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Record W3186243149 · doi:10.1097/aln.0000000000003663

Noninvasive Urine Oxygen Monitoring and the Risk of Acute Kidney Injury in Cardiac Surgery

2021· article· en· W3186243149 on OpenAlexaff
Natalie A. Silverton, Lars Lofgren, Isaac E. Hall, Gregory J. Stoddard, Natalia P. Melendez, Michael Van Tienderen, Spencer Shumway, Bradley J. Stringer, Woon-Seok Kang, Carter Lybbert, Kai Kück

Bibliographic record

VenueAnesthesiology · 2021
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsUniversity of Lethbridge
FundersNational Center for Advancing Translational SciencesDivision of Cardiothoracic Surgery, University of Colorado School of Medicine, Anschutz Medical CampusNational Center for Research ResourcesUniversity of Utah
KeywordsMedicineAcute kidney injuryUrinary systemAnesthesiaCardiac surgeryCardiopulmonary bypassSurgeryUrologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Acute kidney injury (AKI) is a common complication of cardiac surgery. An intraoperative monitor of kidney perfusion is needed to identify patients at risk for AKI. The authors created a noninvasive urinary oximeter that provides continuous measurements of urinary oxygen partial pressure and instantaneous urine flow. They hypothesized that intraoperative urinary oxygen partial pressure measurements are feasible with this prototype device and that low urinary oxygen partial pressure during cardiac surgery is associated with the subsequent development of AKI. METHODS: This was a prospective observational pilot study. Continuous urinary oxygen partial pressure and instantaneous urine flow were measured in 91 patients undergoing cardiac surgery using a novel device placed between the urinary catheter and collecting bag. Data were collected throughout the surgery and for 24 h postoperatively. Clinicians were blinded to the intraoperative urinary oxygen partial pressure and instantaneous flow data. Patients were then followed postoperatively, and the incidence of AKI was compared to urinary oxygen partial pressure measurements. RESULTS: Intraoperative urinary oxygen partial pressure measurements were feasible in 86/91 (95%) of patients. When urinary oxygen partial pressure data were filtered for valid urine flows greater than 0.5 ml · kg-1 · h-1, then 70/86 (81%) and 77/86 (90%) of patients in the cardiopulmonary bypass (CPB) and post-CPB periods, respectively, were included in the analysis. Mean urinary oxygen partial pressure in the post-CPB period was significantly lower in patients who subsequently developed AKI than in those who did not (mean difference, 6 mmHg; 95% CI, 0 to 11; P = 0.038). In a multivariable analysis, mean urinary oxygen partial pressure during the post-CPB period remained an independent risk factor for AKI (relative risk, 0.82; 95% CI, 0.71 to 0.95; P = 0.009 for every 10-mmHg increase in mean urinary oxygen partial pressure). CONCLUSIONS: Low urinary oxygen partial pressures after CPB may be associated with the subsequent development of AKI after cardiac surgery.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations59
Published2021
Admission routes1
Has abstractyes

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