Postoperative Blood Pressure Does Not Affect Lactate Clearance in Cardiac Surgery: A Retrospective Observational Cohort Study
Bibliographic record
Abstract
Background: Tight blood pressure control is a cornerstone of postoperative cardiac surgery patients. In addition, plasma lactate levels are frequently monitored in this setting as it is a marker for malperfusion, with early elevated levels being associated with increased morbidity and mortality. Elevations from malperfusion may be due to decreased cardiac output, hypovolemia, or persistent post bypass vasoplegic response. Here, we investigate whether lower blood pressures, significant changes from baseline, and cardiac perfusion pressures delay the clearance of lactate after cardiac surgery. Methods: This is a retrospective cohort observational study of patients who have undergone coronary artery bypass graft (CABG) and valve replacement or repair surgeries at NYU Langone Long Island Hospital over a 6 month period. Postoperative blood pressures and lactate levels were examined over the first 16 hours of care. The primary outcome was a statistical relationship between specific pressures and fraction of cleared lactate. Intensive Care Unit and hospital length of stay, and mortality were secondary outcomes. Results: A total of 81 patients met inclusion criteria. The average pre-operative mean arterial blood pressure (MAP) was 95.4 mmHg and the average MAP in the first 6 hours post-operatively was 78.4 mmHg. The average change in MAP from baseline was a decrease of 16.7%. The average cleared lactate fraction by 16 hours postoperatively was 85.9%. Lactate clearance was associated in a statistically significant way only with the need for inotropic support on postoperative day 1, p=0.03. There was a slight trend toward a delay in lactate clearance in those with lower early systolic blood pressures, p=0.14. Conclusion: Lactate clearance appears to occur largely independently of postoperative blood pressures in the first 16 hours after surgery but may be delayed in those requiring inotropic support through the morning or postoperative day one.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".