In-Hospital Cardiac Arrest in the Cardiac Catheterization Laboratory: Effective Transition from an ICU- to CCU-Led Resuscitation Team
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Bibliographic record
Abstract
OBJECTIVES: (1) To examine the incidence and outcomes of in-hospital cardiac arrests (IHCAs) in a large unselected patient population who underwent coronary angiography at a single tertiary academic center and (2) to evaluate a transitional change in which the cardiologist is positioned as the cardiopulmonary resuscitation (CPR) leader in the cardiac catheterization laboratory (CCL) at our local tertiary care institution. BACKGROUND: IHCA is a major public health concern with increased patient morbidity and mortality. A proportion of all IHCAs occurs in the CCL. Although in-hospital resuscitation teams are often led by an Intensive Care Unit- (ICU-) trained physician and house staff, little is known on the role of a cardiologist in this setting. METHODS: Between 2012 and 2016, a single-center retrospective cohort study was performed examining 63 adult patients (70 ± 10 years, 60% males) who suffered from a cardiac arrest in the CCL. The ICU-led IHCAs included 19 patients, and the Coronary Care Unit- (CCU-) led IHCAs included 44 patients. RESULTS: Acute coronary syndrome accounted for more than 50% of cardiac arrests in the CCL. Pulseless electrical activity was the most common rhythm requiring chest compression, and cardiogenic shock most frequently initiated a code blue response. No significant differences were observed between the ICU-led and CCU-led cardiac arrests in terms of hospital length of stay and 1-year survival rate. CONCLUSION: In the evolving field of Critical Care Cardiology, the transition from an ICU-led to a CCU-lead code blue team in the CCL setting may lead to similar short-term and long-term outcomes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 it