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Trial registered on ANZCTR
Registration number
ACTRN12625000769482p
Ethics application status
Submitted, not yet approved
Date submitted
21/05/2025
Date registered
21/07/2025
Date last updated
21/07/2025
Date data sharing statement initially provided
21/07/2025
Type of registration
Prospectively registered
Titles & IDs
Public title
Early delirium detection and prediction in hospitalised patients through analysis of clinical signs and behaviours.
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Scientific title
Clinical and behavioural analysis for early prediction and diagnosis of delirium in a hospital setting
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Secondary ID [1]
314489
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None
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Universal Trial Number (UTN)
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Trial acronym
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Linked study record
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Health condition
Health condition(s) or problem(s) studied:
Delirium
337535
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Condition category
Condition code
Mental Health
333897
333897
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0
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Other mental health disorders
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Intervention/exposure
Study type
Observational
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Patient registry
False
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Target follow-up duration
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Target follow-up type
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Description of intervention(s) / exposure
This research will collect data on the clinical and behavioural patterns linked to delirium in hospitalized patients. All consecutive individuals with and without a current diagnosis of delirium admitted as inpatients in the acute and sub-acute services of St Vincent’s Hospital Melbourne will be considered for recruitment to this prospective, longitudinal observational study. First Nations peoples over 50 years of age, and other individuals over 65 years of age, will be eligible for inclusion from the following SVHM study sites: St Vincent’s Hospital Melbourne Inpatient Services (IPS), Bolte Wing (SVHM), and St George’s Hospital, Kew.
Once recruited, patients will continue to receive standard medical care from the treating team in addition to their participation in the study. To effectively monitor participants, re-identified data will be collected from both wearable devices and the hospital database system. The wearable device used in this study is developed by Verisense Health and is a wrist-worn sensor designed to capture continuous physiological and activity-related data. Participants will be asked to wear the device continuously, including while sleeping, for uninterrupted data collection. The device may be temporarily removed for activities such as showering, bathing, or during medical procedures—particularly radiation-based procedures or any intervention where the attending technician advises removal. In all such cases, participants will be reminded to reapply the device as soon as the procedure is completed to maintain consistent data capture.
Unique patient IDs will be created for each participant, and their wearable information and clinical information will be linked to these IDs to facilitate continuous monitoring and recording of relevant data. The wearable device will track physiological indicators including skin temperature, heart rate variability, sleep stages from movement, blood volume changes, sympathetic arousal metrics, and levels of physical activity.
From the hospital database, re-identified demographic information including age, gender, past clinical history (including previous admissions and comorbidities), and current clinical information relevant to delirium risk (e.g., blood test results, fluid and food intake, bowel movements, medication usage, other laboratory test results, and vital signs) will be collected. Observed signs and indications of delirium, such as agitation and any positive results from the 4AT assessment, will also be documented. The 4AT will be administered by trained clinical staff at least every 72 hours and also upon any observed behavioural change suggestive of delirium.
The presence or absence of delirium will be determined based on clinical diagnoses documented in the patient’s medical records by the treating clinicians. These diagnoses will generate timestamped ground truth labels, marking the onset and resolution of delirium episodes. The timestamped labels will be aligned with the wearable sensor data and associated clinical information to create a comprehensive dataset for training and evaluating AI models developed for delirium prediction.
Participants will be observed for the duration of their hospital admission or up to a maximum of 30 days from admission, whichever is shorter. Machine learning and deep learning models, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Decision Tree, Random Forest, Gradient Boosting, Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), and Transformer models will be developed to analyse the data. These models will be compared using standard performance measures such as accuracy, precision, recall, and F1 score to determine the most effective approach for predicting delirium.
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Intervention code [1]
331115
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Early Detection / Screening
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Comparator / control treatment
Validated clinical tools and formal clinical diagnoses will serve as the reference standard for evaluating the AI models developed in this study. Specifically, the 4AT (4 A’s Test), a validated screening tool for delirium, will be administered to all participants as part of the daily clinical routine and additionally upon any observed behavioral changes suggestive of delirium. These assessments will be conducted by trained clinical staff during the participant’s inpatient stay. In addition, formal clinical diagnoses of delirium made by treating clinicians and documented in the patient’s medical record—including ICD-coded hospital data—will be used to generate timestamped ground truth labels marking the onset and resolution of delirium episodes. These labels will be aligned with the wearable sensor data and associated clinical information to create a comprehensive dataset for training and evaluating the AI models. This approach ensures that AI predictions are assessed against established clinical references collected within the same timeframe as the wearable data.
