Abstract 13175: Outcomes of Heart Failure (HF) Patients Admitted to the Intensive Care Unit (ICU): Impact of Early or Late ICU Admission
Bibliographic record
Abstract
Background: Little is known about the outcomes of HF patients who are admitted to an ICU. We examined the outcomes of HF patients who were admitted to directly to the ICU (early) or admitted to the ICU after initial ward admission (late), compared to non-ICU admitted patients. Methods: We examined 118,595 HF patients (ICD-10 code I50) in Ontario, Canada, who were hospitalized from 2003-2012 using the Canadian Institute for Health Information Discharge Abstract Database. We examined the association of ICU admission and timing with: a) 30-day mortality using multiple Cox regression with time-varying covariates, and b) 30-day hospital readmissions using repeated events analysis approach of Prentice, Williams and Peterson. Results: Of the cohort, 24,119 (20%) were admitted to an ICU during the hospital stay, of whom 84% were admitted early (median age 76 years, 54% men) and 16% were admitted later (age 77 years, 53% men). 30-day mortality was higher in early and late ICU compared to no ICU: 13%, 27%, 10.5% (p <.001). Multivariable-adjusted hazard ratios (HR) were: 1.65 (95%CI; 1.58-1.73) for early ICU (p<.001) and 4.59 (95%CI; 4.31-4.89) for late ICU (p<.001) vs. no ICU (referent). All-cause 30-day readmissions were also highest among late ICU, followed by early ICU, and lowest in non-ICU patients: 24.3, 22.9, 21.7 readmissions per 100 person-months (all p <.001). Multivariable-adjusted repeated events analysis demonstrated a progressively increasing HRs for all-cause readmission: 1.07 (95%CI; 1.04-1.11) for early ICU (p<.001) and 1.13 (95%CI; 1.04-1.22) for late ICU (p<.01) vs. no ICU (referent). Median in-hospital costs were $16,553 for late ICU, $8587 for early ICU, and $7296 for non-ICU admitted patients (p <.001). Conclusions: HF patients who are admitted to the ICU are sicker and experience increased risk of 30-day readmissions and death. Late ICU admissions were associated with the highest risk of death and readmission, and incurred substantially higher costs of care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".