A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
| Autor: | Pedro Antonio Salvador, Renato Seligman, Mauricio Butzke, Mariana Berger, Beatriz Graeff Santos Seligman, Pedro Castilhos de Freitas Crivelaro, Marina Petersen Saadi, Leonardo Bressan Anizelli, Tarsila Vieceli, Roberta de Souza Zappelini, Cilomar Martins de Oliveira Filho |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Microbiology (medical)
Adult Male medicine.medical_specialty Multivariate analysis Cross-sectional study Prognóstico Radiography Pneumonia Viral lcsh:QR1-502 Overfitting Sensitivity and Specificity lcsh:Microbiology Article lcsh:Infectious and parasitic diseases Predictive score 03 medical and health sciences Betacoronavirus COVID-19 Testing Predictive Value of Tests Internal medicine Infecções por coronavirus Diagnosis medicine Humans lcsh:RC109-216 Pandemics Bootstrapping (statistics) Aged 0303 health sciences 030306 microbiology business.industry Clinical Laboratory Techniques SARS-CoV-2 Diagnóstico COVID-19 Middle Aged Infectious Diseases Cross-Sectional Studies Predictive value of tests Female Radiography Thoracic Differential diagnosis Abnormality business Coronavirus Infections |
| Zdroj: | The Brazilian Journal of Infectious Diseases Repositório Institucional da UFRGS Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS Brazilian Journal of Infectious Diseases, Volume: 24, Issue: 4, Pages: 343-348, Published: 02 OCT 2020 Brazilian Journal of Infectious Diseases, Vol 24, Iss 4, Pp 343-348 (2020) Brazilian Journal of Infectious Diseases v.24 n.4 2020 Brazilian Journal of Infectious Diseases Brazilian Society of Infectious Diseases (BSID) instacron:BSID |
| ISSN: | 1678-4391 1413-8670 |
| Popis: | Objectives Differential diagnosis of COVID-19 includes a broad range of conditions. Prioritizing containment efforts, protective personal equipment and testing can be challenging. Our aim was to develop a tool to identify patients with higher probability of COVID-19 diagnosis at admission. Methods This cross-sectional study analyzed data from 100 patients admitted with suspected COVID-19. Predictive models of COVID-19 diagnosis were performed based on radiology, clinical and laboratory findings; bootstrapping was performed in order to account for overfitting. Results A total of 29% of patients tested positive for SARS-CoV-2. Variables associated with COVID-19 diagnosis in multivariate analysis were leukocyte count ≤7.7 × 103 mm–3, LDH >273 U/L, and chest radiographic abnormality. A predictive score was built for COVID-19 diagnosis, with an area under ROC curve of 0.847 (95% CI 0.77–0.92), 96% sensitivity and 73.5% specificity. After bootstrapping, the corrected AUC for this model was 0.827 (95% CI 0.75–0.90). Conclusions Considering unavailability of RT-PCR at some centers, as well as its questionable early sensitivity, other tools might be used in order to identify patients who should be prioritized for testing, re-testing and admission to isolated wards. We propose a predictive score that can be easily applied in clinical practice. This score is yet to be validated in larger populations. |
| Databáze: | OpenAIRE |
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