Interventions to prevent hemodynamic instability during renal replacement therapy in critically ill patients: a systematic review
Bibliographic record
Abstract
BACKGROUND: Hemodynamic instability related to renal replacement therapy (HIRRT) may increase the risk of death and limit renal recovery. Studies in end-stage renal disease populations on maintenance hemodialysis suggest that some renal replacement therapy (RRT)-related interventions (e.g., cool dialysate) may reduce the occurrence of HIRRT, but less is known about interventions to prevent HIRRT in critically ill patients receiving RRT for acute kidney injury (AKI). We sought to evaluate the effectiveness of RRT-related interventions for reducing HIRRT in such patients across RRT modalities. METHODS: A systematic review of publications was undertaken using MEDLINE, MEDLINE in Process, EMBASE, and Cochrane's Central Registry for Randomized Controlled Trials (RCTs). Studies that assessed any intervention's effect on HIRRT (the primary outcome) in critically ill patients with AKI were included. HIRRT was variably defined according to each study's definition. Two reviewers independently screened abstracts, identified articles for inclusion, extracted data, and evaluated study quality using validated assessment tools. RESULTS: Five RCTs and four observational studies were included (n = 9; 623 patients in total). Studies were small, and the quality was mostly low. Interventions included dialysate sodium modeling (n = 3), ultrafiltration profiling (n = 2), blood volume (n = 2) and temperature control (n = 3), duration of RRT (n = 1), and slow blood flow rate at initiation (n = 1). Some studies applied more than one strategy simultaneously (n = 5). Interventions shown to reduce HIRRT from three studies (two RCTs and one observational study) included higher dialysate sodium concentration, lower dialysate temperature, variable ultrafiltration rates, or a combination of strategies. Interventions not found to have an effect included blood volume and temperature control, extended duration of intermittent RRT, and slower blood flow rates during continuous RRT initiation. How HIRRT was defined and its frequency of occurrence varied widely across studies, including those involving the same RRT modality. Pooled analysis was not possible due to study heterogeneity. CONCLUSIONS: Small clinical studies suggest that higher dialysate sodium, lower temperature, individualized ultrafiltration rates, or a combination of these strategies may reduce the risk of HIRRT. Overall, for all RRT modalities, there is a paucity of high-quality data regarding interventions to reduce the occurrence of HIRRT in critically ill patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".