Biomarkers for predicting COVID-19 mortality using the joint modelling approach

Abstract

Abstract Background Evidence showed the role of biomarkers in predicting severity and mortality of coronavirus disease 2019 (COVID-19). We evaluated associations between several biomarkers measured throughout the follow-up and COVID-19 mortality using the joint modelling (JM) approach, the candidate tool for this kind of data. Methods Between February and May 2020, a total of 403 COVID-19 patients were admitted. Baseline characteristics included sex and age, whereas biomarkers included lymphocytes, neutrophils, D-dimer, ferritin, C-reactive protein, glucose and lactate dehydrogenase (LDH). Hazard ratios (HR) and 95% confidence interval (CI) were estimated through JM using a Bayesian approach. We fitted univariable and multivariable JMs including a single biomarker and the set of all biomarkers, respectively. Results In univariable JMs, all biomarkers were significantly associated with COVID-19 mortality. In the multivariable JM, HRs were 1.78 (95% CI: 1.13–2.87) per doubling of neutrophils levels, 1.49 (95% CI: 1.19–1.95) per doubling of C-reactive protein levels, 2.66 (95% CI: 1.45–4.95) per an increase of 100 mg/dL of glucose, and 1.31 (95% CI: 1.12–1.55) per an increase of 100 U/L of LDH. No evidence of association was observed for ferritin and lymphocytes in the multivariable JM. Men had a higher risk of COVID-19 mortality than women (HR = 1.75; 95% CI: 1.07–2.80) and age showed the strongest effect with risk starting to rapidly increase from 60 years. Conclusions These findings using JM confirm the usefulness of biomarkers in assessing COVID-19 severity and mortality. Monitoring trend patterns of such biomarkers can provide additional help in tailoring the more appropriate care pathway.