Effect of COVID-19 on Thrombosis Incidence and Patient Prognosis in Kidney Transplant Recipients
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND Thrombosis poses a grave threat to patients undergoing kidney transplants, with a heightened risk of mortality. While previous studies have established a link between COVID-19 and thrombosis, the specific association between COVID-19 and thrombosis in this patient population remains unexplored. MATERIAL AND METHODS We conducted a retrospective analysis utilizing data from 394 individuals who underwent kidney transplantation within the period of September 1, 2015, to April 1, 2023. To evaluate overall survival, we employed Kaplan-Meier analysis and utilized a logistic regression model for risk analysis. Furthermore, we developed a prediction model and assessed its accuracy through calibration curves. RESULTS Out of the 394 patients included in our study, a total of 51 individuals experienced thrombosis, resulting in 2 deaths. Our analysis revealed that COVID-19 infection significantly increased the risk of thrombosis (odds ratio [OR] 8.60, 95% confidence interval 3.13-24.74, P<0.01). Additionally, the use of cyclosporine was found to elevate the risk of death (OR 20.86, 95% CI 7.93-59.24, P<0.01) according to multifactorial analysis. Logistic models were employed to screen variables, and predictive models were constructed based on the presence of COVID-19 infection and the usage of cyclosporine. A nomogram was developed, demonstrating promising accuracy in estimating the risk of thrombosis during internal validation, with a corrected C-index of 0.869. CONCLUSIONS Our study suggests that both COVID-19 infection and the use of cyclosporine can serve as reliable predictors of thrombosis risk in patients undergoing renal transplantation. Furthermore, we developed a mortality risk prediction model based on COVID-19 in assessing thrombosis.
Source: Medical Science Monitor
DOI: 10.12659/msm.944285
Authors: Hao Jiang, Zhijun Cao, Li Liu, Yuhua Huang
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Award: G512207018
OpenAlex ID: W4400221875
