Deep learning on ECGs to improve outcomes in patients on dialysis

心电图深度学习可改善透析患者的预后

基本信息

批准号：
10734856
负责人：
David M Charytan
金额：
$ 73.54万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2028-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10734856
关键词：
Adrenergic beta-Antagonists Affect Blood Pressure Cardiac Cardiac Output Cardiology Cardiovascular system Cause of Death Clinical Clinical Data Clinical Management Data Databases Derivation procedure Detection Development Diagnosis Dialysis procedure Early Diagnosis Early identification Electrocardiogram End stage renal failure Event Functional disorder Goals Health Heart Diseases Heart Rate Heart failure Hemodialysis Hospitalization Hospitals Human Resources Hypotension Impairment Incidence Institution Intervention Lead Left Left Ventricular Ejection Fraction Link Maintenance Midodrine Modeling Monitor Morbidity - disease rate Myocardial Myocardial Infarction Myocardial Stunning Myocardial dysfunction New York City North Carolina Outcome Participant Patients Performance Pharmaceutical Preparations Physicians Population Prevention Prospective Studies Publishing Reflex action Risk Sample Size Techniques Testing Therapeutic Training Ultrafiltration Validation Visit Vulnerable Populations Work advanced analytics adverse outcome clinically actionable convolutional neural network deep learning follow-up heart function high risk improved improved outcome inhibitor insight learning strategy mortality novel outcome prediction patient population predictive modeling prognostication prophylactic prospective recruit recurrent neural network risk prediction risk prediction model secondary outcome side effect structured data transfer learning wearable device

项目摘要

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.

抽象的。在患者中，甘氨酸内部低血压（IDH）和主要的不良心血管事件（MACE）很常见维持血液透析（HD），并为该脆弱患者的发病率和死亡率做出了重大贡献人口。尽管存在减少这些不良结果的策略，但缺乏准确且可行的预测风险模型已导致这些策略的总体低和非目标利用。心电图（ECG）无处不在，便宜，易于执行，并且可以立即访问对心血管反射和健康的无创洞察力。原始波形数据可以通过高级深度学习，以准确确定各种心脏特征以及预测关键结果。在我们先前发表的工作中，我们证明了深度学习的实用性，以确定既正确的权利左心脏功能以及转移学习的实用性以改善HD患者的预测预测。在最近的初步分析，我们还显示了波形数据的实用性，可以在医院IDH和关联中进行预测使用回顾性数据具有30天死亡率。但是，对IDH和狼牙棒对临床部署至关重要。因此，扩展我们先前的工作，我们提出了最大的潜在利用ECG的研究预测HD患者的主要结局。我们将招募1000名不同的患者纽约市透析单元的高清（派生）和北卡罗来纳州的150名患者（验证）和在基线时和基线后4周获得标准持续时间，12铅ECG。另外，一个子集参与者将在探索性的连续3个高清训练期间进行连续的波形监测子研究。然后，我们将使用深度学习和转移学习（使用我们的预培训模型大约有1100万个档案ECG数据库），并在同一会话中使用它来预测IDH 天（目标1）和随访1年（目标2）的MACE的综合结果。该提议的结果对于预测短期和长期心脏结局的临床至关重要。积极的结果将促使研究测试我们的预测模型将其部署到高清单元中以检测和预防的研究 IDH和MACE以及新型可穿戴设备用于IDH和心脏风险预测。