PREDICTION OF SEVERITY OF COVID-19-INFECTED PATIENTS USING CD24-CSF1R INDEX

Abstract

The COVID-19 pandemic causes serious clinical manifestations. Most patients are asymptomatic; however, critically ill patients still face a high risk of organ injury and/or worsening death. Due to asymptotia and unexpected respiratory complications, healthcare systems have been overloaded. Thus, this study aimed to identify a predictor that can predict the COVID-19 severity. Clinical and gene expression data were retrieved from the Gene Expression Omnibus database, which consists of 126 patients. Raw data were processed using the Transcripts Per Million (TPM) method and then transformed using log2 (TPM+1) for normalization. Violin plots, Kaplan-Meier curves, ROC curves, and multivariate proportional Cox regression analyses were performed to evaluate the prognostic value of the established index. We found that the CD24-CSF1R Index was significantly associated with ICU admission and ventilatory status. The ROC curve produced a relatively accurate prediction of ICU admission with an AUC of 0.8524. In addition, patients with a high index had significantly fewer ventilator-free days than patients with a low index. Furthermore, the established index showed a strong prognostic ability for the risk of using machine ventilation in multivariate Cox regression analysis. In conclusion, we investigated the CD24-CSF1R Index as a novel predictor of COVID-19 severity. A high index score was associated with COVID-19 severity. The established index could be considered as a potential biomarker that improves the effectiveness of patients’ severe stratification, prognostic methods, and lightens the healthcare system load in COVID-19.