Using Artificial Intelligence Algorithms to Predict the Overall Survival of Hemodialysis Patients during the COVID-19 Pandemic: A Prospective Cohort Study.

Abstract

Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to utilize artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. A cohort of 433 HD patients was utilized to develop machine learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine learning models, including CatBoost, LightGBM, RandomForest, and XGBoost, were applied to evaluate their discriminative performance using the patient cohorts. Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with AUC values of 0.94 on the testing dataset. The SHapley Additive explanation summary (SHAP) plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Moreover, the fully vaccinated group exhibited higher levels of anti-S RBD antibodies. This prospective cohort study involved utilizing artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.