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Control group
Active
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Outcomes
Primary outcome [1]
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Accuracy of delirium onset prediction by AI techniques compared to 4AT clinical assessment.
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Assessment method [1]
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Data will be collected from two primary sources: wearable devices and hospital clinical records. The wearable devices will continuously capture physiological and behavioral parameters, including physical activity, heart rate, sleep duration and quality, and skin temperature. Clinical data extracted from the hospital medical records will include vital signs—like respiratory rate, blood pressure, oxygen saturation (SpO2), temperature, and heart rate—along with comorbidities, past medical history, medication usage, clinician-documented diagnoses of delirium, and behavioral observations like confusion or agitation. These combined data will be assessed using novel Artificial Intelligence (AI) techniques to identify predictive patterns and early indicators of delirium.
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Timepoint [1]
341550
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Everyday from baseline till 30 days or till the patient is admitted at St Vincent's Hospital Melbourne, whichever is of lesser duration.
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Secondary outcome [1]
447909
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Precision of delirium onset prediction by AI techniques compared to 4AT clinical assessment
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Assessment method [1]
447909
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Data will be collected from two primary sources: wearable devices and hospital clinical records. The wearable devices will continuously capture physiological and behavioral parameters, including physical activity, heart rate, sleep duration and quality, and skin temperature. Clinical data extracted from the hospital medical records will include vital signs—like respiratory rate, blood pressure, oxygen saturation (SpO2), temperature, and heart rate—along with comorbidities, past medical history, medication usage, clinician-documented diagnoses of delirium, and behavioral observations like confusion or agitation. These combined data will be assessed using novel Artificial Intelligence (AI) techniques to identify predictive patterns and early indicators of delirium.
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Timepoint [1]
447909
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Everyday from baseline till 30 days or till the patient is admitted at St Vincent's Hospital Melbourne, whichever is of lesser duration.
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Secondary outcome [2]
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Recall of delirium onset prediction by AI techniques compared to 4AT clinical assessment
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Assessment method [2]
448380
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Data will be collected from two primary sources: wearable devices and hospital clinical records. The wearable devices will continuously capture physiological and behavioral parameters, including physical activity, heart rate, sleep duration and quality, and skin temperature. Clinical data extracted from the hospital medical records will include vital signs—like respiratory rate, blood pressure, oxygen saturation (SpO2), temperature, and heart rate—along with comorbidities, past medical history, medication usage, clinician-documented diagnoses of delirium, and behavioral observations like confusion or agitation. These combined data will be assessed using novel Artificial Intelligence (AI) techniques to identify predictive patterns and early indicators of delirium.
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Timepoint [2]
448380
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Everyday from baseline till 30 days or till the patient is admitted at St Vincent's Hospital Melbourne, whichever is of lesser duration.
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Secondary outcome [3]
448381
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F1 Score of delirium onset prediction by AI techniques compared to 4AT clinical assessment
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Assessment method [3]
448381
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Data will be collected from two primary sources: wearable devices and hospital clinical records. The wearable devices will continuously capture physiological and behavioral parameters, including physical activity, heart rate, sleep duration and quality, and skin temperature. Clinical data extracted from the hospital medical records will include vital signs—like respiratory rate, blood pressure, oxygen saturation (SpO2), temperature, and heart rate—along with comorbidities, past medical history, medication usage, clinician-documented diagnoses of delirium, and behavioral observations like confusion or agitation. These combined data will be assessed using novel Artificial Intelligence (AI) techniques to identify predictive patterns and early indicators of delirium.
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Timepoint [3]
448381
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Everyday from baseline till 30 days or till the patient is admitted at St Vincent's Hospital Melbourne, whichever is of lesser duration.
