Measurement of total hemoglobin reduces red cell transfusion in hospitalized patients undergoing cardiac surgery: a retrospective database analysis
Bibliographic record
Abstract
INTRODUCTION: Historically, perioperative hemoglobin monitoring has relied on calculated saturation, using blood gas devices that measure plasma hematocrit (Hct). Co-oximetry, which measures total hemoglobin (tHb), yields a more comprehensive assessment of hemodilution. The purpose of this study was to examine the association of tHb measurement by co-oximetry and Hct, using conductivity with red blood cell (RBC) transfusion, length of stay (LOS) and inpatient costs in patients having major cardiac surgery. METHODS: A retrospective study was conducted on patients who underwent coronary artery bypass graft (CABG) and/or valve replacement (VR) procedures from January 2014 to June 2016, using MedAssets discharge data. The patient population was sub-divided by the measurement modality (tHb and Hct), using detailed billing records and Current Procedural Terminology coding. Cost was calculated using hospital-specific cost-to-charge ratios. Multivariable logistic regression was performed to identify significant drivers of RBC transfusion and resource utilization. RESULTS: The study population included 18,169 cardiovascular surgery patients. Hct-monitored patients accounted for 66% of the population and were more likely to have dual CABG and VR procedures (10.4% vs 8.9%, p=0.0069). After controlling for patient and hospital characteristics, as well as patient comorbidities, Hct-monitored patients had significantly higher RBC transfusion risk (OR=1.26, CI 1.15-1.38, p<0.0001), longer LOS (IRR=1.08, p<0.0001) and higher costs (IRR=1.15, p<0.0001) than tHb-monitored patients. RBC transfusions were a significant driver of LOS (IRR=1.25, p<0.0001) and cost (IRR=1.22, p<0.0001). CONCLUSIONS: tHb monitoring during cardiovascular surgery could offer a significant reduction in RBC transfusion, length of stay and hospital cost compared to Hct monitoring.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".