Determination of a Limited Sampling Strategy for IV Busulfan Dose Individualization in Children.
Bibliographic record
Abstract
Abstract Busulfan doses are individualized in order to achieve an area under the plasma Busulfan concentration vs time curve (AUC) to ensure engraftment while preventing hepatic venous occlusive disease. We currently calculate AUC using 7 plasma Busulfan samples drawn after the first of 16 Busulfan doses given every 6 hours. The objective of this retrospective study was to evaluate the extent of agreement between the AUC calculated using 7 plasma Busulfan concentrations (AUC-7) and each possible limited sampling strategy (LSS) in children undergoing haematopoietic stem cell transplant. 44 children aged 0.3 to 16 years (median: 3.0 years) received IV Busulfan from April 1, 2003 through January 2006 at our institution. Busulfan doses given and subsequent plasma Busulfan concentrations were recorded for each patient. AUC-7 and AUC using all possible LSSs were calculated using non-compartmental and 1-compartmental methods (WinNonLin version 5.0.1). Based on a target AUC of 1300 micromole·min/L, a theoretical adjusted Busulfan dose was calculated using AUC-7 and all LSSs using 2 and 3 plasma Busulfan concentrations. Extent of agreement betweenthe AUC-7 values and the LSS AUC values andthe theoretical adjusted Busulfan dose based on the AUC-7 values and the 2 and 3 point LSS AUC values was assessed by determination of the intraclass correlation coefficient (ICC; parallel, one-way random effect model) and Bland-Altman (BA) analysis. Agreement was observed between AUC-7 calculated using non-compartmental and 1-compartmental methods and LSSs using 2 and 3 plasma Busulfan concentrations. AUC values calculated using 6 LSSs each consisting of 3 plasma Busulfan concentrations achieved ICC values > 0.96 (p<0.001) and were within the BA limits of agreement. Agreement between the theoretical Busulfan adjusted doses (7-point vs 3-point strategies) was very strong when the Busulfan concentrations obtained at the following time points were used: end of the 2 hour infusion, 3, and 4 hours after completion of the infusion; or 0.25, 3, and 4 hours after completion of the infusion. We conclude that there is excellent agreement between AUC-7 and a LSS for calculation of Busulfan AUC using 3 time points andbetween theoretical adjusted Busulfan doses calculated using AUC-7 and using these strategies. The LSSs described here must be validated in a prospective study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".