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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".