Evidence map›Paper›PMID 40998312›Full record

ArticleJMIR research protocols2025

Harnessing the Power of Technology to Transform Delirium Severity Measurement in the Intensive Care Unit: Protocol for a Prospective Cohort Study.

Roshini Raghu, Keivan Nalaie, Ivan Ayala, Juan Jose Morales Behaine, Juan Pablo Garcia-Mendez, Hannah Friesen, Kathleen Leistikow, Anirban Bhattacharyya, Arun Jayaraman, Pablo Moreno Franco and 5 more

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT06172491 recruitingnot on this map

Harnessing the Power of Technology to Transform Delirium Severity Measurement in the ICU

TypeobservationalSponsorMayo ClinicRan2023 to 2028Enrolled400ConditionsIntensive Care Unit Delirium, Encephalopathy, Delirium
3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Video-based Detection of Delirium in Hospitalized Adults.medRxiv : the preprint server for health sciences · 2026
    Article
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Roshini Raghu *Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0002-9001-4080
Keivan Nalaie *Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN, United States.ORCID 0009-0008-9579-374X
Ivan Ayala *Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0002-2997-3848
Juan Jose Morales Behaine *Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Phoenix, AZ, United States.ORCID 0009-0000-6971-2936
Juan Pablo Garcia-Mendez *Division of Critical Care, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0002-4554-3186
Hannah Friesen *Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN, United States.ORCID 0009-0000-1971-5522
Kathleen Leistikow *Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN, United States.
Anirban Bhattacharyya *Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States.ORCID 0000-0002-1646-709X
Arun Jayaraman *Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Phoenix, AZ, United States.ORCID 0009-0000-7770-1938
Pablo Moreno Franco *Department of Critical Care Medicine, Mayo Clinic, Jacksonville, FL, United States.ORCID 0000-0001-9146-5599
Alejandro Rabinstein *Department of Neurology, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0001-8359-2838
Linda L Chlan *Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0001-6922-7551
Malaz Boustani *Center for Health Innovation and Implementation Science, School of Medicine, Indiana University, Indianapolis, IN, United States.ORCID 0000-0003-0330-677X
Vitaly Herasevich *Division of Critical Care, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0002-0214-0651
Heidi Lindroth *Division of Nursing Research, Department of Nursing, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0002-5389-4701

Funding

Harnessing the Power of Technology to Transform Delirium Severity Measurement in the ICUK23AG076662 · NIA · MAYO CLINIC ROCHESTER · PI Heidi Lindroth · 2022 to 2026
$815k
NIA NIH HHS K23 AG076662NIA NIH HHS L30 AG074162
6 · The paper itself

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.

Indexed as

DeliriumIntensive Care UnitsSeverity of Illness IndexElectronic Health RecordsFemaleHumansLongitudinal StudiesMaleMiddle AgedObservational Studies as TopicProspective StudiesAlzheimer’s diseaseartificial intelligencecomputer vision technologydelirium severityICU deliriumintensive care unitother related dementiaspassive digital marker

Identifiers

PMID40998312
PMCPMC12511816

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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.