Predicting and preventing all forms of cardiac arrest in hospitals with CodeRhythm™ AI software.

使用 CodeRhythm™ AI 软件预测和预防医院中各种形式的心脏骤停。

• 批准号: 10026788
• 金额: $ 71.56万
• 依托单位: TRANSFORMATIVE AI LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10026788
• 关键词: Predicting; preventing; forms; cardiac; arrest

In-hospital cardiac arrest is a leading cause of death in the UK, impacting \>15k patients per year. Only one-in-five victims ultimately leave the hospital alive.Cardiac arrest can often be prevented by making timely changes to a patient's medical care before the heart stops beating, but such changes can only be made if physicians are alerted that a patient is in danger with enough time to act. When cardiac arrest does occur, patients are much more likely to survive when physicians act right away. Delays of mere minutes can dramatically lower a patient's odds of surviving.Current ECG monitoring technology only identifies cardiac arrest after it occurs, providing no help for prevention and making rapid response difficult. Future predictive technology that alerts nurses and physicians when a patient develops a high-risk of imminent cardiac arrest could enable life-saving preventive care and faster response when cardiac arrest nonetheless occurs. Transformative is building this technology.Using advanced AI techniques originally developed at the CERN Large Hadron Collider, and having amassed the world's largest database of hospital continuous ECG monitoring data, Transformative has built a proprietary machine learning algorithm---CodeRhythm(tm)---that can predict cardiac arrests caused by a shockable rhythm. Based on initial climnical testing, CodeRhythm has a 97% accuracy at predicting shockable IHCA up to 8 hours in advance. Having proven the ability to accurately predict cardiac arrest due to shockable rhythms, Transformative now aims, in this proposed project, to improve CodeRhythm so that it can also predict non-shockable cardiac arrest.With Innovate-UK support and in collaboration with Barts NHS Trust, a 15-month project, will deliver a proof-of-concept model that predicts all types of cardiac arrest. If successful, the approach will offer the first reliable predictive monitoring software for all forms of cardiac arrest, which could improve care for high-risk patients and more than double survival rates.

在英国，医院内心脏骤停是导致死亡的主要原因，每年影响1.5万名患者。只有五分之一的患者最终活着离开医院。心脏骤停通常可以通过在心脏停止跳动之前及时改变病人的医疗护理来预防，但只有当医生意识到病人有危险并有足够的时间采取行动时，才能做出这样的改变。当心脏骤停发生时，如果医生立即采取行动，病人存活的可能性要大得多。仅仅几分钟的延迟就会大大降低病人的生存几率。目前的心电监测技术只能在心脏骤停发生后进行识别，对预防没有帮助，难以快速反应。未来的预测技术可以在病人出现即将发生的心脏骤停的高风险时提醒护士和医生，从而实现挽救生命的预防性护理，并在心脏骤停发生时更快地做出反应。变革就是建立这种技术。利用欧洲核子研究中心（CERN）大型强子对撞机（Large Hadron Collider）最初开发的先进人工智能技术，并积累了世界上最大的医院连续心电图监测数据数据库，Transformative建立了一种专有的机器学习算法——CodeRhythm（tm）——可以预测由震荡节奏引起的心脏骤停。根据最初的临床测试，CodeRhythm在提前8小时预测可休克性IHCA方面的准确率为97%。在证明了准确预测由于震荡节律引起的心脏骤停的能力之后，在这个拟议的项目中，transformable现在的目标是改进coderhym，使其也能预测非震荡性心脏骤停。在Innovate-UK的支持下，与Barts NHS Trust合作，这是一个为期15个月的项目，将提供一个概念验证模型，预测所有类型的心脏骤停。如果成功，该方法将为所有形式的心脏骤停提供第一个可靠的预测监测软件，这将改善对高危患者的护理，使生存率提高一倍以上。