Impact of sampling time deviations on the prediction of the area under the curve using regression limited sampling...

The regression limited sampling strategy approach (R-LSS), which is based on a small number of blood samples drawn at selected time points, has been used as an alternative method for the estimation of the area under the concentration-time curve (AUC). However, deviations from planned sampling times may affect the performance of R-LSS, influencing related therapeutic decisions and outcomes. The aim of this study was to investigate the impact of different sampling time deviation (STD) scenarios on the estimation of AUC by the R-LSS using a simulation approach. Three types of scenarios were considered going from the simplest case of fixed deviations, to random deviations and then to a more realistic case where deviations of mixed nature can occur. In addition, the sensitivity of the R-LSS to STD in each involved sampling point was evaluated. A significant impact of STD on the performance of R-LSS was demonstrated. The tolerance of R-LSS to STD was found to depend not only on the number of sampling points but more importantly on the duration of the sampling process. Sensitivity analysis showed that sampling points at which rapid concentration changes occur were relatively more critical for AUC prediction by R-LSS. As a practical approach, nomograms were proposed, where the expected predictive performance of R-LSS was provided as a function of STD information. The investigation of STD impact on the predictive performance of R-LSS is a critical element and should be routinely performed to guide R-LSS selection and use.