A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations.
A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations.
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DOI：
10.1007/s12265-021-10151-7
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发表时间：
2022-03
影响因子：
3.4
通讯作者：
Swaminathan S
中科院分区：
文献类型：
--
作者：
Morrill J;Qirko K;Kelly J;Ambrosy A;Toro B;Smith T;Wysham N;Fudim M;Swaminathan S
家庭管理不足和对心力衰竭(HF)恶化的自我意识是已知的主要原因，仅在美国就估计有超过100万人因心力衰竭而住院。大多数目前的家庭心力衰竭管理方案包括纸质指南或探索性健康应用程序，在单个患者的水平上缺乏严密性和有效性。我们报告了一种新的分诊方法，它使用机器学习预测来实时检测和评估病情恶化。使用医学专家对统计和临床综合的模拟病例的意见来训练和验证预测算法。模型性能通过与医生小组共识进行比较，在100个小片段的代表性样本外验证集合中进行评估。算法预测的准确性和安全性指标在确定对恶化的存在/严重程度和适当的治疗反应的共识意见方面超过了所有个人专家。在评估紧急护理的需求时，这些算法也获得了最高的敏感度、特异度和PPV。在这里，我们开发了一种机器学习方法，为被诊断为充血性心力衰竭的成年人提供实时决策支持。与医生的共识意见相比，该算法获得了比任何单个医生更高的恶化和分诊分类性能。网上版载有补充材料，可在10.1007/s12265-021-10151-7查阅。
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. 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. The online version contains supplementary material available at 10.1007/s12265-021-10151-7.
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