索性健康应用程序，在个体患者层面缺乏严格性和验证。我们报告了一种新的分类方法，该方法使用机器学习预测来实时检测和评估恶化。医学专家对统计和临床综合模拟患者病例的意见被用来训练和验证预测算法。通过与 100 个小插曲的代表性样本外验证集中的医师小组共识进行比较来评估模型性能。算法预测准确性和安全性指标在确定关于恶化的存在/严重性和适当治疗反应的共识意见方面超过了所有个别专家。在评估紧急护理需求时，这些算法还获得了最高的灵敏度、特异性和 PPV。

总结

在这里，我们开发了一种机器学习方法，为被诊断患有充血性心力衰竭的成年人提供实时决策支持。与医师共识意见相比，该算法实现了比任何个体医师更高的恶化和分诊分类性能。

图形概要