Kidney-ventilator interaction and kidney-protective ventilation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Invasive mechanical ventilation (IMV) is a cornerstone in the management of acute respiratory failure (ARF) and acute respiratory distress syndrome (ARDS); however, positive pressure ventilation (PPV) and injurious IMV can contribute to renal dysfunction. This review aims to summarize current evidence on kidney-ventilator interactions and explore strategies for kidney-protective ventilation. RECENT FINDINGS: The relationship between ARF/ARDS and acute kidney injury (AKI) is a major contributor to morbidity, mortality, and adverse outcomes among critically ill patients. PPV can induce hemodynamic and neurohormonal changes that may impair kidney function. Additionally, injurious IMV can exacerbate these effects and promote biotrauma, triggering inflammatory responses that further compromise kidney function. Conversely, AKI can exert both inflammatory and non-inflammatory effects, impairing pulmonary function. Lung-protective ventilation (LPV) using low tidal volume and conservative fluid management are strategies that may mitigate AKI. Extracorporeal organ support, including renal replacement therapy and extracorporeal membrane oxygenation, may facilitate LPV and be associated with improved outcomes in patients with IMV-associated AKI. SUMMARY: IMV influences lung-kidney interactions in a bidirectional manner. Evidence suggests the use of LPV, and extracorporeal organ support may mitigate dual organ injury. A thorough understanding of this interplay is essential to optimizing outcomes in critically ill patients receiving IMV.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".