When Should Renal Replacement Therapy for Acute Kidney Injury Be Initiated and Discontinued?
Bibliographic record
Abstract
BACKGROUND: Critically ill patients with acute kidney injury (AKI) are at high risk for death and frequently require initiation of renal replacement therapy (RRT). There is wide variation in clinical practice on the indications for and timing of initiation and discontinuation of RRT. Numerous clinical and biochemical factors (i.e. uremic, metabolic, fluid balance) have been used; however, at present there is no consensus to guide clinicians on the most favorable time to initiate and/or discontinue RRT to optimize patient outcomes. METHODS: In this review, we appraise the available clinical studies that have assessed timing of initiation and/or discontinuation of RRT for critically ill patients with AKI. 'Timing' of initiation has been variably defined including use of conventional biomarkers (i.e. serum urea and creatinine), urine output, fluid balance, and time relative to intensive care unit admission. CONCLUSIONS: Numerous studies consistently point toward a survival benefit to early initiation of RRT; however, there is a paucity of high-quality randomized trials. If early RRT is associated with clinical benefit, it remains uncertain whether this is attributable to more rapid metabolic/uremic control, management of fluid balance or a combination of clinical factors. In addition, timing of RRT initiation is likely context-specific and varies by clinical factors and/or etiology of AKI. There is also little data to accurately distinguish in advance between the injured kidney that will need extracorporeal renal support and one that retains capacity for early recovery. Fewer studies have evaluated the process of weaning of RRT or ideal methods to predict sufficient recovery to avoid re-initiation. Longer duration of RRT support, higher illness severity and lower urine output (independent of diuretic therapy) have all predicted need for re-initiation. Additional investigations on these issues are clearly warranted and urgently 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".