Utilizing high resolution physiological data and artificial intelligence to develop a pediatric cardiac arrest prediction tool for integration into bedside clinical practice

利用高分辨率生理数据和人工智能开发儿科心脏骤停预测工具，以融入床边临床实践

• 批准号: 538815-2019
• 负责人: Goldenberg, Anna
• 金额: $ 17.69万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=698149
• 关键词: Utilizing; resolution; physiological; data; artificial

Critically ill children are at risk of cardio-pulmonary arrest (CPA). These devastating events occur 300-700 times per year in Canadian pediatric intensive care units (PICUs) and are associated with high mortality (45 to 85%), disability and higher costs of care. Effective management of CPA requires immediate reaction from clinicians. However, changes in a patients state that precede the CPA are subtle. If these were appreciated, interventions would be possible, potentially preventing CPA and the associated poor outcomes and costs. The objective of this project is to use artificial intelligence to develop and implement a tool to predict CPA in critically ill children. We have been collecting and storing continuous physiologic signals in our PICU since April 2013. We have undertaken preliminary work using machine learning to analyze these data in patients who suffered a CPA. Our results indicate that earlier prediction of CPA is possible. We will use funding from this grant to: 1) Further validate, train and test the model predicting CPA, utilizing multidisciplinary expertise in a team that includes clinicians, engineers, computer scientists, and 2) Translate the model to the bedside using a visualization platform and implementation plan developed with human factors engineers and ethicists. This project is part of the new field of predictive, personalized medicine, and is associated with novel ethical dilemmas related to communicating the significance of predictions to patients and their families, which we will explore and define. An accurate model for CPA will save the lives of critically ill Canadian children and reduce the burden associated with these events. Its development will create opportunities for Canadian researchers to extend predictive modelling and redefine the healthcare team. This initiative will reduce healthcare costs, and positions Canada as a leader in using artificial intelligence to solve crucial healthcare problems.

危重儿童有心肺骤停（CPA）的风险。这些毁灭性事件在加拿大儿科重症监护病房 (PICU) 中每年发生 300-700 次，并与高死亡率（45% 至 85%）、残疾和较高的护理费用相关。 CPA 的有效管理需要临床医生立即做出反应。然而，变化 患者表示，CPA 之前的情况很微妙。如果这些得到重视，干预措施将成为可能，从而有可能预防 CPA 以及相关的不良结果和成本。该项目的目标是利用人工智能开发和实施一种工具来预测危重儿童的 CPA。自 2013 年 4 月以来，我们一直在 PICU 中收集和存储连续的生理信号。我们已利用机器学习开展了初步工作，以分析 CPA 患者的这些数据。我们的结果表明，早期预测 CPA 是可能的。 我们将利用这笔赠款的资金来：1）利用包括临床医生、工程师、计算机科学家在内的团队的多学科专业知识，进一步验证、训练和测试预测 CPA 的模型，2）使用可视化平台和与人因工程师和伦理学家共同开发的实施计划将模型转化为临床。该项目是预测、个性化医学新领域的一部分，与向患者及其家人传达预测的重要性相关的新伦理困境有关，我们将对此进行探索和定义。准确的 CPA 模型将挽救危重加拿大儿童的生命，并减轻与这些事件相关的负担。它的发展将为加拿大研究人员创造机会扩展预测模型并重新定义医疗团队。这一举措 将降低医疗保健成本，并使加拿大成为利用人工智能解决关键医疗保健问题的领导者。