Long-Term Outcomes in ICU Patients with Delirium: A Population-based Cohort Study
Bibliographic record
Abstract
Abstract Rationale Delirium is common in the ICU and portends worse ICU and hospital outcomes. The effect of delirium in the ICU on post–hospital discharge mortality and health resource use is less well known. Objectives To estimate mortality and health resource use 2.5 years after hospital discharge in critically ill patients admitted to the ICU. Methods This was a population-based, propensity score–matched, retrospective cohort study of adult patients admitted to 1 of 14 medical–surgical ICUs from January 1, 2014, to June 30, 2016. Delirium was measured by using the 8-point Intensive Care Delirium Screening Checklist. The primary outcome was mortality. The secondary outcome was a composite measure of subsequent emergency department visits, hospital readmission, or mortality. Measurements and Main Results There were 5,936 propensity score–matched patients with and without a history of incident delirium who survived to hospital discharge. Delirium was associated with increased mortality 0–30 days after hospital discharge (hazard ratio, 1.44 [95% confidence interval, 1.08–1.92]). There was no significant difference in mortality more than 30 days after hospital discharge (delirium: 3.9%, no delirium: 2.6%). There was a persistent increased risk of emergency department visits, hospital readmissions, or mortality after hospital discharge (hazard ratio, 1.12 [95% confidence interval, 1.07–1.17]) throughout the study period. Conclusions ICU delirium is associated with increased mortality 0–30 days after hospital discharge.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".