ORCID
- Shang-Ming Zhou: 0000-0002-0719-9353
Abstract
ObjectivesThe chronic kidney disease (CKD) patients were at high risk for severe clinical complications during the COVID-19 pandemic. Our objectives were to evaluate comorbidity prevalence; predict mortality risks for CKD patients during the pandemic; assess how various health factors interact to influence mortality; and provide insights for targeted prevention strategies.MethodWe analysed data from 186,396 CKD patients in Mexico during the entire pandemic (Jan 2020- May 2023). Explainable artificial intelligence (XAI) methods with extreme gradient boosting (XGBoost) models and Shapley Additive Explanations (SHAP) were developed to predict mortality for CKD patients with model interpretations. Different metrics were used to comprehensively evaluate model’s generalisation performances.ResultsThe most prevalent comorbidities were hypertension (64.39 %), diabetes (49.79 %), and obesity (16.46 %). Male patients and older individuals showed higher risk for adverse outcomes. The overall mortality rate was 19.33 %, with significantly higher mortality in COVID-19 positive patients (33.9 %) compared to COVID-19 negative patients (10.1 %). Comorbidities with the most significant impact on the mortality included diabetes, hypertension, and obesity, which were more frequent in the COVID-19 positive group and associated with higher rates of intubation, and ICU admission. Pneumonia was identified as a major predictor of negative outcomes in CKD patients with COVID-19. CVD was more common in the COVID-19 negative group. Our machine learning models achieved performances of AUC= 0.76 and F1-score= 0.75 for predicting mortality during the pandemic.ConclusionTargeted management of comorbid conditions, especially respiratory infections, is crucial in CKD patients during pandemics.
DOI Link
Publication Date
2025-11-25
Publication Title
Annals of Epidemiology
Volume
113
ISSN
1047-2797
Acceptance Date
2025-11-23
Deposit Date
2025-11-28
Funding
SMZ was supported in part by the UK CDT in Artificial Intelligence, Machine Learning and Advanced Computing (EP/S023992/1) and the international collaboration with Guangxi University “Digital ASEAN Cloud Big Data Security and Mining Technology” Innovation Team. LH was supported in part by the Major Project of National Social Science Foundation of China (16ZDA0092), and in part by Guangxi University “Digital ASEAN Cloud Big Data Security and Mining Technology” Innovation. The funders had no involvement in the study design, collection, analysis and interpretation of data, writing of the report and decision to submit the article for publication.
Keywords
COVID-19, Chronic kidney disease, Comorbidities, Mortality, Machine learning, Interpretability, Risk factors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
First Page
1
Last Page
12
Recommended Citation
Abdollahi, Z., Huo, L., Fraser, D., & Zhou, S. (2025) 'Unravelling impact of comorbidities on mortality risks in CKD patients during the COVID-19 pandemic: An explainable AI-driven study', Annals of Epidemiology, 113, pp. 1-12. Available at: 10.1016/j.annepidem.2025.11.005
