ArticlePloS one2024
A cluster-randomized controlled trial of a nurse-led artificial intelligence assisted prevention and management for delirium (AI-AntiDelirium) on delirium in intensive care unit: Study protocol.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- AI literacy as a potential mediator between attitude and self-efficacy among PICU nurses: a cross-sectional study.Frontiers in public health · 2026Article
- Progress of nursing management for delirium in critically ill geriatric patients: a narrative review.Frontiers in human neuroscience · 2026Review
- Enhancing patient care quality and safety under the hospital-wide "One Bed" model: a FMEA-based approach.Frontiers in medicine · 2026Review
- Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach.Life (Basel, Switzerland) · 2025Article
- A Comprehensive Understanding of Psychomotor Agitation: From Causes to Hospital Care.Current neuropharmacology · 2025Review
- Artificial intelligence applications in intensive care unit nursing: A narrative review (2020-2025).Digital healthReview
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDelirium is a common complication among intensive care unit (ICU) patients that is linked to negative clinical outcomes. However, adherence to the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS guidelines), which recommend the use of the ABCDEF bundle, is sub-optimal in routine clinical care. To address this issue, AI-AntiDelirium, a nurse-led artificial intelligence-assisted prevention and management tool for delirium, was developed by our research team. Our pilot study yielded positive findings regarding the use of AI-AntiDelirium in preventing patient ICU delirium and improving activities of daily living and increasing intervention adherence by health care staff.
methodsThe proposed large-scale pragmatic, open-label, parallel-group, cluster randomized controlled study will assess the impact of AI-AntiDelirium on the incidence of ICU delirium and delirium-related outcomes. Six ICUs in two tertiary hospitals in China will be randomized in a 1:1 ratio to an AI-AntiDelirium or a PADIS guidelines group. A target sample size of 1,452 ICU patients aged 50 years and older treated in the ICU for at least 24 hours will be included. The primary outcome evaluated will be the incidence of ICU delirium and the secondary outcomes will be the duration of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, patient activities of daily living, and ICU nurse adherence to the ABCDEF bundle. DISCUSSION: If this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice. CLINICAL
trial registrationChiCTR1900023711 (Chinese Clinical Trial Registry).
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