Kidney Week

Abstract: TH-PO010

Using Machine-Learning Algorithms to Predict the Risk of AKI and CKD during the COVID-19 Pandemic Based on National Electronic Health Records

Session Information

• Augmented Intelligence for Prediction and Image Analysis
  October 24, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Augmented Intelligence, Digital Health, and Data Science

• 300 Augmented Intelligence, Digital Health, and Data Science

Authors

• Zhang, Yue, Penn State College of Medicine, Hershey, Pennsylvania, United States

Yue Zhang, MPH

• Ghahramani, Nasrollah, Penn State College of Medicine, Hershey, Pennsylvania, United States

Nasrollah Ghahramani, MD, FASN

• Chinchilli, Vernon M., Penn State College of Medicine, Hershey, Pennsylvania, United States

Vernon M. Chinchilli, PhD

• Ba, Djibril, Penn State College of Medicine, Hershey, Pennsylvania, United States

Djibril Ba, PhD

Background

The application of machine learning algorithms in predicting the risk of acute kidney injury (AKI) and chronic kidney disease (CKD) has shown promise within local healthcare organizations. However, their performance using national electronic health records (EHR) remains unclear. Moreover, the impact of including COVID-19 infection histories in predictive models is not clear.

Methods

Data were sourced from the TriNetX Research Network, with study cohort period from 2022/07/01 to 2024/03/31. The incidence of AKI or CKD was extracted based on ICD-10 codes after the index date. Covariates were assessed within 1 year before the index dates, including 4 demographics, 33 comorbidities, 18 lab test results, 13 medications, 5 vital signs, 3 visit histories, and COVID-19 infections. Missing values were imputed, and upsampling methods were used to address data imbalances. Extreme gradient boosting was used to build machine learning algorithms for predicting AKI and CKD separately, for each of the two prediction windows.

Results

We included 104,565 participants. The area under the receiver operating characteristic (AUROC) curve was stable across the prediction windows, with the highest score for CKD within 1 month (0.87) and the lowest for AKI within 1 year (0.79) (Table). Counts of inpatient visits and eGFR were the most important variables for predicting AKI and CKD, respectively. The count of COVID-19 infections was the most important variable among baseline conditions (Figure).

Conclusion

Machine learning with national EHR data shows promising performance for AKI and CKD predictions. COVID-19 status should be included in the prediction model.

Performance of Predictive Models