Modes of Death in Patients with Cardiogenic Shock in the Cardiac Intensive Care Unit: A Report from the Critical Care Cardiology Trials Network
Bibliographic record
Abstract
BACKGROUND: There are limited data on how patients with cardiogenic shock (CS) die. METHODS: The Critical Care Cardiology Trials Network is a research network of cardiac intensive care units coordinated by the Thrombolysis In Myocardial Infarction (TIMI) Study Group (Boston, MA). Using standardized definitions, site investigators classified direct modes of in-hospital death for CS admissions (October 2021 to September 2022). Mutually exclusive categories included 4 modes of cardiovascular death and 4 modes of noncardiovascular death. Subgroups defined by CS type, preceding cardiac arrest (CA), use of temporary mechanical circulatory support (tMCS), and transition to comfort measures were evaluated. RESULTS: Among 1068 CS cases, 337 (31.6%) died during the index hospitalization. Overall, the mode of death was cardiovascular in 82.2%. Persistent CS was the dominant specific mode of death (66.5%), followed by arrhythmia (12.8%), anoxic brain injury (6.2%), and respiratory failure (4.5%). Patients with preceding CA were more likely to die from anoxic brain injury (17.1% vs 0.9%; P < .001) or arrhythmia (21.6% vs 8.4%; P < .001). Patients managed with tMCS were more likely to die from persistent shock (P < .01), both cardiogenic (73.5% vs 62.0%) and noncardiogenic (6.1% vs 2.9%). CONCLUSIONS: Most deaths in CS are related to direct cardiovascular causes, particularly persistent CS. However, there is important heterogeneity across subgroups defined by preceding CA and the use of tMCS.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".