Epidemiology of cardiogenic shock using the Shock Academic Research Consortium (SHARC) consensus definitions
Bibliographic record
Abstract
AIMS: The Shock Academic Research Consortium (SHARC) recently proposed pragmatic consensus definitions to standardize classification of cardiogenic shock (CS) in registries and clinical trials. We aimed to describe contemporary CS epidemiology using the SHARC definitions in a cardiac intensive care unit (CICU) population. METHODS AND RESULTS: The Critical Care Cardiology Trials Network (CCCTN) is a multinational research network of advanced CICUs coordinated by the TIMI Study Group (Boston, MA). Cardiogenic shock was defined as a cardiac disorder resulting in SBP < 90 mmHg for ≥30 min [or the need for vasopressors, inotropes, or mechanical circulatory support (MCS) to maintain SBP ≥ 90 mmHg] with evidence of hypoperfusion. Primary aetiologic categories included acute myocardial infarction-related CS (AMI-CS), heart failure-related CS (HF-CS), and non-myocardial (secondary) CS. Post-cardiotomy CS was not included. Heart failure-related CS was further subcategorized as de novo vs. acute-on-chronic HF-CS. Patients with both cardiogenic and non-cardiogenic components of shock were classified separately as mixed CS. Of 8974 patients meeting shock criteria (2017-23), 65% had isolated CS and 17% had mixed shock. Among patients with CS (n = 5869), 27% had AMI-CS (65% STEMI), 59% HF-CS (72% acute-on-chronic, 28% de novo), and 14% secondary CS. Patients with AMI-CS and de novo HF-CS were most likely to have had concomitant cardiac arrest (P < 0.001). Patients with AMI-CS and mixed CS were most likely to present in more severe shock stages (SCAI D or E; P < 0.001). Temporary MCS use was highest in AMI-CS (59%). In-hospital mortality was highest in mixed CS (48%), followed by AMI-CS (41%), similar in de novo HF-CS (31%) and secondary CS (31%), and lowest in acute-on-chronic HF-CS (25%; P < 0.001). CONCLUSION: SHARC consensus definitions for CS classification can be pragmatically applied in contemporary registries and reveal discrete subpopulations of CS with distinct phenotypes and outcomes that may be relevant to clinical practice and future research.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".