Steef Kurstjens1, Armando van der Horst1, Robert Herpers2, Mick W L Geerits3, Yvette C M Kluiters-de Hingh4, Eva-Leonne Göttgens5, Martinus J T Blaauw6, Marc H M Thelen5,7,8, Marc G L M Elisen4,9, Ron Kusters1,10. 1. Laboratory for Clinical Chemistry and Hematology, Jeroen Bosch Hospital, 5223 GZ, Den Bosch, the Netherlands. 2. Laboratory of Clinical Chemistry and Hematology, Bernhoven Hospital, Uden, the Netherlands. 3. Abnormal Design Ltd, London, UK. 4. Laboratory of Clinical Chemistry and Hematology, Elisabeth TweeSteden Hospital, Tilburg, the Netherlands. 5. Laboratory of Clinical Chemistry and Hematology, Amphia Hospital, Breda, the Netherlands. 6. Department of Internal Medicine, Bernhoven Hospital, Uden, the Netherlands. 7. Dutch Foundation for Quality Assessment in Medical Laboratories (SKML), Nijmegen, the Netherlands. 8. Chair in 'Quality in Medical Laboratory Care', Radboud University, Nijmegen, the Netherlands. 9. Netherlands Society of Clinical Chemistry and Laboratory Medicine (NVKC), Utrecht, the Netherlands. 10. Department of Health Technology and Services Research, Technical Medical Centre, University of Twente, Enschede, the Netherlands.
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.
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.
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Authors: Caitlin M Dugdale; David M Rubins; Hang Lee; Suzanne M McCluskey; Edward T Ryan; Camille N Kotton; Rocio M Hurtado; Andrea L Ciaranello; Miriam B Barshak; Dustin S McEvoy; Sandra B Nelson; Nesli Basgoz; Jacob E Lazarus; Louise C Ivers; Jennifer L Reedy; Kristen M Hysell; Jacob E Lemieux; Howard M Heller; Sayon Dutta; John S Albin; Tyler S Brown; Amy L Miller; Stephen B Calderwood; Rochelle P Walensky; Kimon C Zachary; David C Hooper; Emily P Hyle; Erica S Shenoy Journal: Clin Infect Dis Date: 2021-12-16 Impact factor: 9.079
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Authors: Jacqueline Dinnes; Jonathan J Deeks; Ada Adriano; Sarah Berhane; Clare Davenport; Sabine Dittrich; Devy Emperador; Yemisi Takwoingi; Jane Cunningham; Sophie Beese; Janine Dretzke; Lavinia Ferrante di Ruffano; Isobel M Harris; Malcolm J Price; Sian Taylor-Phillips; Lotty Hooft; Mariska Mg Leeflang; René Spijker; Ann Van den Bruel Journal: Cochrane Database Syst Rev Date: 2020-08-26
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