Dynamic Risk Prediction
Cross-source consensus on Dynamic Risk Prediction from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
Benefits
Evidence quality
Highlighted claims
- Dynamic risk prediction estimated event probability within a future window conditional on survival and biomarker history up to a landmark time. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
- The practical goal was dynamically updated patient-specific risk prediction based on accumulated creatinine history. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
- Prediction performance was evaluated using 4-fold cross-validation at 0.5-year and 2.0-year landmarks across 1-, 2-, and 3-year prediction windows. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
- The model could adjust predicted risk upward or downward as new creatinine values became available. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
- Dynamic prediction performance was generally strong, though censoring required caution. — Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study