Lactate Clearance as a Surrogate for Mortality in Cardiogenic Shock: Insights From the DOREMI Trial
Bibliographic record
Abstract
Background Recent studies have shown improved outcomes in cardiogenic shock through protocols directed toward early identification and initiation of mechanical circulatory support. However, objective therapeutic targets—based on clinical and/or laboratory data—to guide real‐time clinical decision making are lacking. Lactate clearance has been suggested as a potential treatment target because of its independent association with mortality. Methods and Results In a post hoc analysis of the DOREMI (Dobutamine Compared to Milrinone in the Treatment of Cardiogenic Shock) trial—a randomized, double‐blind, controlled trial comparing milrinone to dobutamine in the treatment of cardiogenic shock—we used prospectively collected lactate data to evaluate lactate clearance as a surrogate marker for in‐hospital mortality. In total, 82 (57.7%) patients survived to hospital discharge (survivors). In multivariate logistic regression analysis, complete lactate clearance, percentage lactate clearance, and percentage lactate clearance per hour were independently associated with survival beginning as early as 8 hours after enrollment. Complete lactate clearance was the strongest predictor of survival at all time points, with odds ratios ranging between 2.46 (95% CI, 1.09–5.55; P =0.03) at 8 hours to 5.44 (95% CI, 2.14–13.8; P <0.01) at 24 hours. Conclusions Complete lactate clearance is a strong and independent predictor of in‐hospital survival in patients with cardiogenic shock. Together with previously published data, these results further support the validity of lactate clearance as an appropriate surrogate for mortality and as a potential therapeutic target in future cardiogenic shock trials. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03207165.
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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 | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".