Background

Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid-based reverse transcription polymerase chain reaction (PCR) testing.

Methods

This retrospective case-control study involves subjects (7 to 98 years) presenting at the designated national outbreak screening centre and tertiary care hospital in Singapore for SARS-CoV-2 testing from January 26 to February 16, 2020. COVID-19 status was confirmed by PCR testing of sputum, nasopharyngeal swabs or throat swabs. Demographic, clinical, laboratory and exposure-risk variables ascertainable at presentation were analyzed to develop an algorithm for estimating the risk of COVID-19. Model development used Akaike’s information criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction scores. Performance was measured using receiver operating characteristics curves, adjusting for over-confidence using leave-out-one cross validation.

Results

The study population included 788 subjects, of whom 54 (6.9%) were SARS-CoV-2 positive and 734 (93.1%) were SARS-CoV-2 negative. The median age was 34 years and 407 (51.7%) were female. Using leave-out-one cross validation, all the models incorporating clinical tests (Models 1, 2 and 3) performed well with areas under the receiver operating characteristics curve (AUC) of 0.91, 0.88 and 0.88 respectively. In comparison, Model 4 had an AUC of 0.65.

Conclusions

Rapidly ascertainable clinical and laboratory data could identify individuals at high risk of COVID-19 and enable prioritization of PCR-testing and containment efforts. Basic laboratory test results were crucial to prediction models.

中文翻译：

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COVID-19 的流行病学和临床预测因素。

背景

快速识别 COVID-19 病例对于遏制疫情工作至关重要，但由于缺乏特征性症状，且在基于核酸的专门逆转录聚合酶链反应 (PCR) 检测能力有限的环境中，快速识别 COVID-19 病例具有挑战性。

方法

这项回顾性病例对照研究涉及 2020 年 1 月 26 日至 2 月 16 日期间在新加坡指定的国家疫情筛查中心和三级护理医院进行 SARS-CoV-2 检测的受试者（7 至 98 岁）。COVID-19 状态得到确认通过痰液、鼻咽拭子或咽喉拭子进行 PCR 检测。对就诊时可确定的人口统计、临床、实验室和暴露风险变量进行了分析，以开发一种用于估计 COVID-19 风险的算法。模型开发逐步使用 Akaike 的信息标准来构建逻辑回归模型，然后将其转化为预测分数。使用接收者操作特征曲线来测量性能，并使用留一交叉验证来调整过度自信。

结果

研究人群包括 788 名受试者，其中 54 名 (6.9%) 为 SARS-CoV-2 阳性，7​​34 名 (93.1%) 为 SARS-CoV-2 阴性。中位年龄为 34 岁，其中 407 名 (51.7%) 为女性。使用留一交叉验证，所有纳入临床测试的模型（模型 1、2 和 3）均表现良好，受试者工作特征曲线 (AUC) 下面积分别为 0.91、0.88 和 0.88。相比之下，Model 4 的 AUC 为 0.65。

结论

快速确定的临床和实验室数据可以识别感染 COVID-19 的高风险个体，并确定 PCR 检测和遏制工作的优先顺序。基本实验室测试结果对于预测模型至关重要。