Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule.
Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule.
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通过使用临床预测规则优先进行测试，对 SARS-CoV-2 传播和医院负担的减少进行建模。
DOI：
10.1101/2020.07.07.20148510
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Leung,DanielT
中科院分区：
文献类型：
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作者：
Reimer,JodyR;Ahmed,ShariaM;Brintz,Benjamin;Shah,RashmeeU;Keegan,LindsayT;Ferrari,MatthewJ;Leung,DanielT
及时识别病例对于减缓COVID-19的传播至关重要。然而，许多地区面临着诊断检测短缺的问题，需要在不知道这些决定对人口传播动态的影响的情况下，就谁接受检测作出困难的决定。临床预测规则（CPR）是指导临床决策的常用工具。我们使用来自电子健康记录的数据开发了一种简约的5变量CPR，以识别那些最有可能检测阳性的人，并发现其应用程序优先考虑检测增加了检测能力有限的情况下检测阳性的比例。为了考虑这些在人群水平上每日病例检测方面的收获的影响，我们将使用CPR的测试纳入到一个划分的疾病传播模型中。我们发现，优先检测导致感染高峰延迟和降低（即“曲线”），在有效生殖数量较低值（如同时采取社交距离措施）时影响最大，并且当较高比例的感染者寻求检测时。此外，优先检测导致总体感染以及医院和重症监护室（ICU）负担减少。总之，我们提出了一种基于证据的有限诊断能力分配的新方法，以实现COVID-19的公共卫生目标。
Prompt identification of cases is critical for slowing the spread of COVID-19. However, many areas have faced diagnostic testing shortages, requiring difficult decisions to be made regarding who receives a test, without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. We used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive, and found that its application to prioritize testing increases the proportion of those testing positive in settings of limited testing capacity. To consider the implications of these gains in daily case detection on the population level, we incorporated testing using the CPR into a compartmentalized disease transmission model. We found that prioritized testing led to a delayed and lowered infection peak (i.e. “flattens the curve”), with the greatest impact at lower values of the effective reproductive number (such as with concurrent social distancing measures), and when higher proportions of infectious persons seek testing. Additionally, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit (ICU) burden. In conclusion, we present a novel approach to evidence-based allocation of limited diagnostic capacity, to achieve public health goals for COVID-19.