Clinical Practice Patterns in Temporary Mechanical Circulatory Support for Shock in the Critical Care Cardiology Trials Network (CCCTN) Registry
Bibliographic record
Abstract
BACKGROUND: Temporary mechanical circulatory support (MCS) devices provide hemodynamic assistance for shock refractory to pharmacological treatment. Most registries have focused on single devices or specific etiologies of shock, limiting data regarding overall practice patterns with temporary MCS in cardiac intensive care units. METHODS: The CCCTN (Critical Care Cardiology Trials Network) is a multicenter network of tertiary CICUs in North America. Between September 2017 and September 2018, each center (n=16) contributed a 2-month snapshot of consecutive medical CICU admissions. RESULTS: Of the 270 admissions using temporary MCS, 33% had acute myocardial infarction-related cardiogenic shock (CS), 31% had CS not related to acute myocardial infarction, 11% had mixed shock, and 22% had an indication other than shock. Among all 585 admissions with CS or mixed shock, 34% used temporary MCS during the CICU stay with substantial variation between centers (range: 17%-50%). The most common temporary MCS devices were intraaortic balloon pumps (72%), Impella (17%), and veno-arterial extracorporeal membrane oxygenation (11%), although intraaortic balloon pump use also varied between centers (range: 40%-100%). Patients managed with intraaortic balloon pump versus other forms of MCS (advanced MCS) had lower Sequential Organ Failure Assessment scores and less severe metabolic derangements. Illness severity was similar at high- versus low-MCS utilizing centers and at centers with more advanced MCS use. CONCLUSIONS: There is wide variation in the use of temporary MCS among patients with shock in tertiary CICUs. While hospital-level variation in temporary MCS device selection is not explained by differences in illness severity, patient-level variation appears to be related, at least in part, to illness severity.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".