Predicting immunogenicity of COVID-19 vaccines in hemodialysis patients with renal disease.

Autor:	Awad M Alqahtani S; Physiology Department, Taibah University, Saudi Arabia., Mahallawi WH; Medical Laboratory Technology Department, College of Applied Medical Sciences, Taibah University, Madinah, Saudi Arabia., Alomar S; Zoology Department, College of Science, King Saud University, P.O. Box: 2455, 11451, Riyadh, Saudi Arabia.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 Mar 07; Vol. 10 (6), pp. e27594. Date of Electronic Publication: 2024 Mar 07 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e27594
Abstrakt:	Individuals who are diagnosed with chronic kidney disease, particularly those receiving maintenance hemodialysis treatment, face a greater likelihood of suffering from severe symptoms and fatality due to COVID-19. This study aimed to explore the optimal vaccination approach for these individuals. The study used data analysis tasks such as data preprocessing, cleaning, and exploration, and machine learning models including linear regression, random forest, XGBoost, gradient boosting, AdaBoost, decision trees, Lasso, and ridge regression were used to construct the predictive model. The study found that the Lasso model performed the best overall in predicting anti-S IgG antibodies levels in response to COVID-19 vaccines for people with kidney failure with MAE of 8.81, RMSE of 19.59, and R 2 value of 0.93. The adjusted R 2 value for the Lasso model was also 0.93, indicating that the model's ability to explain the variance in the data was not affected by the number of predictors in the model. The Random Forest model best predicted the duration of immunogenicity, with R 2 and adjusted R 2 values of 0.71 and 0.69, respectively. The ensemble model that includes all eight models, i.e., Ridge, Lasso, Linear Regression, Random Forest, AdaBoost, Gradient Boosting, XGBoost, and Decision Tree, has the best performance with the lowest MAE, the lowest RMSE, the highest R2, and the highest adjusted R2 values of 3.91, 5.00, 0.73, and 0.72, respectively. However, further research is required to validate these models and extend their application to different populations and vaccine types, as well as considering other factors that may affect immune response to COVID-19 vaccines. These findings can be helpful in improving vaccination strategies and promoting public health. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 Published by Elsevier Ltd.)
Databáze:	MEDLINE
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