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Predicting potassium trajectories for risk monitoring in outpatients with heart failure, diabetes mellitus or chronic kidney disease

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Analysis shows potassium prediction in patients with heart failure, CKD, and diabetes, suggesting improved monitoring.

Predicted potassium levels were accurately assessed in outpatients, achieving a RMSE of 0.52.
Data from 5918 patients with a mean age of 76.2 years were used, providing robust predictive performance.
The model utilized administrative claims data and linear mixed-effect models for effective prediction.
Findings imply that dynamic updates to predictions can enhance monitoring of potassium over time.

Scherkl et al. (2025) studied this question.

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View Full Paper

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Authors

Analysis shows potassium prediction in patients with heart failure, CKD, and diabetes, suggesting improved monitoring.

Predicted potassium levels were accurately assessed in outpatients, achieving a RMSE of 0.52.
Data from 5918 patients with a mean age of 76.2 years were used, providing robust predictive performance.
The model utilized administrative claims data and linear mixed-effect models for effective prediction.
Findings imply that dynamic updates to predictions can enhance monitoring of potassium over time.

Scherkl et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark