Intravenous Albumin for Mitigating Hypotension and Augmenting Ultrafiltration during Kidney Replacement Therapy
Bibliographic record
Abstract
Among its many functions, owing to its oversized effect on colloid oncotic pressure, intravascular albumin helps preserve the effective circulatory volume. Hypoalbuminemia is common in hospitalized patients and is found especially frequently in patients who require KRT either for AKI or as maintenance hemodialysis. In such patients, hypoalbuminemia is strongly associated with morbidity, intradialytic hypotension, and mortality. Intravenous albumin may be administered in an effort to prevent or treat hypotension or to augment fluid removal, but this practice is controversial. Theoretically, intravenous albumin administration might prevent or treat hypotension by promoting plasma refilling in response to ultrafiltration. However, clinical trials have demonstrated that albumin administration is not nearly as effective a volume expander as might be assumed according to its oncotic properties. Although intravenous albumin is generally considered to be safe, it is also very expensive. In addition, there are potential risks to using it to prevent or treat intradialytic hypotension. Some recent studies have suggested that hyperoncotic albumin solutions may precipitate or worsen AKI in patients with sepsis or shock; however, the overall evidence supporting this effect is weak. In this review, we explore the theoretical benefits and risks of using intravenous albumin to mitigate intradialytic hypotension and/or enhance ultrafiltration and summarize the current evidence relating to this practice. This includes studies relevant to its use in patients on maintenance hemodialysis and critically ill patients with AKI who require KRT in the intensive care unit. Despite evidence of its frequent use and high costs, at present, there are minimal data that support the routine use of intravenous albumin during KRT. As such, adequately powered trials to evaluate the efficacy of intravenous albumin in this setting are clearly needed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".