tetano
Editor, Senior Moderator
Clin Chem Lab Med
. 2020 Jun 29;/j/cclm.ahead-of-print/cclm-2020-0593/cclm-2020-0593.xml.
doi: 10.1515/cclm-2020-0593. Online ahead of print.
Rapid Identification of SARS-CoV-2-infected Patients at the Emergency Department Using Routine Testing
Steef Kurstjens[SUP] 1 [/SUP], Armando van der Horst[SUP] 1 [/SUP], Robert Herpers[SUP] 2 [/SUP], Mick W L Geerits[SUP] 3 [/SUP], Yvette C M Kluiters-de Hingh[SUP] 4 [/SUP], Eva-Leonne G?ttgens[SUP] 5 [/SUP], Martinus J T Blaauw[SUP] 6 [/SUP], Marc H M Thelen[SUP] 5 7 8 [/SUP], Marc G L M Elisen[SUP] 4 9 [/SUP], Ron Kusters[SUP] 1 10 [/SUP]
AffiliationsExpand
Abstract
Objectives The 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 individual's risk of SARS-CoV-2 infection at the ED. Methods In 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. Results The 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 vs. 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. Conclusions The 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.
Keywords: COVID-19; SARS-CoV-2; algorithm; coronavirus; emergency department; pandemic; prediction-model.
. 2020 Jun 29;/j/cclm.ahead-of-print/cclm-2020-0593/cclm-2020-0593.xml.
doi: 10.1515/cclm-2020-0593. Online ahead of print.
Rapid Identification of SARS-CoV-2-infected Patients at the Emergency Department Using Routine Testing
Steef Kurstjens[SUP] 1 [/SUP], Armando van der Horst[SUP] 1 [/SUP], Robert Herpers[SUP] 2 [/SUP], Mick W L Geerits[SUP] 3 [/SUP], Yvette C M Kluiters-de Hingh[SUP] 4 [/SUP], Eva-Leonne G?ttgens[SUP] 5 [/SUP], Martinus J T Blaauw[SUP] 6 [/SUP], Marc H M Thelen[SUP] 5 7 8 [/SUP], Marc G L M Elisen[SUP] 4 9 [/SUP], Ron Kusters[SUP] 1 10 [/SUP]
AffiliationsExpand
- PMID: 32598302
- DOI: 10.1515/cclm-2020-0593
Abstract
Objectives The 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 individual's risk of SARS-CoV-2 infection at the ED. Methods In 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. Results The 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 vs. 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. Conclusions The 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.
Keywords: COVID-19; SARS-CoV-2; algorithm; coronavirus; emergency department; pandemic; prediction-model.