Healthcare utilization and mortality outcomes in patients with pre-existing psychiatric disorders after intensive care unit discharge: A population-based retrospective cohort study
Bibliographic record
Abstract
PURPOSE: Pre-existing psychiatric disorders may lead to negative outcomes following intensive care unit (ICU) discharge. We evaluated the association of pre-existing psychiatric disorders with subsequent healthcare utilization and mortality in patients discharged from ICU. MATERIALS AND METHODS: We retrospectively studied adult patients admitted to 14 medical-surgical ICUs (January 2014-June 2016) with ICU length stay ≥24 h who survived to hospital discharge. Pre-existing psychiatric disorders were identified using algorithms for diagnostic codes captured ≤5 years before ICU admission. Outcomes were healthcare utilization (emergency department visit, hospital or ICU readmission) and mortality. We used logistic regression models with propensity scores to estimate associations, converted to risk ratios (RR). RESULTS: We included 10,598 patients. 37.6% (n = 3982) had a psychiatric history. Patients with pre-existing psychiatric disorders were at higher risk of subsequent emergency department visits (RR 1.49, 95%CI 1.29-1.71), hospital readmission (RR 1.49, 95%CI 1.34-1.66), ICU readmission (RR 2.64, 95%CI 1.55-4.49) one-year post-ICU discharge, compared to patients without pre-existing psychiatric disorders. Patients with pre-existing psychiatric disorders had a higher risk of mortality (RR 1.31, 95%CI 1.00-1.71) six-months post-ICU discharge. CONCLUSION: Critically ill patients with pre-existing psychiatric disorders have an increased risk of healthcare utilization and mortality outcomes following an ICU stay.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".