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. 2020 Jul-Aug;24(4):343-348.
doi: 10.1016/j.bjid.2020.06.009. Epub 2020 Jul 25.

A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data

Affiliations

A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data

Tarsila Vieceli et al. Braz J Infect Dis. 2020 Jul-Aug.

Abstract

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 ≤7.7×103mm-3, LDH >273U/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.

Keywords: COVID-19; Diagnosis; Predictive score; SARS-CoV-2.

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Figures

Fig. 1
Fig. 1
ROC Curve for model including leukocyte count, LDH and chest radiography abnormality.

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