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[Nature发表论文]:持续预测AKI的临床可行方法
2019年08月19日 时讯速递, 进展交流 暂无评论

Letter | Published: 31 July 2019

A clinically applicable approach to continuous prediction of future acute kidney injury

Nenad Tomašev, Xavier Glorot, Jack W. Rae, et al

Nature  572, pages116–119 (2019)

Abstract

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. 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 and using acute kidney injury—a common and potentially life-threatening condition—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. 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.

早期预测病情恶化对于医务人员有重要意义,估计约有11%的住院期间死亡与未能及时识别并治疗病情恶化有关。实现上述目标需要在个体水平持续并准确预测患者风险,并有充足的时间以便采取行动。近期我们根据电子病历系统以急性肾损伤(一种常见且可能危及生命的综合征)为例,建立了不良事件模型。在此基础上,我们建立了一种深度学习方法,以持续预测此后患者病情恶化的风险。我们根据一个大规模纵向电子病历系统数据库(覆盖不同临床情况,包括172个住院中心及1,062个门诊的703,782名成年患者)建立模型。我们的模型能够预测55.8%的急性肾损伤住院患者,以及后续需要透析治疗的90.2%的急性肾损伤患者,时间最多提前48小时,每个正确的预警有2个假阳性预警。除预测未来的急性肾损伤外,我们的模型还可以提供可靠的评估,以及对于每个预测至关重要的一系列临床特征,并能够预测今后临床中药的血液化验结果。尽管急性肾损伤的识别与及时治疗面临很大挑战,但我们的方法有助于在充分的时间窗内鉴别高危患者并开始早期治疗。

[评论](仅代表个人意见)

  • 人工智能在医学领域的应用方兴未艾,图形或图像识别恐怕是早期最容易取得成功及赚取眼球的应用实例
  • 重症医学领域的研究多集中在临床综合征(如急性肾损伤)或临床分型(脓毒症内表型,ARDS表型等)的预测,也有少数研究涉足图形识别(如人机不同步)
  • 人工智能用于危重病患者的医疗决策仅见于少数研究(Nat Med 2018; 24: 1716-1720),但或许是更有意义的应用方向

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