Abstract 15975: Cardiogenic Shock Teams Are Associated With Lower Mortality, Bleeding and Vascular Complications: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Short-term mortality and morbidity associated with cardiogenic shock (CS) remains high. However, observational studies suggest that CS teams improve outcomes. Our aim is to systematically evaluate the outcomes associated with CS team use by performing a meta-analysis of the published literature. Methods: A comprehensive literature search of the PubMed/EMBASE, Cochrane databases and published abstracts was performed from inception to 06/01/2023 including studies comparing CS outcomes before and after the implementation of CS teams (excluding case reports and pediatric studies). Outcomes included in-hospital mortality, bleeding, vascular complications, use of temporary mechanical circulatory support (tMCS), and renal replacement therapy (RRT). Odds ratios (OR) with 95% confidence intervals (CI) were calculated using DerSimonian-Laird method. The Eggers test was used to assess publication bias; significant heterogeneity was considered if I 2 > 75%. Results: Of 524 studies screened, 5 met inclusion criteria. This included 2,331 subjects (1091 managed with a CS team and 1270 without, mean age 62.5 years, 72.1% male, 41% non-white). The implementation of CS teams was associated with lower risk of in-hospital mortality ([OR] = 0.63 [95% CI 0.49 - 0.81]; I 2 =19%; p < 0.001) (Fig), major bleeding (OR = 0.69 [95% CI 0.48 - 0.99]; I 2 =0%; p = 0.042), and vascular complications (OR = 0.61 [95% CI 0.40 - 0.93]; I 2 =0%; p = 0.021) compared to management without CS teams. There were no differences in tMCS (OR = 0.81 [95% CI 0.56 - 1.17]; I 2 =52%; p = 0.26) or RRT use (OR = 0.74 [95% CI 0.44 - 1.25]; I 2 =78%; p = 0.26). There was no evidence of publication bias (p > 0.05). Conclusions: In this meta-analysis, CS team implementation was associated with lower in-hospital mortality, major bleeding, and vascular complications, but no difference in the use of tMCS or RRT. These findings support further prospective studies to assess the impact of CS teams in improving outcomes.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".