Unravelling impact of comorbidities on mortality risks in CKD patients during the COVID-19 pandemic: An explainable AI-driven study.

OBJECTIVES

The 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.

METHOD: We 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.

RESULTS

The 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.

CONCLUSION

Targeted management of comorbid conditions, especially respiratory infections, is crucial in CKD patients during pandemics.