Influence of Weight and Body Size on the Pharmacokinetics of Heart Failure Pharmacotherapy: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To conduct a review of studies evaluating the influence of body size and weight (WT) on the pharmacokinetics (PK) of drugs recommended for heart failure (HF) treatment. DATA SOURCES: A systematic search of the MEDLINE (1946 to April 2023) and EMBASE (1974 to April 2023) databases was conducted for articles that focused on the impact of WT or body size on the PK of drugs of interest used in HF patients. STUDY SELECTION AND DATA EXTRACTION: Articles written in English or French related to the aim of our study were retained for analysis. DATA SYNTHESIS: Of 6493 articles, 20 were retained for analysis. Weight was associated with the clearance of digoxin, carvedilol, enalapril, and candesartan as well as the volume of distribution of eplerenone and bisoprolol. There was no documented direct impact of WT on the PK of furosemide, valsartan, and metoprolol, although these studies were limited or confounded by the small sample size, adjustment of PK factors by WT, or the use of the Cockroff-Gault equation for the evaluation of creatinine clearance, which includes WT. RELEVANCE TO PATIENT CARE AND CLINICAL PRACTICE: This review highlights and summarizes the available data on the importance of WT on the PK of HF treatment. CONCLUSION: Considering the significant impact of WT on most HF drugs in this review, it may be important to further investigate it in the context of personalized therapy, particularly in patients presenting extreme WTs.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".