Association between serum lactate levels and mortality in patients with cardiogenic shock receiving mechanical circulatory support: a multicenter retrospective cohort study
Bibliographic record
Abstract
To evaluate the prognostic value of peak serum lactate and lactate clearance at several time points in cardiogenic shock treated with temporary mechanical circulatory support (MCS) using veno-arterial extracorporeal membrane oxygenation (VA-ECMO) or Impella CP®.Serum lactate and clearance were measured before MCS and at 1 h, 6 h, 12 h, and 24 h post-MCS in 43 patients at four tertiary-care centers in Southern Brazil. Prognostic value was assessed by univariable and multivariable analysis and receiver operating characteristic (ROC) curves for 30-day mortality.VA-ECMO was the most common MCS modality (58%). Serum lactate levels at all time points and lactate clearance after 6 h were associated with mortality on unadjusted and adjusted analyses. Lactate levels were higher in non-survivors at 6 h, 12 h, and 24 h after MCS. Serum lactate > 1.55 mmol/L at 24 h was the best single prognostic marker of 30-day mortality [area under the ROC curve = 0.81 (0.67-0.94); positive predictive value = 86%). Failure to improve serum lactate after 24 h was associated with 100% mortality.Serum lactate was an important prognostic biomarker in cardiogenic shock treated with temporary MCS. Serum lactate and lactate clearance at 24 h were the strongest independent predictors of short-term survival.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".