Presentation and Outcomes of Patients With Preoperative Critical Illness Undergoing Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Little is known about the prevalence and post-surgical outcomes associated with cardiac intensive care unit (CICU) therapeutics among CICU patients referred for cardiac surgery. OBJECTIVES: The purpose of this study was to investigate the clinical characteristics and outcomes of CICU patients referred for cardiac surgery from the intensive care unit. METHODS: We analyzed characteristics and outcomes of CICU admissions referred from the CICU for cardiac surgery during 2017 to 2020 across 29 centers. The primary outcome was in-hospital mortality. RESULTS: Among 10,321 CICU admissions, 887 (8.6%) underwent cardiac surgery, including 406 (46%) coronary artery bypass graftings, 201 (23%) transplants or ventricular assist devices, 171 (19%) valve surgeries, and 109 (12%) other procedures. Common indications for CICU admission included shock (33.5%) and respiratory insufficiency (24.9%). Preoperative CICU therapies included vasoactive therapy in 52.2%, mechanical circulatory support in 35.9%, renal replacement in 8.2%, mechanical ventilation in 35.7%, and 17.5% with high-flow nasal cannula or noninvasive positive pressure ventilation. In-hospital mortality was 11.7% among all CICU admissions and 9.1% among patients treated with cardiac surgery. After multivariable adjustment, pre-op mechanical circulatory support and renal replacement therapy were associated with mortality, while respiratory support and vasoactive therapy were not. CONCLUSIONS: Nearly 1 in 12 contemporary CICU patients receive cardiac surgery. Despite high preoperative disease severity, CICU admissions undergoing cardiac surgery had a comparable mortality rate to CICU patients overall; highlighting the ability of clinicians to select higher acuity patients with a reasonable perioperative risk.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".