Mechanisms for hemodynamic instability related to renal replacement therapy: a narrative review
Bibliographic record
Abstract
Hemodynamic instability related to renal replacement therapy (HIRRT) is a frequent complication of all renal replacement therapy (RRT) modalities commonly used in the intensive care unit. HIRRT is associated with increased mortality and may impair kidney recovery. Our current understanding of the physiologic basis for HIRRT comes primarily from studies of end-stage kidney disease patients on maintenance hemodialysis in whom HIRRT is referred to as 'intradialytic hypotension'. Nonetheless, there are many studies that provide additional insights into the underlying mechanisms for HIRRT specifically in critically ill patients. In particular, recent evidence challenges the notion that HIRRT is almost entirely related to excessive ultrafiltration. Although excessive ultrafiltration is a key mechanism, multiple other RRT-related mechanisms may precipitate HIRRT and this could have implications for how HIRRT should be managed (e.g., the appropriate response might not always be to reduce ultrafiltration, particularly in the context of significant fluid overload). This review briefly summarizes the incidence and adverse effects of HIRRT and reviews what is currently known regarding the mechanisms underpinning it. This includes consideration of the evidence that exists for various RRT-related interventions to prevent or limit HIRRT. An enhanced understanding of the mechanisms that underlie HIRRT, beyond just excessive ultrafiltration, may lead to more effective RRT-related interventions to mitigate its occurrence and consequences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".