COVID-19: An Algorithmic Model for Critical Medical Resource Rationing in a Public Health Emergency

COVID-19：突发公共卫生事件中关键医疗资源配给的算法模型

• 批准号: EP/V050761/1
• 负责人: LI DING
• 金额: $ 14.71万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV050761%2F1
• 关键词: COVID; 19; Algorithmic; Model; Critical

The aim of the project is to develop an algorithmic model that calibrates a dynamic index for patient priority by addressing the shortcomings of the current allocation protocols of scarce medical resources. The total number of confirmed Covid-19 deaths in the UK has already passed the 45,000 mark. Such a horrific number of deaths is partly attributable to the shortage of PPE, medical staff, and ICU beds in the early stages of UK pandemic. For a second wave of Covid-19 likely in the winter when the healthcare system is most stretched, scientists have estimated that the UK could see about 120,000 new coronavirus deaths. To achieve the greatest good for the greatest number of patients, it is essential to have in place ethically and clinically sound policies on the allocation of scarce resources. Existing triage guidelines determine patient priority based on several attributes, including the illness severity and the near-term prognosis after discharge. They focus on individual patients but ignore the overall mixture of current patient profiles and the uncertainty in the number of patients who become critical ill over time. Previous research has shown that such frameworks could lead to preventative deaths and inefficient usage of scarce resources. We aim to address these limitations in this project via the development of an algorithmic model that calibrates a dynamic index (priority). Its performance is to be compared against the benchmarks via an empirical study using anonymised data of Covid-19 patients collected by Public Health England.

该项目的目的是开发一个算法模型，通过解决稀缺医疗资源的当前分配协议的缺点，校准患者优先级的动态指数。英国确诊的新冠肺炎死亡总人数已经超过4.5万人。如此可怕的死亡人数部分归因于英国大流行早期阶段个人防护设备、医务人员和重症监护病房床位的短缺。科学家们估计，新冠肺炎的第二波疫情可能会在冬季出现，届时医疗体系将最为紧张。为了使最多的病人获得最大的利益，必须制定在伦理和临床上合理的稀缺资源分配政策。现有的分流指南根据几个属性确定患者的优先级，包括疾病的严重程度和出院后的近期预后。他们专注于个别患者，但忽略了当前患者概况的整体混合以及随着时间的推移变得危重的患者数量的不确定性。以前的研究表明，这种框架可能导致预防性死亡和稀缺资源的低效使用。我们的目标是通过开发一个算法模型来校准动态索引（优先级），从而解决该项目中的这些限制。其表现将通过一项实证研究与基准进行比较，该研究使用了英国公共卫生部收集的2019冠状病毒病患者匿名数据。