Departments of Health Administration and Policy (Drs Alemi, Vang, and Wojtusiak and Ms Guralnik) and Global and Community Health (Dr Roess), College of Health and Human Services, George Mason University, Fairfax, Virginia; and Vibrent Health, Inc, Fairfax, Virginia (Ms Peterson and Mr Jain).
Qual Manag Health Care. 2023;32(Suppl 1):S3-S10. doi: 10.1097/QMH.0000000000000398.
This article describes how multisystemic symptoms, both respiratory and nonrespiratory, can be used to differentiate coronavirus disease-2019 (COVID-19) from other diseases at the point of patient triage in the community. The article also shows how combinations of symptoms could be used to predict the probability of a patient having COVID-19.
We first used a scoping literature review to identify symptoms of COVID-19 reported during the first year of the global pandemic. We then surveyed individuals with reported symptoms and recent reverse transcription polymerase chain reaction (RT-PCR) test results to assess the accuracy of diagnosing COVID-19 from reported symptoms. The scoping literature review, which included 81 scientific articles published by February 2021, identified 7 respiratory, 9 neurological, 4 gastrointestinal, 4 inflammatory, and 5 general symptoms associated with COVID-19 diagnosis. The likelihood ratio associated with each symptom was estimated from sensitivity and specificity of symptoms reported in the literature. A total of 483 individuals were then surveyed to validate the accuracy of predicting COVID-19 diagnosis based on patient symptoms using the likelihood ratios calculated from the literature review. Survey results were weighted to reflect age, gender, and race of the US population. The accuracy of predicting COVID-19 diagnosis from patient-reported symptoms was assessed using area under the receiver operating curve (AROC).
In the community, cough, sore throat, runny nose, dyspnea, and hypoxia, by themselves, were not good predictors of COVID-19 diagnosis. A combination of cough and fever was also a poor predictor of COVID-19 diagnosis (AROC = 0.56). The accuracy of diagnosing COVID-19 based on symptoms was highest when individuals presented with symptoms from different body systems (AROC of 0.74-0.81); the lowest accuracy was when individuals presented with only respiratory symptoms (AROC = 0.48).
There are no simple rules that clinicians can use to diagnose COVID-19 in the community when diagnostic tests are unavailable or untimely. However, triage of patients to appropriate care and treatment can be improved by reviewing the combinations of certain types of symptoms across body systems.
本文描述了如何在社区患者分诊时,使用呼吸系统和非呼吸系统的多种系统症状来区分 2019 年冠状病毒病(COVID-19)与其他疾病。本文还展示了如何使用症状组合来预测患者患有 COVID-19 的可能性。
我们首先使用范围性文献综述来确定在全球大流行的第一年报告的 COVID-19 症状。然后,我们对有报告症状和最近逆转录聚合酶链反应(RT-PCR)检测结果的个体进行了调查,以评估从报告症状诊断 COVID-19 的准确性。该范围性文献综述包括截至 2021 年 2 月发表的 81 篇科学文章,确定了与 COVID-19 诊断相关的 7 种呼吸症状、9 种神经症状、4 种胃肠症状、4 种炎症症状和 5 种全身症状。从文献中报告的症状的敏感性和特异性估计与每个症状相关的似然比。然后,对总共 483 名个体进行了调查,以验证使用文献综述中计算的似然比根据患者症状预测 COVID-19 诊断的准确性。调查结果进行了加权处理,以反映美国人口的年龄、性别和种族。使用受试者工作特征曲线下的面积(AUC)评估从患者报告的症状预测 COVID-19 诊断的准确性。
在社区中,咳嗽、喉咙痛、流鼻涕、呼吸困难和缺氧本身并不能很好地预测 COVID-19 的诊断。咳嗽和发热的组合也不能很好地预测 COVID-19 的诊断(AUC = 0.56)。根据症状诊断 COVID-19 的准确性最高时,个体出现来自不同身体系统的症状(AUC 为 0.74-0.81);当个体仅出现呼吸系统症状时,准确性最低(AUC = 0.48)。
在无法获得或时机不合适的诊断测试时,临床医生无法使用简单的规则在社区中诊断 COVID-19。然而,通过审查跨身体系统的某些类型症状的组合,可以改善对患者进行适当护理和治疗的分诊。