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Secondary outcome [4]
448382
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False Positive Rate of delirium onset prediction by AI techniques compared to 4AT clinical assessment
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Assessment method [4]
448382
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Data will be collected from two primary sources: wearable devices and hospital clinical records. The wearable devices will continuously capture physiological and behavioral parameters, including physical activity, heart rate, sleep duration and quality, and skin temperature. Clinical data extracted from the hospital medical records will include vital signs—like respiratory rate, blood pressure, oxygen saturation (SpO2), temperature, and heart rate—along with comorbidities, past medical history, medication usage, clinician-documented diagnoses of delirium, and behavioral observations like confusion or agitation. These combined data will be assessed using novel Artificial Intelligence (AI) techniques to identify predictive patterns and early indicators of delirium.
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Timepoint [4]
448382
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Everyday from baseline till 30 days or till the patient is admitted at St Vincent's Hospital Melbourne, whichever is of lesser duration.
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Secondary outcome [5]
448383
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Classification of hypoactive states of delirium using AI techniques
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Assessment method [5]
448383
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Data will be collected from two primary sources: wearable devices and hospital clinical records. The wearable devices will continuously capture physiological and behavioral parameters, including physical activity, heart rate, sleep duration and quality, and skin temperature. Clinical data extracted from the hospital medical records will include vital signs—like respiratory rate, blood pressure, oxygen saturation (SpO2), temperature, and heart rate—along with comorbidities, past medical history, medication usage, clinician-documented diagnoses of delirium, and behavioral observations like confusion or agitation. These combined data will be assessed using novel Artificial Intelligence (AI) techniques to identify predictive patterns and early indicators of delirium.
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Timepoint [5]
448383
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Everyday from baseline till 30 days or till the patient is admitted at St Vincent's Hospital Melbourne, whichever is of lesser duration.
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Secondary outcome [6]
448384
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Classification of hyperactive states of delirium using AI techniques
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Assessment method [6]
448384
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Data will be collected from two primary sources: wearable devices and hospital clinical records. The wearable devices will continuously capture physiological and behavioral parameters, including physical activity, heart rate, sleep duration and quality, and skin temperature. Clinical data extracted from the hospital medical records will include vital signs—like respiratory rate, blood pressure, oxygen saturation (SpO2), temperature, and heart rate—along with comorbidities, past medical history, medication usage, clinician-documented diagnoses of delirium, and behavioral observations like confusion or agitation. These combined data will be assessed using novel Artificial Intelligence (AI) techniques to identify predictive patterns and early indicators of delirium.
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Timepoint [6]
448384
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Everyday from baseline till 30 days or till the patient is admitted at St Vincent's Hospital Melbourne, whichever is of lesser duration.
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Eligibility
Key inclusion criteria
Inpatients admitted to acute and sub-acute services of SVHM study sites (1. St Vincent's Hospital Melbourne (SVHM), 2. Bolt Wing (SVHM), 3. St George's Hospital, Kew)
People 45 years of age or greater in case of First Nations peoples, and 65 years of age or greater for other individuals
Inpatients with/without a current diagnosis of delirium
Inpatients diagnosed with dementia, as documented in their medical history or based on clinical evaluation, who are at risk for delirium
Inpatients without a history of dementia
Inpatients capable of and willing to wear the wearable device on their wrist
Inpatients, or their legal representative, must be able to provide informed consent to participate in the study (Inpatients not capable of providing informed consent may also have a legally authorized representative (for example, a next-of-kin, guardian, or medical treatment decision-maker) who can give consent on their behalf).
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Minimum age
45
Years
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Maximum age
No limit
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Sex
Both males and females
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Can healthy volunteers participate?
