[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100643704":3},{"organization":4,"armGroups":7,"interventions":21,"overallOfficials":20,"centralContacts":36,"locations":43,"responsibleParty":59,"collaborators":20,"id":63,"slug":64,"hasResults":65,"nctId":66,"briefTitle":67,"officialTitle":68,"acronym":20,"eligibilityCriteria":69,"healthyVolunteers":70,"sex":71,"minAge":72,"maxAge":20,"enrollmentInfo":73,"targetDuration":20,"studyType":76,"phases":77,"briefSummary":79,"conditions":80,"keywords":83,"overallStatus":90,"whyStopped":20,"lastUpdateSubmitDate":91,"lastUpdatePostDateStruct":92,"startDateStruct":95,"completionDateStruct":97,"leadSponsor":99,"locationsCount":100},{"fullName":5,"class":6},"Taipei Medical University","OTHER",[8,16],{"label":9,"type":10,"description":11,"interventionNames":12},"Smart Computing Assisted Nursing Decision in Delirium Prevention(SCAN-D) group","EXPERIMENTAL","The algorithm will calculate variable weights and select features contributing to 80% of the risk. These variables will guide the implementation of personalized delirium prevention strategies, with making decision based on nurses' clinical expertise.",[13,14,15],"Device: MLP-based Clinical Decision Support System (MLP-CDSS)","Behavioral: VR-based Cognitive Intervention","Behavioral: VR-assisted Early Mobility Exercise",{"label":17,"type":18,"description":19,"interventionNames":20},"routine care control group","NO_INTERVENTION","The control group will receive routine care, including delirium assessments and sleep environment management.",null,[22,27,32],{"type":23,"name":24,"description":25,"armGroupLabels":26,"otherNames":20},"DEVICE","MLP-based Clinical Decision Support System (MLP-CDSS)","An AI-integrated system utilizing a Multilayer Perceptron (MLP) model to predict delirium risk and guide targeted interventions. The system continuously analyzes real-time data from Electronic Health Records (EHR), including physiological parameters, medication history, and laboratory results.",[9],{"type":28,"name":29,"description":30,"armGroupLabels":31,"otherNames":20},"BEHAVIORAL","VR-based Cognitive Intervention","A virtual reality intervention designed to provide cognitive stimulation and reduce sensory deprivation. Patients engage in interactive games (e.g., traditional-themed tasks such as goldfish scooping or lantern festivals) that require attention, memory, and spatial orientation. These sessions are conducted twice daily for total 30-40 minutes, 3-4 times a week.",[9],{"type":28,"name":33,"description":34,"armGroupLabels":35,"otherNames":20},"VR-assisted Early Mobility Exercise","A VR-based physical activity program focused on promoting upper limb movement and range of motion. The virtual environment encourages patients to perform specific gestures or reaching tasks while in bed or a seated position, aiming to improve mobility and reduce the physical deconditioning common in ICU stays. These sessions are conducted twice daily for total 30-40 minutes, 3-4 times a week.",[9],[37],{"name":38,"role":39,"phone":40,"phoneExt":41,"email":42},"Hsiao-Yean Chiu, Professor","CONTACT","886-2-27361661","6329","hychiu0315@tmu.edu.tw",[44],{"facility":45,"status":20,"city":46,"state":20,"zip":47,"country":48,"countryCode":49,"cosmosGeoPoint":50,"geoPoint":55,"contacts":56},"Taipei Medical University, Taipei, 110","Taipei","110","Taiwan","TW",{"type":51,"coordinates":52},"Point",[53,54],121.52639,25.05306,{"lat":54,"lon":53},[57],{"name":58,"role":39,"phone":40,"phoneExt":41,"email":42},"Hisao-Yean Chiu, Professor",{"type":60,"investigatorFullName":61,"investigatorTitle":62,"investigatorAffiliation":5,"oldNameTitle":20,"oldOrganization":20},"PRINCIPAL_INVESTIGATOR","Hsiao-Yean Chiu","Principal Investigator","100643704","precision-icu-care-evaluating-an-ai-driven-nursing-decision-support-system-for-delirium-prevention-100643704",false,"NCT07631494","Precision ICU Care: Evaluating an AI-Driven Nursing Decision Support System for Delirium Prevention","Efficacy of an Intelligent Computational Nursing Decision Support System in Precision Critical Care for Delirium Prevention: A Randomized Controlled Trial","Inclusion Criteria:\n\n* Patients aged 18 years\n* High risk of developing delirium are defined as PRE-DELIRIC (PREdiction of DELIRium in ICu patients) with a score of ≥40%\n* Sedation assessment form (Richmond Agitation-Sedation). Scale, RASS) is assessed as between +1 and -1\n* Patient is clearly aware and able to communicate with Chinese and Taiwanese customers.\n\nExclusion Criteria:\n\n* Those who have developed delirium before joining the study (assessed by the Intensive Care Delirium Screening Checklist (ICDSC) \\>4 points)\n* Expected to stay in the ICU for less than 24 hours\n* APACHE II score \\> 25 (meaning \\> 50% mortality)\n* Delirium cannot be evaluated, such as those with severe acute brain injury, audio-visual impairment, speech problems, psychotic disorders, aphasia, or in the entire ICU During this period, patient remained comatose.",true,"ALL","18 Years",{"count":74,"type":75},188,"ESTIMATED","INTERVENTIONAL",[78],"NA","Delirium affects up to 83% of mechanically ventilated patients in the Intensive Care Unit (ICU), often leading to longer hospital stays and long-term memory or cognitive problems. While standard care protocols (such as the ABCDEF bundle) exist, they are often difficult to implement fully due to their complexity and the heavy workload on nursing staff.\n\nTo address these challenges, this study introduces a 'Precision Nursing' approach by integrating Artificial Intelligence (AI) and Virtual Reality (VR). We will implement an AI-driven system to assist nurses in making personalized care decisions more efficiently. Additionally, interactive VR technology will be used to stimulate patients' cognitive function and encourage early mobility. Our goal is to reduce the clinical burden on healthcare providers while significantly improving recovery outcomes for ICU patients.",[81,82],"Delirium in the Intensive Care Unit","Sleep Quality",[84,85,86,87,88,89],"Artificial Intelligence","delirium","intensive care unit","Virtual Reality","sleep","machine learning","NOT_YET_RECRUITING","2026-06-01",{"date":93,"type":94},"2026-06-08","ACTUAL",{"date":96,"type":75},"2026-06",{"date":98,"type":75},"2028-06",{"name":5,"class":6},1]