Practical Considerations When Prescribing Icodextrin: A Narrative Review
Bibliographic record
Abstract
BACKGROUND: Icodextrin is a peritoneal dialysis solution that is commonly used to increase ultrafiltration during the long dwell. The other major clinical benefit of icodextrin is that it is glucose-sparing, which may help preserve peritoneal membrane function. Since it has a different chemical composition than dextrose, and with its increasing use, there are several clinical considerations healthcare providers must familiarize themselves with prior to prescribing icodextrin. SUMMARY: Failure to recognize these special properties of icodextrin can lead to adverse events reaching patients. This narrative review explores the hemodynamic, metabolic, and idiopathic effects of icodextrin to facilitate the safe use of icodextrin in peritoneal dialysis. KEY MESSAGES: Hemodynamic effects include hypotension from enhanced ultrafiltration contributing to loss of residual kidney function. Metabolic effects include the chemical structure of icodextrin interfering with biochemical assays, resulting in misleading glucose readings on non-specific glucometers. Idiopathic adverse effects include a diffuse rash and sterile peritonitis. It is also important to remember that not all antibiotic combinations have undergone stability testing in icodextrin. This narrative review will help healthcare providers to confidently prescribe icodextrin to maximize its benefit in peritoneal dialysis patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".