Epidemiology and Prognostic Significance of Acute Noncardiac Organ Dysfunction Across Cardiogenic Shock Subtypes
Bibliographic record
Abstract
BACKGROUND: The epidemiology and prognostic significance of acute noncardiac organ dysfunction across cardiogenic shock (CS) subtypes are not well-defined. METHODS: CS admissions from 2017 to 2022 in the Critical Care Cardiology Trials Network Registry were classified as acute myocardial infarction-related CS (AMI-CS), acute-on-chronic heart failure-related CS (AoC HF-CS), or de novo HF-CS, and categorized as having at least moderate respiratory, kidney, liver, and/or neurological dysfunction using established criteria. Burden of organ dysfunction was defined as no noncardiac organ dysfunction (NOD), single organ dysfunction, or multiorgan dysfunction (≥2) (MOD). Multivariable models were used to evaluate associations of burden and type of noncardiac organ dysfunction with in-hospital death. RESULTS: Among 3904 CS admissions, 29.4% had AMI-CS, 50.9% had AoC HF-CS, and 19.7% had de novo HF-CS. AMI-CS and de novo HF-CS had greater prevalence of MOD (35.0% and 33.9%, respectively) compared with AoC HF-CS (23.1%; P < .01). In-hospital mortality was higher with a greater burden of organ dysfunction in the overall CS cohort (single organ dysfunction vs NOD, adjusted odds ratio [aOR] for in-hospital death 2.5, 95% confidence interval [CI] 2.0-3.2; MOD vs NOD: aOR 6.5, 95% CI 5.1-8.2) and across each CS subtype. Kidney dysfunction was the most prognostically important form of organ dysfunction in the overall cohort (aOR 4.1, 95% CI 3.4-5.0) and for each CS subtype. CONCLUSIONS: Admissions for AoC HF-CS had a lower burden of acute noncardiac organ dysfunction compared with admissions for de novo HF-CS and AMI-CS. However, acute noncardiac organ dysfunction burden was similarly adversely prognostic across all CS subtypes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".