ArticleJMIR research protocols2025
Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.
Article in JMIR research protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06172491 (Harnessing the Power of Technology to Transform Delirium Severity Measurement in the ICU), which is not on this map. Cited by 4 papers.
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Harnessing the Power of Technology to Transform Delirium Severity Measurement in the ICU
Who cites it
4 citing papers in PubMed.
- Video-based Detection of Delirium in Hospitalized Adults.medRxiv : the preprint server for health sciences · 2026Article
- Video-based detection of Delirium in hospitalized adults.PLOS digital health · 2026Article
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Review
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Authors and funding
15 authors.
Funding
Abstract
backgroundDelirium, an acute brain dysfunction, is a complication in up to 50% of patients in the intensive care unit (ICU). Measuring and mitigating delirium severity can reduce associated morbidity and improve long-term health outcomes post discharge. However, the perceived complexity of the available delirium detection tools and clinical workload limits the routine assessment of delirium severity. Developing a passive digital marker for delirium severity, combining routine electronic health record (EHR) and computer vision technology data, could be an implementable, scalable, and sustainable approach.
objectiveOur primary objective is to develop a passive digital marker for delirium severity (PDM-Del) and examine its performance in comparison to validated delirium severity tools. Our secondary objective is to evaluate the acceptability and usability of the PDM-Del by patients, families, and clinicians.
methodsWe will conduct a prospective, longitudinal cohort study to develop a PDM-Del using computer vision data and routinely collected EHR data. Following informed consent, the study team will collect image data through continuous digital video recordings of adult patients (>50 years) in their ICU room, routine EHR data (demographic and clinical variables), and administer delirium severity assessments (4 times daily) until ICU discharge or death. We will examine the usability and acceptability of the developed PDM-Del by patients, families, and direct care clinicians in a pilot randomized controlled clinical trial (aim 3). Descriptive statistics (means, SDs, medians, IQRs, and frequencies) and statistical differences between study instruments will be examined. We will use convolutional neural networks and machine learning to inform model development, testing, and validation. We will report model performance statistics, including accuracy, precision, recall, and the F
resultsWe are currently in the recruitment and data collection phase. As of March 2025, we screened 3980 patients (32% eligible, n=1307), approached 665 (50%), and enrolled 150 participants (23% enrollment rate). Among the 150 patients, the median age was 67 (IQR 61-74) years, 62% (93/150) were male, and 91% (136/150) were White.
conclusionsThe PDM-Del could provide real-time, actionable feedback to direct care clinicians on the brain health of patients in the ICU. Early mitigation of delirium severity may decrease the risk of mortality, future Alzheimer disease and related dementia, and length of hospital stay.
trial registrationClinicalTrials.gov NCT06172491; https://clinicaltrials.gov/study/NCT06172491. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/62912.
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