Risk factors and outcomes of hyperactive delirium in older medical inpatients admitted to non-intensive care unit: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Hyperactive delirium is a common complication in older medical inpatients in non-intensive care units. This condition increases the risk of diminished physical function, morbidity, and mortality. Moreover, antipsychotics and sedatives were widely used in these patients, contributing to many drug interactions and adverse drug reactions. This study aimed to evaluate the risk factors for hyperactive delirium and assess adverse outcomes among these susceptible patients. METHODS: We conducted a prospective observational study to examine hyperactive delirium as an exposure and its association with adverse outcomes without intervention. A total of 238 medical patients aged ≥ 60 admitted to non-intensive care units at Ramathibodi Hospital between September 1, 2022, and December 31, 2023, were enrolled. The clinical characteristics, physical examination, and biochemical profiles at baseline were assessed. Adverse clinical outcomes at 90 days after discharge were evaluated by reviewing electronic medical records (EMRs). The Confusion Assessment Method and Richmond Agitation-Sedation Scale (RASS) score of + 1 to + 4 were used to diagnose hyperactive delirium. The Cox proportional hazard model was performed to identify risk factors and adverse clinical outcomes associated with hyperactive delirium, with results reported as hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Overall, hyperactive delirium was diagnosed in 115 (48.3%) patients and had an incidence rate of 101.1 cases per 1000 person-days. The risk factors for hyperactive delirium were urinary incontinence (HR 1.69, 95% CI 1.11-2.57), clinical frailty scale (CFS) ≥ 5 (HR 2.79, 95% CI 1.69-4.62), and Montreal Cognitive Assessment (MoCA) score < 25 (HR 4.63, 95% CI 1.09-19.75). Within 90 days after discharge, 14 (12.2%) patients with delirium had died. Medical inpatients who experienced hyperactive delirium had an 8.23-fold increased risk of 90-day mortality following hospital discharge compared to those without delirium (HR 8.23, 95% CI 1.38-48.98). CONCLUSIONS: The risk factors for hyperactive delirium were urinary incontinence, frailty (CFS score ≥ 5), and cognitive impairment (MoCA score < 25). Among older medical inpatients, hyperactive delirium was an independent predictor of 90-day mortality after 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".