A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
Date
2020Author
Vieceli, Tarsila
Oliveira Filho, Cilomar Martins de
Berger, Mariana
Petersen Saadi, Marina
Salvador, Pedro Antonio
Bressan Anizelli, Leonardo
Freitas Crivelaro, Pedro Castilhos de
Butzke, Mauricio
Souza Zappelini, Roberta de
Santos Seligman, Beatriz Graeff dos
Seligman, Renato
Metadata
Show full item recordAbstract
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
Link to resource
https://doi.org/10.1016/j.bjid.2020.06.009Collections
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