Early Rehospitalization After Prolonged Intensive Care Unit Stay Post Cardiac Surgery: Outcomes and Modifiable Risk Factors
Bibliographic record
Abstract
BACKGROUND: Prolonged intensive care unit length of stay (prICULOS) following cardiac surgery (CS) in older adults is increasingly common but rehospitalization characteristics and outcomes are understudied. We sought to describe the rehospitalization characteristics and subsequent non-institutionalized survival of prICULOS (ICULOS ≥5 days) patients and identify modifiable risk factors to decrease 30-day rehospitalization. METHODS AND RESULTS: Consecutive patients from January 1, 2000 to December 31, 2011 were analyzed utilizing linked clinical and administrative databases. Logistic regression was used to identify risk factors associated with 30-day rehospitalization. Out of 9210 consecutive patients discharged from the hospital alive, 596 (6.5%) experienced prICULOS. Cumulative incidence of rehospitalization for the prICULOS cohort at 30 and 365 days was 17.5% and 45.6% versus 11.4% and 28.1% for non-prICULOS (P<0.01). Over 40% of rehospitalizations for the entire cohort occurred within 30 days of discharge costing over $12 million. The most common reasons for rehospitalization were heart failure (in prICULOS) and infection (in non-prICULOS). Rehospitalization within 30 days was associated with a 2.29-fold risk of poor 1-year noninstitutionalized survival for the entire cohort. Potentially modifiable factors affecting 30-day rehospitalization included lack of physician visits within 30 days of discharge (odds ratio 2.11; P=0.01), and preoperative anxiety diagnosis (odds ratio 2.20; P=0.01). CONCLUSIONS: PrICULOS patients have high rates of rehospitalization that is associated with an increased rate of poor noninstitutionalized survival. Addressing modifiable risk factors including early postdischarge access to physician services, as well as access to mental health services may improve patient 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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".