Department of Mathematics, University of Oxford, Oxford, UK.
Booking.com, Amsterdam, Netherlands.
J Cardiovasc Transl Res. 2022 Feb;15(1):103-115. doi: 10.1007/s12265-021-10151-7. Epub 2021 Aug 28.
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.
在家管理不善和对心力衰竭(HF)恶化的自我意识不足,据了解,这是导致仅在美国就有超过 100 万例估计与 HF 相关的住院治疗的主要原因。目前大多数家庭 HF 管理方案包括纸质指南或探索性健康应用程序,这些指南或应用程序在个体患者层面缺乏严谨性和验证。我们报告了一种新颖的分诊方法,该方法使用机器学习预测实时检测和评估恶化情况。使用医学专家对统计上和临床上全面的模拟患者病例的意见来训练和验证预测算法。通过将算法与 100 个病例的代表性样本外验证集中的医生小组共识进行比较,评估模型性能。算法的预测准确性和安全性指标在识别共识意见、存在/严重程度恶化以及适当的治疗反应方面均超过所有专家。在评估是否需要紧急护理时,这些算法的灵敏度、特异性和阳性预测值也最高。在这里,我们开发了一种机器学习方法,为诊断为充血性心力衰竭的成年人提供实时决策支持。与医生共识相比,该算法在识别恶化和分诊分类方面的表现优于任何一位医生。