2026-02-25 ペンシルベニア州立大学（Penn State）
＜関連情報＞
- https://www.psu.edu/news/research/story/covid-19-infection-predicts-higher-risk-kidney-disease-study-finds
- https://www.nature.com/articles/s43856-026-01460-6
- https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(25)00170-7/fulltext
インフルエンザと比較して、SARS-CoV-2感染後の腎臓病のリスクは増加します The risk of kidney disease increases following SARS-CoV-2 infection compared to influenza
Yue Zhang,Nasrollah Ghahramani,Vernon M. Chinchilli & Djibril M. Ba
Communications Medicine Published:25 February 2026
DOI:https://doi.org/10.1038/s43856-026-01460-6 Unedited version
Abstract
Background
Although case reports and observational studies suggest COVID-19 increases the risk of kidney diseases, real-world evidence comparing it with influenza is limited. Our study aims to assess the association between COVID-19 infections and subsequent kidney diseases, using influenza as a positive control and incorporating a negative control to establish clearer associations.
Methods
A large retrospective cohort study with strata matching was conducted using the MarketScan database with records from Jan. 2020 to Dec. 2021. We used the ICD-10 codes to identify individuals and build three cohorts: (1) COVID-19 group, (2) Positive control group: Influenza but no COVID-19, and (3) Negative control group: no COVID-19 / Influenza. The outcomes were acute kidney injury (AKI), chronic kidney disease (CKD), end-stage renal disease (ESRD), and glomerular diseases. Multivariable stratified Cox proportional hazards regression analyses were performed.
Results
The study includes 939,241 individuals with COVID-19, 1,878,482 individuals in the negative control group, and 199,071 individuals with influenza. COVID-19 is significantly associated with increased risks of AKI (adjusted hazards ratio, aHR: 2.74; 95% CI, 2.61-2.87), CKD (aHR: 1.38, 1.32-1.45), ESRD (aHR: 3.22, 2.67-3.88), and glomerular diseases (aHR:1.28, 1.09-1.50), while influenza has no impact on CKD, ESRD, and glomerular diseases. Time-specific analyses indicate that COVID-19 has stronger effects on AKI in the short term but has stable long-term effects on CKD.
Conclusions
In this large real-world study of working-age, commercially insured adults in the United States, COVID-19 infection is associated with a 2.3-fold risk of developing AKI, a 1.4-fold risk of CKD, and a 4.7-fold risk of ESRD compared to influenza. Greater attention to kidney diseases post-COVID-19 is essential to prevent future adverse health outcomes.
Plain Language Summary
COVID-19, caused by the SARS-CoV-2 virus, has been linked to multiple organ complications, with emerging evidence suggesting effects on kidney diseases. However, it is unclear how the risk of kidney disease after COVID-19 compares with influenza, another common viral infection. In this study, we analyzed commercial health insurance data from over three million working-age adults in the United States to compare individuals with COVID-19, those with influenza, and those with neither infection. We found that individuals who had COVID-19 were more likely to develop kidney problems, including short-term injury and long-term chronic disease. These findings suggest that COVID-19 may have a stronger impact on kidney diseases than influenza, highlighting the need for greater attention and monitoring of kidney function after COVID-19 infection.
機械学習モデルを用いたCOVID-19パンデミック後の急性および慢性腎臓病の予測：米国における国民電子健康記録の活用 Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US
Yue Zhang ∙ Nasrollah Ghahramani ∙ Runjia Li ∙ Vernon M. Chinchilli ∙ Djibril M. Ba
eBioMedicine Published April 26, 2025
DOI:https://doi.org/10.1016/j.ebiom.2025.105726
Summary
Background
COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these risks post-pandemic have been absent. We aimed to use large electronic health records (EHR) and ML algorithms to predict the incidence of AKI and CKD during the post-pandemic period, assess the necessity of including COVID-19 infection history as a predictor, and develop a practical webpage application for clinical use.
Methods
National EHR data from TriNetX, emulating a prospective cohort of 104,565 patients from 07/01/2022 to 03/31/2024, were used. A total of 69 baseline variables were included, with demographics, comorbidities, lab test results, vital signs, medication histories, hospitalization visits, and COVID-19-related variables. Prediction windows of 1 month and 1 year were defined to assess AKI and CKD incidence. Eight machine learning models, primarily including extreme gradient boosting (XGBoost), neural network, and random forest (RF), were applied. Cross-validation and model tuning were conducted during the training process. Model performance was evaluated using six metrics, including the area under the receiver-operating-characteristic curve (AUROC). A combination of model-driven, data-driven, and clinical-driven methods was employed to identify the final models. An application with the final models was built using the R Shiny framework.
Findings
The final models, incorporating 9 variables—primarily including eGFR, inpatient visit number, and number of COVID-19 infections—were selected. XGBoost demonstrated the best performance for predicting the incidence of AKI in 1 month (AUROC = 0.803), AKI in 1 year (AUROC = 0.799), and CKD in 1 year (AUROC = 0.894). Random Forest (RF) was selected for predicting the incidence of CKD in 1 month (AUROC = 0.896). A comparison of AUROC with and without COVID-19 infection confirmed its importance as a critical predictor in the model. The final models were translated into a convenient tool to facilitate their use in clinical settings.
Interpretation
Our study demonstrates the applicability of using large national EHR data in developing high-performance machine learning models to predict AKI and CKD risks in the post-COVID-19 period. Incorporating the number of COVID-19 infections in the past year showed improved prediction performance and should be considered in future models for kidney disease prediction. A user-friendly application was created to support clinicians in risk assessment and surveillance.
Funding
Artificial Intelligence and Biomedical Informatics Pilot Funding, Penn State College of Medicine.
