Normal-Range Blood Lactate Concentration in Septic Shock Is Prognostic and Predictive
Bibliographic record
Abstract
We hypothesized that lactate levels even within the normal range are prognostic and that low lactate levels predict a beneficial response to vasopressin infusion in septic shock. We conducted a retrospective analysis using the Vasopressin in Septic Shock Trial (VASST) as a derivation cohort (n = 665), then validated using another single-center septic shock cohort, St Paul's Hospital (SPH) cohort (n = 469). Lactate levels were divided into quartiles. The primary outcome variable was 28-day mortality in both cohorts. We used receiver operating characteristic (ROC) curve analysis to compare the prognostic value of lactate concentrations versus Acute Physiology and Chronic Health Evaluation II scores. We then explored whether lactate concentrations might predict beneficial response to vasopressin compared with noradrenaline in VASST. Normal lactate range is less than 2.3 mmol/L. At enrollment, patients in the second quartile (1.4 < lactate < 2.3 mmol/L) had significantly increased mortality and organ dysfunction compared with patients who had lactate ≤ 1.4 mmol/L (quartile 1) (P < 0.0001). Quartile 2 outcomes were as severe as quartile 3 (2.3 ≤ lactate < 4.4 mmol/L) outcomes. Baseline lactate values (ar ea under the ROC curve = 0.63, 0.66; VASST, SPH) were as good as Acute Physiology and Chronic Health Evaluation II scores (area under the ROC curve = 0.66, 0.73; VASST, SPH) as prognostic indicators of 28-day mortality. Lactate concentrations of 1.4 mmol/L or less predicted a beneficial response in those randomized to vasopressin compared with noradrenaline in VASST (P < 0.05). Lactate concentrations within the "normal" range can be a useful prognostic indicator in septic shock. Furthermore, patients whose lactate level is less than or equal to 1.4 mmol/L may benefit from vasopressin infusion.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".