Dynamic Measures of Fluid Responsiveness to Guide Resuscitation in Patients With Sepsis and Septic Shock: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of using dynamic measures of fluid responsiveness in guiding the resuscitation of adult patients with sepsis and septic shock. DATA SOURCE: We searched MEDLINE, Embase, and unpublished sources from inception to February 3, 2025. STUDY SELECTION: We included randomized controlled trials (RCTs) that evaluated the use of dynamic measures of fluid responsiveness to guide resuscitation compared with any other method in patients with sepsis and septic shock. DATA EXTRACTION: We collected data regarding study and patient characteristics, definitions of fluid responsiveness, modality for assessing fluid responsiveness, and outcome data. We performed a random-effects meta-analysis and rated the certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework. DATA SYNTHESIS: We included nine eligible RCTs (n = 698 patients). The use of dynamic measures of fluid responsiveness to guide IV fluid (IVF) administration of patients with septic shock probably reduces 28-day mortality (relative risk] 0.61; 95% CI, 0.42-0.90, moderate certainty), may reduce the risk of acute kidney injury (AKI) (RR 0.66; 95% CI, 0.44-0.98, low certainty), and cumulative fluid balance on day 3 (mean difference -1.57L; 95% CI, -2.44 L to -0.69 L, low certainty). The use of dynamic measures of fluid responsiveness has an uncertain effect on ICU mortality, ICU and hospital length of stay, need for and duration of mechanical ventilation, need for renal replacement therapy, vasoactive medication administration, duration of vasopressor use, and IVF administration on day 1. CONCLUSIONS: In adult patients with sepsis and septic shock, using dynamic measures of fluid responsiveness may improve survival and reduce the risk of AKI. Future studies should evaluate the impact of this intervention on other important clinical outcomes and determine the comparative efficacy of specific modalities for assessing fluid responsiveness.
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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 | 0.024 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".