Evaluation of Time-Varying Biomarkers in Mortality Outcome in COVID-19: an Application of Extended Cox Regression Model

BACKGROUND: COVID-19 pandemic has created many challenges for clinicians. The monitoring trend for laboratory biomarkers is helpful to provide additional information to determine the role of those in the severity status and death outcome. OBJECTIVE: This article aimed to evaluate the time-varying biomarkers by LOWESS Plot, check the proportional hazard assumption, and use to extended Cox model if it is violated. METHODS: In the retrospective study, we evaluated a total of 1641 samples of confirmed patients with COVID-19 from October until March 2021 and referred them to the central hospital of Ayatollah Rohani Hospital affiliated with Babol University of medical sciences, Iran. We measured four biomarkers AST, LDH, NLR, and lymphocyte in over the hospitalization to find out the influence of those on the rate of death of COVID-19 patients. RESULTS: The standard Cox model suggested that all biomarkers were prognostic factors of death (AST: HR=2.89, P<0.001, Lymphocyte: HR=2.60, P=0.004, LDH: HR=2.60, P=0.006, NLR: HR=1.80, P<0.001). The additional evaluation showed that the PH assumption was not met for the NLR biomarker. NLR biomarkers had a significant time-varying effect, and its effect increase over time (HR(t)=exp (0.234+0.261×log(t)), p=0.001). While the main effect of NLR did not show any significant effect on death outcome (HR=1.26, P=0.097). CONCLUSION: The reversal of results between the Cox PH model and the extended Cox model provides insight into the value of considering time-varying covariates in the analysis, which can lead to misleading results otherwise.