No
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Key exclusion criteria
Inpatients less than 45 years of age in case of First Nations peoples, and less than 65 years of age for other individuals
Individuals unable to provide informed consent; if a legally authorized representative (e.g., next-of-kin, guardian, or medical treatment decision-maker) also cannot provide consent on their behalf
Inability to wear the wearable device
Patients admitted under the direct care of mental health services or subject to an assessment or treatment order under the Mental Health and Wellbeing Act (Vic) 2022
Estimated to be admitted for less than one week
Patients under palliative care services or otherwise imminently approaching end-of-life
The treating team decides that it is not in the patient’s interest to participate in the study and/or might be detrimental to the patient
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Study design
Purpose
Natural history
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Duration
Longitudinal
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Selection
Random sample
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Timing
Both
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Statistical methods / analysis
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Recruitment
Recruitment status
Not yet recruiting
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Date of first participant enrolment
Anticipated
1/08/2025
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Actual
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Date of last participant enrolment
Anticipated
30/04/2027
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Actual
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Date of last data collection
Anticipated
30/07/2028
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Actual
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Sample size
Target
143
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Accrual to date
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Final
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Recruitment in Australia
Recruitment state(s)
VIC
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Recruitment hospital [1]
27967
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St Vincent's Hospital (Melbourne) Ltd - Fitzroy
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Recruitment postcode(s) [1]
44159
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3065 - Fitzroy
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Funding & Sponsors
Funding source category [1]
319030
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University
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Name [1]
319030
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RMIT University
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Address [1]
319030
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Country [1]
319030
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Australia
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Primary sponsor type
University
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Name
RMIT University
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Address
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Country
Australia
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Secondary sponsor category [1]
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None
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Name [1]
321495
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Address [1]
321495
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Country [1]
321495
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Ethics approval
Ethics application status
Submitted, not yet approved
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Ethics committee name [1]
317639
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St Vincent's Hospital Melbourne Human Research Ethics Committee
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Ethics committee address [1]
317639
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https://svhm.org.au/home/research/researchers/human-research-ethics-committee
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Ethics committee country [1]
317639
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Australia
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Date submitted for ethics approval [1]
317639
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28/02/2025
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Approval date [1]
317639
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Ethics approval number [1]
317639
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Summary
Brief summary
Delirium is a clinical condition characterized by an acute disturbance in a person's attention, cognition, and awareness. It can occur in any age group but is more common in older adults. It can be caused by the combined effect of hospital admissions, certain medications, and underlying medical conditions. Delirium is associated with poor outcomes for patients, with increased postoperative complications, delayed functional recovery, and prolonged hospital stays, and increased risk of death. Healthcare providers face difficulties in identifying or diagnosing delirium and providing high standard care for patients experiencing delirium. In this project, we will evaluate a comprehensive approach involving data from wearable devices and clinical records of patients to develop a predictive model to detect the onset of delirium and to classify the hypoactive and hyperactive states of delirium. The wearable device, which is a smartwatch designed for clinical trials, can monitor physical activity and physiological parameters in older adults, and the clinical records can provide valuable patient health information. This project aims to develop Artificial Intelligence (AI)-based predictive and diagnostic applications using the data collected from the wearable device worn by inpatients along with their clinical records to predict and classify delirium. This will help in timely identification and management of the underlying causes and risk factors, which are crucial in preventing the development of delirium and understanding its symptoms. The developed predictive and diagnostic applications could assist healthcare providers in enhancing health outcomes for older adults admitted to hospitals.
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Trial website
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Trial related presentations / publications
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Public notes
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Contacts
Principal investigator
Name
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Dr James Mahon
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Address
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St. Vincent's Hospital Melbourne, 41 Victoria Parade, Fitzroy, VIC - 3065
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Country
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Australia
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Phone
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+61 491721361
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Fax
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Email
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[email protected]
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Contact person for public queries
Name
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Dr Priya Rani
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Address
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RMIT University, Building 12 Level 11 Room 13-2, 124 La Trobe St, Melbourne , VIC - 3000
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Country
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Australia
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Phone
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+61 0410784111
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Fax
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Email
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[email protected]
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Contact person for scientific queries
Name
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Dr Priya Rani
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Address
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RMIT University, Building 12 Level 11 Room 13-2, 124 La Trobe St, Melbourne , VIC - 3000
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Country
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Australia
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Phone
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+61 0410784111
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Fax
141656
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Email
141656
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[email protected]
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Data sharing statement
Will the study consider sharing individual participant data?
No
What supporting documents are/will be available?
No Supporting Document Provided
Results publications and other study-related documents
Documents added manually
No documents have been uploaded by study researchers.
Documents added automatically
No additional documents have been identified.
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