A clinically applicable approach to continuous prediction of future acute kidney injury
A clinically applicable approach to continuous prediction of future acute kidney injury
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DOI：
10.1038/s41586-019-1390-1
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发表时间：
2019-08-01
期刊：
影响因子：
64.8
通讯作者：
Mohamed, Shakir
中科院分区：
文献类型：
--
作者：
Tomasev, Nenad;Glorot, Xavier;Mohamed, Shakir
恶化的早期预测可能在支持医疗保健专业人员方面发挥重要作用，因为估计有11%的住院死亡是由于未能及时识别和治疗恶化的患者（1）。为了实现这一目标，需要不断更新和准确的患者风险预测，并在个人层面提供足够的背景和足够的时间采取行动。在这里，我们开发了一种深度学习方法，用于持续预测患者未来恶化的风险，建立在最近的工作基础上，该工作从电子健康记录（2-17）中建模不良事件，并使用急性肾损伤-一种常见的和可能危及生命的疾病（18）-作为范例。我们的模型是在涵盖不同临床环境的大型纵向电子健康记录数据集上开发的，该数据集包括172个住院和1，062个门诊站点的703，782名成年患者。我们的模型预测了55.8%的急性肾损伤住院患者，以及90.2%的需要后续透析的急性肾损伤患者，提前时间长达48小时，每个真实警报的比例为2个假警报。除了预测未来的急性肾损伤外，我们的模型还提供了置信度评估和对每个预测最突出的临床特征列表，以及临床相关血液检查的预测未来轨迹（9）。虽然急性肾损伤的识别和及时治疗是具有挑战性的，但我们的方法可以提供在早期治疗的时间窗口内识别风险患者的机会。
The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients(1). To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records(2-17) and using acute kidney injury-a common and potentially life-threatening condition(18)-as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests(9). Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment.