Laboratory Markers of Acidosis and Mortality in Cardiogenic Shock: Developing a Definition of Hemometabolic Shock
Bibliographic record
Abstract
BACKGROUND: Acidosis and higher lactate predict worse outcomes in cardiogenic shock (CS) patients. We sought to determine whether overall acidosis severity on admission predicted in-hospital mortality in CS patients. METHODS: This retrospective descriptive analysis included CS patients admitted to a single academic tertiary cardiac intensive care unit from 2007 to 2015. Admission arterial pH, base excess, and anion gap values were used to generate a Composite Acidosis Score (range 0-5, with a score ≥2 defining Severe Acidosis). Adjusted in-hospital mortality was analyzed using multivariable logistic regression. RESULTS: We included 1,065 patients with median age of 68.9 (59.0, 77.2) years (36.4% females). Concomitant diagnoses included cardiac arrest in 38.1% and acute coronary syndrome in 59.1%. Severe Acidosis was present in 35.2%, and these patients had worse shock and more organ failure. In-hospital mortality occurred in 34.1% and was higher among patients with Severe Acidosis (54.9% vs. 22.4%, adjusted odds ratio [OR] 2.01, 95% CI 1.43-2.83, P < 0.001). Increasing Composite Acidosis Score was associated with higher in-hospital mortality (adjusted OR 1.25 per point, 95% CI 1.11-1.40, P < 0.001). Severe Acidosis was associated with higher hospital mortality at every level of shock severity and organ failure (all P < 0.05). Admission lactate level had equivalent discrimination for in-hospital mortality as the Composite Acidosis Score (0.69 vs. 0.66; P = 0.32 by De Long test). CONCLUSION: Given its incremental association with higher in-hospital mortality among CS patients beyond shock severity and organ failure, we propose Severe Acidosis as a marker of hemometabolic shock. Lactate levels performed as well as a composite measure of acidosis for predicting mortality.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".