A rolling-horizon pharmacokinetic pharmacodynamic model for warfarin inpatients in transient clinical states

Abstract

Aim: To design a pharmacokinetic pharmacodynamic model to make individualized and adaptive international normalized ratio (INR) predictions for warfarin inpatients in changing clinical status. Methods: We tested a new model on 60 inpatients at Columbia University. The model personalizes four submodels and minimizes the number of parameters to be estimated. Prediction accuracy was assessed by prediction error, absolute prediction error and percentage absolute prediction error. Results: The INRs were accurately predicted 5 days into the future. Median prediction error: 0.01–0.12; median absolute prediction error: 0.17–0.5 and median percentage absolute prediction error: 9.85–26.06%. Conclusion: Patients exhibit interindividual and intertemporal variability. The model captures the variability and provides accurate and personalized INR predictions.