Kidney perfusion in critical illness: between the macrocirculation and the microcirculation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Acute kidney injury (AKI) remains a major challenge in critical care, with high morbidity and mortality. This review aims to highlight existing and upcoming tools to integrate macrocirculatory and microcirculatory perspectives to better understand and prevent AKI. RECENT FINDINGS: Hemodynamic optimization after initial resuscitation is currently based on central hemodynamic measurement of arterial/venous pressure and cardiac output, although they do not correlate well with kidney hemodynamics. Bedside ultrasound techniques, particularly contrast-enhanced ultrasound (CEUS), have uncovered impaired renal perfusion even when systemic flow appears adequate. Emerging high-frame-rate and super-resolution ultrasound methods promise to visualize renal microvessels at micrometer scales enabling true assessment of the microcirculation. Furthermore, urinary partial oxygen pressure monitoring provides continuous insight into medullary hypoxia. These diagnostics can be combined with biological phenotyping to define treatable AKI sub-phenotypes. SUMMARY: The integration of multimodal hemodynamic monitoring holds promise for identifying actionable AKI sub-phenotypes and guiding precision therapies. Future clinical trials should incorporate mechanistic endpoints from both the macrocirculatory and microcirculatory domains to improve our understanding of treatment effects, optimize trial design, and ultimately enhance patient outcomes in this high-risk population.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".