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Record W4409169007 · doi:10.1186/s12888-025-06731-5

Risk factors and outcomes of hyperactive delirium in older medical inpatients admitted to non-intensive care unit: a prospective cohort study

2025· article· en· W4409169007 on OpenAlexaboutno aff
Panumas Kaewpongsa, Kulapong Jayanama, Sirasa Ruangritchankul

Bibliographic record

VenueBMC Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
FundersMahidol University
KeywordsDeliriumMedicineHazard ratioProspective cohort studyIntensive care unitMedical recordEmergency medicineAdverse effectIntensive careCohort studySedationPediatricsConfidence intervalIntensive care medicineInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.315
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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