Deep learning on ECGs to improve outcomes in patients on dialysis

抽象的。 透析中低血压(IDH)和主要心血管不良事件(MACE)在以下患者中很常见 维持性血液透析(HD)，并对该脆弱患者的发病率和死亡率有显著影响 人口。尽管存在减少这些不利后果的战略，但缺乏准确和可操作的 预测性风险模型导致对这些战略的总体使用率较低且没有针对性。 心电图学(ECG)无处不在，价格低廉，操作简单，它提供了一种立即可访问的， 对心血管反射和健康的非侵入性洞察。原始波形数据可通过以下方式利用 高级深度学习，用于准确确定各种心脏特征以及预测 关键成果。在我们之前发表的工作中，我们演示了深度学习的效用，以确定两者的正确 以及左心功能和转移学习在改善HD患者预后方面的作用。在……里面 最近的初步分析，我们还显示了波形数据在医院IDH预测和关联中的应用 使用回溯性数据计算30天的死亡率。然而，IDH和IDH的未来发展和验证 MACE对临床部署至关重要。因此，扩展我们先前的工作，我们提出了最大的预期 利用心电图预测HD患者关键预后的研究。我们将招募1000名不同的患者 来自纽约市透析单元的HD(派生)和来自北卡罗来纳州的150名患者(验证)和 在基线和基线后4周获得标准持续时间、12导联心电图。此外，一个子集 参与者将在连续3次高清治疗期间进行连续波形监测 分组研究。然后，我们将使用深度学习和迁移学习(使用我们的 大约1100万个档案心电数据库)，并使用该数据库在同一时段和30分钟内预测IDH 天数(目标1)和1年随访时MACE的综合结果(目标2)。这项提议的结果是 对于预测短期和长期心脏结果具有很高的临床重要性。积极的结果 将促使研究测试我们的预测模型部署到HD单位，以检测和预防 IDH和MACE以及用于IDH和心脏风险预测的新型可穿戴设备的使用。

ABSTRACT. Intradialytic hypotension (IDH) and major adverse cardiovascular events (MACE) are common in patients on maintenance hemodialysis (HD) and contribute significantly to morbidity and mortality in this vulnerable patient population. Although strategies to decrease these adverse outcomes exist, the lack of accurate and actionable predictive risk models has led to overall low and non-targeted utilization of these strategies. Electrocardiography (ECG) is ubiquitous, cheap, simple to perform, and it provides an immediately accessible, non-invasive insight into cardiovascular reflexes and health. The raw waveform data can be leveraged by advanced deep learning for accurate determination of various cardiac features as well as prognostication of key outcomes. In our prior published work, we demonstrated the utility of deep learning to determine both right and left heart function and the utility of transfer learning to improve outcome prediction in patients on HD. In recent preliminary analysis, we also show utility of waveform data to predict in hospital IDH and association with 30-day mortality using retrospective data. However, prospective development and validation on IDH and MACE are critical to clinical deployment. Thus, extending our prior work, we propose the largest prospective study on utilizing ECGs for prediction of key outcomes in patients on HD. We will recruit 1000 diverse patients on HD from dialysis units in New York City (derivation) and 150 patients from North Carolina (validation) and obtain standard duration, 12-lead ECGs at baseline and 4 weeks after baseline. In addition, a subset of participants will undergo continuous waveform monitoring during 3 consecutive HD sessions in an exploratory sub-study. We will then use deep learning and transfer learning (using pre-trained models from our approximately 11 million archival ECG database) and use this to predict IDH at the same session and within 30 days (Aim 1) and a composite outcome of MACE at 1 year of follow up (Aim 2). The results of this proposal are of high clinical importance for the prediction of both short- and long-term cardiac outcomes. Positive results will prompt studies testing deployment of our predictive models into HD units for detection and prevention of IDH and MACE as well use of novel wearables for IDH and cardiac risk prediction.