Hemoglobin and Cholesterol Affect Apparent Tacrolimus Clearance in Pediatric Transplant Recipients – a Retrospective Cohort Study
Bibliographic record
Abstract
Introduction: Tacrolimus has a narrow therapeutic index with substantial inter- and intra-patient variability. Factors beyond genetic and developmental factors are poorly understood. Recent adult studies suggest that hemoglobin affects the apparent clearance (CL/F), whereas this and other potential factors in children are understudied. Methods: After ethics approval, we performed a single center retrospective cohort study of pediatric renal transplant recipients, who were followed between January 1st, 2004, and June 30th, 2018. Patients without tacrolimus therapy or concomitant sirolimus were excluded. The aim was to show the impact of hemoglobin, albumin, cholesterol and HDL on the apparent tacrolimus clearance (CL/F = Dose/AUC). Data were collected from electronic health record. We used 12-point pharmacokinetic (PK) profiles. Results: Thirty-three patients were included. Median age at transplantation was 10 years, 52% were female, the median tacrolimus area under the curve (AUC) was 133 ng*h/mL. CL/F mainly correlated with hemoglobin (n=1,257, r=-0.3767, p<0.0001), HDL-cholesterol (n=236, r=-0.3973, p<0.0001) and total cholesterol (n=373, r=-0.1821, p=0.0004). Conclusion: The present study suggests a moderate impact of the biochemical factors studied in the tacrolimus CL/F. Lower hemoglobin seems to increase it, while higher cholesterol decreases it. Physicians should be aware of this association during the TDM follow up.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".