Proceedings of the 2022 UAB CRRT Academy: Non-Invasive Hemodynamic Monitoring to Guide Fluid Removal with CRRT and Proliferation of Extracorporeal Blood Purification Devices
Bibliographic record
Abstract
In 2022, we celebrated the 15th anniversary of the University of Alabama at Birmingham (UAB) Continuous Renal Replacement Therapy (CRRT) Academy, a 2-day conference attended yearly by an international audience of over 100 nephrology, critical care, and multidisciplinary trainees and practitioners. This year, we introduce the proceedings of the UAB CRRT Academy, a yearly review of select emerging topics in the field of critical care nephrology that feature prominently in the conference. First, we review the rapidly evolving field of non-invasive hemodynamic monitoring and its potential to guide fluid removal by renal replacement therapy (RRT). We begin by summarizing the accumulating data associating fluid overload with harm in critical illness and the potential for harm from end-organ hypoperfusion caused by excessive fluid removal with RRT, underscoring the importance of accurate, dynamic assessment of volume status. We describe four applications of point-of-care ultrasound used to identify patients in need of urgent fluid removal or likely to tolerate fluid removal: lung ultrasound, inferior vena cava ultrasound, venous excess ultrasonography, and Doppler of the left ventricular outflow track to estimate stroke volume. We briefly introduce other minimally invasive hemodynamic monitoring technologies before concluding that additional prospective data are urgently needed to adapt these technologies to the specific task of fluid removal by RRT and to learn how best to integrate them into practical fluid-management strategies. Second, we focus on the growth of novel extracorporeal blood purification devices, starting with brief reviews of the inflammatory underpinnings of multiorgan dysfunction and the specific applications of pathogen, endotoxin, and/or cytokine removal and immunomodulation. Finally, we review a series of specific adsorptive technologies, several of which have seen substantial clinical use during the COVID-19 pandemic, describing their mechanisms of target removal, the limited existing data supporting their efficacy, ongoing and future studies, and the need for additional prospective trials.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".