Outcomes of post-cardiac surgery patients with persistent hyperlactatemia in the intensive care unit: a matched cohort study
Bibliographic record
Abstract
BACKGROUND: Higher morbidity and mortality rates are seen amongst patients presenting with hyperlactatemia in the postoperative period. The purpose of this study was to determine the relationship between persistent elevations in lactate and poor ICU outcome in post-cardiac surgery patients. METHODS: This was a retrospective matched cohort analysis of cardiac surgery patients undergoing bypass and/or valve surgery in a university hospital centre. Selection criteria were: cardiac bypass and/or valve surgery; admission to the ICU for > 24 h postoperatively; and peak lactate ≥ 3.0 mmol/L. Hyperlactatemic patients were matched to 2 normolactatemic patients. Multivariable conditional logistic regression was used to determine predictors of hyperlactatemia and mortality. RESULTS: Four hundred sixty-nine post-cardiac surgery patients were admitted to the ICU for > 24 h. 144 of these patients had an arterial blood lactate ≥ 3.0 mmol/L. Amongst the mortalities, 78.9 % presented with hyperlactatemia. Independent risk factors predictive of a lactate ≥ 3.0 mmol/L were preoperative IABP insertion (RR 2.8, CI 1.1-7.2) and postoperative acute kidney injury (RR 3.2, CI 2.1-5.4). Patients whose lactate concentrations continued to increase >30 h postoperatively were more likely to die (RR 8.44 CI 2.50-28.53). CONCLUSIONS: The persistence of hyperlactatemia is a more important determinant of postoperative outcome than the absolute value of the peak lactate concentration. A simple postoperative lactate washout does not sufficiently explain this lactate accumulation. Mortality is proposed to be secondary to a state of ongoing hypoperfusion.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".