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Record W4389096383 · doi:10.24908/pocus.v8i2.16792

Venous Excess Ultrasound (VExUS) Grading to Assess Perioperative Fluid Status for Noncardiac Surgeries: a Prospective Observational Pilot Study

2023· article· en· W4389096383 on OpenAlexvenueno aff
Justin Magin, Jacob R Wrobel, Xinming An, J. D. Acton, Alexander S. Doyal, Shawn Jia, James C. Krakowski, Jay Schoenherr, Ricardo Serrano, David Flynn, Duncan McLean, Stuart A. Grant

Bibliographic record

VenuePOCUS Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsMedicinePacuPerioperativeGrading (engineering)Perioperative medicineObservational studyIntensive care unitAnesthesiaSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Objectives: Perioperative fluid administration impacts the rate of complications following surgery. VExUS grading system is a standardized point of care ultrasound (POCUS)-based, comprehensive method to assess volume status. VExUS could serve as a tool to guide fluid management, if validated perioperatively. The primary aim was to assess the success rate of obtaining required windows for VExUS grading , as well as the feasibility within a perioperative setting among noncardiac surgery. Further, this study describes the incidence of perioperative venous congestion and associations with 30-day postoperative complications. Methods: This observational study was conducted in non-critically ill adults undergoing noncardiac surgery. Patients were scanned preoperatively, in the post anesthesia care unit (PACU), and 24 hours postoperatively for venous congestion. Researchers retrospectively captured 30-day complications for multivariate analyses. Results: The cohort included 69 participants. Ninety-one percent of scans over all timepoints were successfully completed. Pre-operatively, 57 (83%) scans were Grade 0, and 11 (16%) were Grade 1. Venous congestion was observed in 29 (44%) patients in the PACU (n=66). 22 (33%) patients were Grade 1, while 7 (11%) were Grade 2. At 24 hours (n=63), 31 patients (49%) had venous congestion: 20 (32%) Grade 1 and 11 (17%) Grade 2. Of the pre-operative Grade 0, 28 (50%) had at least one postoperative scan with venous congestion. No patients were Grade 3 at any timepoint. The 30-day complication rate was 32% (n=22). Eleven (16%) patients developed acute kidney injury (AKI). There was no statistically significant association between VExUS grading and all-cause complications or AKI. Conclusion: This study demonstrates that perioperative VExUS scoring is a feasible tool among a variety of noncardiac surgeries. We highlight that venous congestion is common and increases postoperatively within non-ICU populations. Larger studies are needed to assess the relationship between VExUS grading and postoperative complications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.167
GPT teacher head0.395
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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