Utilization of Shock Team and Veno-Arterial Extracorporeal Membrane Oxygenation (VA-ECMO) in the Management of Cardiogenic Shock in Northern Ontario
Bibliographic record
Abstract
Background: Despite advancements in critical care and coronary revascularization, cardiogenic shock (CS) outcomes remain poor. Implementing a shock team and use of veno-arterial extracorporeal membrane oxygenation (VA-ECMO) have been associated with improved CS outcomes, but its feasibility in remote and rural areas remains unknown. Methods: This retrospective study included patients with CS who required mechanical circulatory support (MCS) at Health Sciences North, Sudbury, Ontario. The analysis aimed to accomplish 2 objectives: first, to review the outcomes associated with use of Impella (Abiomed, Danvers, MA) and, second, to assess the feasibility of establishing a shock team to facilitate the local implementation of VA-ECMO. The primary endpoint was in-hospital mortality. Results: The outcomes of 15 patients with CS who received Impella between 2015 and 2021 were reviewed. Their average age was 65 years (standard deviation [SD]: 13), and 8 patients (53%) were female. CS was ischemic in 12 patients (80%). Transfemoral Impella CP (cardiac power) was the most frequently used (93%). Thirteen patients (87%) died during the index hospital stay post-Impella because of progressive circulatory failure. The shock team was established following consultations with several Canadian MCS centres, leading to the development of a protocol to guide use of MCS. There have been 4 cases in which percutaneous VA-ECMO using Cardiohelp (Getinge/Maquet, Wayne, NJ) has been used; 3 (75%) survived beyond the index hospitalization. Conclusions: This analysis demonstrated the feasibility of implementing a shock team in remote Northern Ontario, enabling the use of VA-ECMO with success in a centre with a sizeable rural catchment area. This initiative helps address the gap in cardiac care outcomes between rural and urban areas in Ontario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".