Implementation of a Multidisciplinary Cardiogenic Shock Team in a Nonacademic Canadian Heart Centre: An Implementation Study
Bibliographic record
Abstract
Background In this study we evaluated our ability to implement team-based cardiogenic shock (CS-Team), focussing on: 1) early screening; 2) CS-Team activation; and 3) use of invasive monitoring to guide therapy. Methods All patients admitted to the coronary care unit (CCU) over 12 months were screened for CS. A diagnosis of CS was made when both hypotension and hypoperfusion were present. The CS-Team was composed of the CCU attending, an interventional cardiologist, and a cardiac surgeon. Multivariate analysis was carried out with mortality as the outcome of interest. Results Screening was documented in 74% (1160 of 1562) of patients admitted to a critical care unit; of these, 1080 were not in CS. We identified 80 patients in CS (Society for Cardiovascular Angiography & Interventions [SCAI] stages C-E), which represented 6.9% of all screened patients. Patients in CS had significantly higher in-hospital mortality (35% vs 2%, P < 0.0001). CS-Team was activated in 35 of 80 patients (44%). CS-Team activation resulted in significantly greater use of invasive monitoring (pulmonary artery catheter [49% vs 7%, P < 0.0001], cardiac catheterization [94% vs 76%, P < 0.032], and mechanical circulatory support [51% vs 2%, P < 0.001]). Independent predictors of mortality were severity of CS (SCAI grades D or E) (odds ratio [OR] 18.78, 95% confidence interval [CI] 4.89-96.65) and age, in years (OR 1.07, 95% CI 1.01-1.14), whereas CS-Team was not predictive of mortality (OR 0.66, 95% CI 0.16-2.41). Conclusions We found that: 1) early screening by frontline staff was feasible but had limitations (26% screening failure); 2) CS-Team activation appeared discretionary (limited activation to 45% of patients); and 3) CS-Team activation resulted in a significant increase in the use of invasive monitoring that helped guide therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".