Sex Differences in Characteristics, Resource Utilization, and Outcomes of Cardiogenic Shock: Data From the Critical Care Cardiology Trials Network (CCCTN) Registry
Bibliographic record
Abstract
BACKGROUND: Sex disparities exist in the management and outcomes of various cardiovascular diseases. However, little is known about sex differences in cardiogenic shock (CS). We sought to assess sex-related differences in the characteristics, resource utilization, and outcomes of patients with CS. METHODS: The Critical Care Cardiology Trials Network is a multicenter registry of advanced cardiac intensive care units (CICUs) in North America. Between 2018 and 2022, each center (N=35) contributed annual 2-month snapshots of consecutive CICU admissions. Patients with CS were stratified as either CS after acute myocardial infarction or heart failure–related CS (HF-CS). Multivariable logistic regression was used for analyses. RESULTS: Of the 22 869 admissions in the overall population, 4505 (20%) had CS. Among 3923 patients with CS due to ventricular failure (32% female), 1235 (31%) had CS after acute myocardial infarction and 2688 (69%) had HF-CS. Median sequential organ failure assessment scores did not differ by sex. Women with HF-CS had shorter CICU lengths of stay (4.5 versus 5.4 days; P <0.0001) and shorter overall lengths of hospital stay (10.9 versus 12.8 days; P <0.0001) than men. Women with HF-CS were less likely to receive pulmonary artery catheters (50% versus 55%; P <0.01) and mechanical circulatory support (26% versus 34%; P <0.0001) compared with men. Women with HF-CS had higher in-hospital mortality than men, even after adjusting for age, illness severity, and comorbidities (34% versus 23%; odds ratio, 1.76 [95% CI, 1.42–2.17]). In contrast, there were no significant sex differences in utilization of advanced CICU monitoring and interventions, or mortality, among patients with CS after acute myocardial infarction. CONCLUSIONS: Women with HF-CS had lower use of pulmonary artery catheters and mechanical circulatory support, shorter CICU lengths of stay, and higher in-hospital mortality than men, even after accounting for age, illness severity, and comorbidities. These data highlight the need to identify underlying reasons driving the differences in treatment decisions, so outcomes gaps in HF-CS can be understood and eliminated.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".