ArticleIntensive care medicine2022
Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome.
Article in Intensive care medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Association of polypharmacy and perioperative outcomes: Systematic review and meta-analysis.Journal of perioperative practicePooled it
- Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation.JMIR medical informatics · 2026Article
- Efficacy of large language models in detecting postoperative delirium from unstructured clinical notes: A retrospective cohort study.NPJ digital medicine · 2025Article
- Natural language processing techniques to detect delirium in hospitalized patients from clinical notes: a systematic review.NPJ digital medicine · 2025Article
- The Association Between Frequency of Fingertip Blood Glucose Measurement and Delirium in Intensive Care Unit Patients: A Retrospective Study Based on the MIMIC-IV Database.Nursing in critical care · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study.Journal of medical Internet research · 2025Article
- Association of low-dose ketamine with hallucinations in critically ill patients: a target trial emulation.Intensive care medicine · 2025Article
- Delirium in ICU and the Macbeth's dagger.Intensive care medicine · 2025Article
- Using AI to detect and treat delirium.Intensive care medicine · 2025Article
- Hallucinations and disturbed behaviour in the critically ill: incidence, patient characteristics, associations, trajectory, and outcomes.Critical care (London, England) · 2025Observational
- A target trial emulation of dexmedetomidine to treat agitation in the intensive care unit.Critical care science · 2025Article
- Advancing Delirium Treatment Trials in Older Adults: Recommendations for Future Trials From the Network for Investigation of Delirium: Unifying Scientists (NIDUS).Critical care medicine · 2025Article
- Olanzapine versus quetiapine in critically ill patients with hyperactive delirium: Protocol for a multicentre, cluster-randomised, double-crossover, pragmatic clinical trial (CALM-ICU).Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2024Article
- Should we abandon the CAM-ICU and practice "humanizing care" for all patients?Intensive care medicine · 2024Article
- Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes.Critical care (London, England) · 2023Article
- Long-term mortality of critically ill patients with cancer and delirium who survived to discharge: a retrospective cohort study.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2023Article
- Triglyceride-glucose index as a valuable predictor for aged 65-years and above in critical delirium patients: evidence from a multi-center study.BMC geriatrics · 2023Article
- Clinical Informatics needs to be a competency for Intensive care training.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2023Article
- Identifying encephalopathy in patients admitted to an intensive care unit: Going beyond structured information using natural language processing.Frontiers in digital health · 2023Article
Corrections and comments
- Commented on by
- Commented on by
- Commented on by
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity.
methodsIn three combined medical-surgical ICUs, we obtained data on demographics, treatment with antipsychotic medications, and outcomes. We applied NLP to caregiver progress notes to diagnose behavioral disturbance and analyzed simultaneous CAM-ICU.
resultsWe assessed 2313 patients with a median lowest Richmond Agitation-Sedation Scale (RASS) score of - 2 (- 4.0 to - 1.0) and median highest RASS score of 1 (0 to 1). Overall, 1246 (53.9%) patients were NLP-Dx-BD positive (NLP-Dx-BD
conclusionMore patients were NLP-Dx-BD positive than CAM-ICU positive. NLP-Dx-BD and CAM-ICU assessment describe partly overlapping populations. However, NLP-Dx-BD identifies more patients likely to receive antipsychotic medications. In the absence of NLP-Dx-BD, treatment with antipsychotic medications is rare.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.