Smell and taste symptom-based predictive model for COVID-19 diagnosis
Smell and taste symptom-based predictive model for COVID-19 diagnosis
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DOI：
10.1002/alr.22602
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发表时间：
2020-06-07
影响因子：
6.4
通讯作者：
Chang, Jolie L.
中科院分区：
文献类型：
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作者：
Roland, Lauren T.;Gurrola, Jose G., II;Chang, Jolie L.
背景冠状病毒2019（Covid-19）的呈现与常见的流感症状重叠。关于特定症状或收集症状是否可能有用的数据可能有用。方法是通过社交媒体宣传匿名电子调查，以查询参与者对COVID-19的测试。询问受访者有关10种症状，人口统计信息，合并症和COVID-19测试结果的质疑。逐步的逻辑回归用于识别COVID-19阳性的预测因子。使用接收器操作特征（ROC）曲线分析对选定的分类器进行了预测性能。分析总共包括145名参与者进行阳性Covid-19测试，157个参与者包括了157个参与者。参与者的平均年龄为39岁，女性为214岁（72％）。气味或味觉的变化，发烧和身体疼痛与19岁的阳性有关，呼吸急促和喉咙痛与阴性测试结果相关（p <0.05）。使用所有5种诊断症状的模型具有最高的精度，预测能力为82％，在区分COVID-19的结果方面。为了最大程度地提高灵敏度并保持公平的诊断准确性，两种症状的结合，发现气味或味觉和发烧的变化的灵敏度为70％，总体区分准确性为75％。结论气味或口味变化是强大的预测指标对于COVID-19阳性测试结果。使用闻起来或发烧的气味变化或味道变化，该简约的分类器正确预测了共vid-19测试结果的75％。为了完善分类器的性能，需要更大的受访者队列。
Background The presentation of coronavirus 2019 (COVID-19) overlaps with common influenza symptoms. There is limited data on whether a specific symptom or collection of symptoms may be useful to predict test positivity.Methods An anonymous electronic survey was publicized through social media to query participants with COVID-19 testing. Respondents were questioned regarding 10 presenting symptoms, demographic information, comorbidities, and COVID-19 test results. Stepwise logistic regression was used to identify predictors for COVID-19 positivity. Selected classifiers were assessed for prediction performance using receiver operating characteristic (ROC) curve analysis.Results A total of 145 participants with positive COVID-19 testing and 157 with negative results were included. Participants had a mean age of 39 years, and 214 (72%) were female. Smell or taste change, fever, and body ache were associated with COVID-19 positivity, and shortness of breath and sore throat were associated with a negative test result (p < 0.05). A model using all 5 diagnostic symptoms had the highest accuracy with a predictive ability of 82% in discriminating between COVID-19 results. To maximize sensitivity and maintain fair diagnostic accuracy, a combination of 2 symptoms, change in sense of smell or taste and fever was found to have a sensitivity of 70% and overall discrimination accuracy of 75%.Conclusion Smell or taste change is a strong predictor for a COVID-19-positive test result. Using the presence of smell or taste change with fever, this parsimonious classifier correctly predicts 75% of COVID-19 test results. A larger cohort of respondents will be necessary to refine classifier performance.