A predictive score for COVID-19 diagnosis using clinical, laboratory and chest image data
Tarsila Vieceli, Cilomar Martins de Oliveira Filho, Mariana Berger do Rosario, Marina Petersen Saadi, Pedro Antonio Salvador, Leonardo Bressan Anizelli, Pedro Castilhos de Freitas Crivelaro, Maurício Butzke, Roberta de Souza Zappelini, Beatriz Graeff dos Santos Seligman, Renato Seligman
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: , 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.