Haematological and radiological-based prognostic markers of COVID-19

Alsagaby, Suliman A.; Aljouie, Abdulrhman; Alshammari, Talal H.; Ahmad Mir, Shabir; Alhumaydhi, Fahad A.; Al Abdulmonem, Waleed; Alshaalan, Hesham; Alomaish, Hassan; Daghistani, Rayyan; Alsehawi, Ali; Alharbi, Naif Khalaf

BACKGROUND: Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged in 2019 and caused a global pandemic in 2020, manifesting in the coronavirus disease 2019 (COVID-19). The majority of patients exhibit a mild form of the disease with no major complications; however, moderate to severe and fatal cases are of public health concerns. Predicting the potential prognosis of COVID-19 could assist healthcare workers in managing the case and controlling the pandemic in an effective way. METHODS: Here, clinical data of COVID-19 patients admitted to two large centers in Saudi Arabia between April and June 2020 were retrospectively analysed. The objectives of the study were to search for biomarkers associated with COVID-19 mortality and predictors of the overall survival (OS) of the patients. RESULTS: More than 23% of the study subjects with available data have died, enabling the prediction of mortality in our cohort. Markers that were significantly associated with mortality in our study were older age, increased D-dimer in the blood, higher counts of WBCs, higher percentage of neutrophil, and a higher chest X-ray (CXR) score. The CXR scores were also positively associated with age, D-dimer, WBC count, and percentage of neutrophil. This supports the utility of CXR scores in the absence of blood testing. Predicting mortality based on Ct values of RT-PCR was not successful, necessitating a more quantitative RT-PCR to determine virus quantity in samples. Our work has also identified age, D-dimer concentration, leukocyte parameters and CXR score to be prognostic markers of the OS of COVID-19 patients. CONCLUSION: Overall, this retrospective study on hospitalised cohort of COVID-19 patients presents that age, haematological, and radiological data at the time of diagnosis are of value and could be used to guide better clinical management of COVID-19 patients.