Management of Acute Decompensated Heart Failure in the Cardiac Intensive Care Unit: The Importance of Co-management With a Heart Failure Specialist
Bibliographic record
Abstract
Background Heart failure (HF) is a common reason for admission to the cardiac intensive care unit. We sought to identify the role of an HF consultation service in improving the management of this patient population. Methods We identified all adult patients admitted to the cardiac intensive care unit (2014-2015) at the University Health Network with a diagnosis of acute decompensated HF ± cardiogenic shock (CS). Clinical characteristics and course were recorded. We calculated a propensity score–adjusted association between HF consultation and in-hospital mortality. Results A total of 285 unique patients were identified in our cohort. Of these, 82 (28.7%) died. A total of 150 patients (52.6%) were co-managed by an HF service, and 135 patients (47.3%) were not. Patients who were managed by an HF team were younger (52.5 vs 68.0 years, P < 0.0001), were more likely to be admitted with CS (61.3 vs 41.5%, P < 0.0009), and had higher rates of vasoactive medications during their admission (69.3% vs 52.6%, P < 0.005). At discharge, there were higher rates of discharge to a HF clinic (52.0% vs 27.5%, P < 0.0001) and prescription of guideline-directed medical therapy. In-hospital mortality was lower in those co-managed by a HF team (16.7% vs 42.2%, P < 0.0001). HF consultation reduced the odds of readmission by 76% (odds ratio, 0.24; 95% confidence interval, 0.13-0.47). Conclusions Patients managed by a HF team were more likely to be in CS at admission, to survive to discharge from hospital, and to be initiated on guideline-directed medical therapy with HF follow-up.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".