A chest-CT and clinical chemistry based flowchart for rapid COVID-19 triage at emergency departments - a multicenter study

BackgroundDue to the large number of patients with coronavirus disease 19 (COVID-19), rapid diagnosis at the emergency department (ED) is of critical importance. In this study we have developed a flowchart based on two well-known diagnostic methods: the corona-score and the CO-RADS. This flowchart can be used in hospitals that use chest-CT, instead of chest X-ray, for COVID-19 suspected patients at the ED. MethodsED patients (n=1904) from the Jeroen Bosch Hospital, Amphia Hospital, HagaHospital, Elisabeth TweeSteden Hospital, Bernhoven Hospital and Slingeland Hospital were included. A laboratory-based corona-score, without radiology, called the lab-corona-score was combined with a chest-CT based radiology scoring system (CO-RADS), to develop a flowchart. The performance was assessed by sensitivity/specificity analyses using the RT-PCR outcome or the physicians final diagnosis as golden standard. ResultsOut of the 1904 patients, 611 (32.1%) patients tested positive for the SARS-CoV-2 virus. The lab-corona-score alone had an AUC of 0.86, a sensitivity of 87% and a specificity of 88% using cut-off values of 0-2 (negative) and 8-10 (positive). Of 255 patients, from the Amphia and Slingeland Hospitals, a CO-RADS score was determined. The flowchart, which combined the CO-RADS with the lab-corona-score, was developed based on data from Slingeland Hospital (sensitivity 97%, specificity 96%). Hereafter, the performance of the flowchart was validated using an independent dataset from Amphia hospital, and reached a sensitivity of 98% and specificity of 93%. A decision could be made in 79% of the patients, which was correct in 95% of the cases. ConclusionThis flowchart, based on radiology (CO-RADS) and clinical chemistry parameters (lab-corona-score), results in a rapid and accurate diagnosis of COVID-19 at the ED.

Declining SARS-CoV-2 PCR sensitivity with time and dependence on clinical features: consequences for control

Real-time reverse transcription-polymerase chain reaction (RT-PCR) on upper respiratory tract (URT) samples is the primary method to diagnose SARS-CoV-2 infections and guide public health measures, with a supportive role for serology. However, the clinical sensitivity of RT-PCR remains uncertain. In the present study, Bayesian statistical modeling was used to retrospectively determine the sensitivity of RT-PCR using SARS-CoV-2 serology in 644 COVID-19-suspected patients with varying degrees of disease severity and duration. The sensitivity of RTPCR ranged between 79-95%; while increasing with disease severity, it decreased rapidly over time in mild COVID-19 cases. Negative URT RT-PCR results should therefore be interpreted in the context of clinical characteristics, especially with regard to containment of viral transmission based on the test, trace and isolate principle.

Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing

BackgroundThe novel coronavirus disease 19 (COVID-19), caused by SARS-CoV-2, spreads rapidly across the world. The exponential increase in the number of cases has resulted in overcrowding of emergency departments (ED). Detection of SARS-CoV-2 is based on an RT-PCR of nasopharyngeal swab material. However, RT-PCR testing is time-consuming and many hospitals deal with a shortage of testing materials. Therefore, we aimed to develop an algorithm to rapidly evaluate an individuals risk of SARS-CoV-2 infection at the ED. MethodsIn this multicenter retrospective study, routine laboratory parameters (C-reactive protein, lactate dehydrogenase, ferritin, absolute neutrophil and lymphocyte counts), demographic data and the chest X-ray/CT result from 967 patients entering the ED with respiratory symptoms were collected. Using these parameters, an easy-to-use point-based algorithm, called the corona-score, was developed to discriminate between patients that tested positive for SARS-CoV-2 by RT-PCR and those testing negative. Computational sampling was used to optimize the corona-score. Validation of the model was performed using data from 592 patients. ResultsThe corona-score model yielded an area under the receiver operating characteristic curve of 0.91 in the validation population. Patients testing negative for SARS-CoV-2 showed a median corona-score of 3 versus 11 (scale 0-14) in patients testing positive for SARS-CoV-2 (p<0.001). Using cut-off values of 4 and 11 the model has a sensitivity and specificity of 96% and 95%, respectively. ConclusionThe corona-score effectively predicts SARS-CoV-2 RT-PCR outcome based on routine parameters. This algorithm provides the means for medical professionals to rapidly evaluate SARS-CoV-2 infection status of patients presenting at the ED with respiratory symptoms.