Hughes [Buelga] et al. 2021
A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors
CPT: Pharmacometrics & Systems Pharmacology · 10.1002/psp4.12684
Structure
A one-compartment model, driven by weight and creatinine clearance.
Study population
General patients — United States and North America. Fitted to 2,700 patients and 5,153 concentration observations.
| Covariate | As reported |
|---|---|
| Age | 64.2 (IQR 31.2–87.5) yr |
| Weight | 84.2 (IQR 52–153.4) kg |
| BMI | 28.1 (IQR 19–47) kg/m² |
| SCr | 0.9 (IQR 0.5–2.6) mg/dL |
Reported as the source publication gives them — mean ± SD, or median with range or interquartile range.
Equations
The model's structural parameters as Chorus implements them.
LaTeX source
CL = 0.713 \cdot CrCl \cdot \frac{60}{1000}
V_1 = 1.12 \cdot Weight
Implementation notes
Re-estimated version of the Buelga 2005 model using IDMS-standardized serum creatinine.