Advanced Respiratory Support in the Contemporary Cardiac ICU
Bibliographic record
Abstract
The medical complexity and critical care needs of patients admitted to cardiac ICUs are increasing, and prospective studies examining the underlying cardiac and noncardiac diagnoses, the management strategies, and the prognosis of cardiac ICU patients with respiratory failure are needed. DESIGN: Prospective cohort study. SETTING: The Critical Care Cardiology Trials Network is a research collaborative of cardiac ICUs across the United States and Canada. PATIENTS: We included all medical cardiac ICU admissions at 25 cardiac ICUs during two consecutive months annually at each center from 2017 to 2019. MEASUREMENTS: We evaluated the use of advanced respiratory therapies including invasive mechanical ventilation, noninvasive ventilation, and high-flow nasal cannula versus no advanced respiratory support across admission diagnoses and the association with in-hospital mortality. MAIN RESULTS: Of 8,240 cardiac ICU admissions, 1,935 (23.5%) were treated with invasive mechanical ventilation, 573 (7.0%) with noninvasive ventilation, and 281 (3.4%) with high-flow nasal cannula. Admitting diagnoses among those with advanced respiratory support were diverse including general medical problems in patients with heart disease as well as primary cardiac problems. In-hospital mortality was higher in patients who received invasive mechanical ventilation (38.1%; adjusted odds ratio, 2.53; 2.02-3.16) and noninvasive ventilation or high-flow nasal cannula (8.8%; adjusted odds ratio, 2.25; 1.73-2.93) compared with patients without advanced respiratory support (4.6%). Reintubation rate was 7.6%. The most common variables associated with respiratory insufficiency included heart failure, infection, chronic obstructive pulmonary disease, and pulmonary vascular disease. CONCLUSIONS: One-third of cardiac ICU admissions receive respiratory support with associated increased mortality. These data provide benchmarks for quality improvement ventures in the cardiac ICU, inform cardiac critical care training and staffing patterns, and serve as foundation for future studies aimed at 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".