RÉSUMÉ
BackgroundFalse negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike. ObjectiveTo develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive. DesignRetrospective Cohort Study (Ethics number of Tongji Hospital: No. IRBID: TJ-20200320) SettingA database of outpatients in Tongji Hospital (University Hospital) from 15 January 2020 to 19 February 2020. Patients1,324 outpatients with COVID-19 MeasurementsClinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive. ResultsOf the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. "Ground glass opacity" in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. "Consolidation" was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly. LimitationData were generated in a large single-center study. ConclusionTesting outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world. Primary Funding SourceNone.