Abstract: PUB047
Under Pressure: Using a Machine-Learning Program to Stratify Risk Factors Associated with Incident CKD
Session Information
Category: Artificial Intelligence, Digital Health, and Data Science
- 300 Artificial Intelligence, Digital Health, and Data Science
Authors
- Pollock, James M, Prisma Health Midlands, Columbia, South Carolina, United States
- Brennan, Meghan, Prisma Health Midlands, Columbia, South Carolina, United States
Background
The initial stages of chronic kidney disease (CKD) are often asymptomatic, but targeted screening can help prevent early disease progression and improve outcomes. Neural networks are a machine learning program capable of combing through large databases to attribute statistical importance to multiple risk factors associated with CKD.
Methods
Using data from the CDC’s Kidney Disease Surveillance System and Atlas of Heart Disease and Stroke, 10-year trends in several risk factors were calculated from 2009 to 2019 on a county-wide basis across the United States. The quintile-based trend for each was then input to a neural network tasked with predicting an increase or decrease in the prevalence of CKD over the 10-year window. Statistical importance was calculated to determine which risk factors were most important to control incident CKD.
Results
3,021 counties in the United States were included. 50 iterations of the optimized neural network were generated with each model utilizing a random 30% of input data for training and 70% for performance challenge. Mean area under the curve during these performance challenges was 0.954 (95% CI: 0.946 - 0.963) (Figure 1). Accuracy was similar in predictions of increased or decreased CKD prevalence. Hypertension trends were the most statistically important risk factor in correctly adjudicated forecasting (Figure 2).
Conclusion
These findings underscore the importance of early detection and timely management of hypertension in preventing the onset and progression of CKD.