Research on the Development and Validation of an Early Prediction Model for Delirium

NCT07337356 | Not Yet Recruiting

• 1 other identifier: ZWQ21886-2025-EC-579
• Study Type: observational
• Target: 795
• Locations: 0 countries
• Sites: N/A

Brief Summary

Delirium has a high incidence rate and significantly affects patient prognosis. Diagnosis often relies on manual assessment, which is subject to strong subjectivity, high rates of missed diagnosis, and poor stability. This study employs non-contact identification technology based on machine vision analysis to quantitatively analyze characteristic biological feature data such as micro-expressions. It then investigates the correlation between these features and delirium subtypes. By integrating clinical phenotypic data and using machine learning algorithms, a multi-modal early prediction model for delirium is constructed to meet the clinical need for early warning of delirium subtypes and enhance the efficacy of delirium identification.

Conditions

Delirium; Prediction Models; Machine Learning

Outcome Measures

Primary Outcomes (1)

• Number of participants with delirium as assessed by DSM-5

  Zero is equivalent to no delirium and a high score means a higher occurrence of delirium

  7th day after ICU admission

Secondary Outcomes (4)

• Accuracy

  7th day after ICU admission

• Precision

  7th day after ICU admission

• Recall

  7th day after ICU admission

• F1-score

  7th day after ICU admission

Study Arms (2)

observational

1. Meets the delirium diagnostic criteria specified in the Diagnostic and Statistical Manual of Mental Disorders (5th Edition) (DSM-5), which requires the concurrent presence of: ① disturbance in awareness (reduced clarity of awareness of the environment) ; ② change in cognition (e.g., memory impairment, disorientation); 2. Undergoes consecutive daily assessments for 7 days using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) at three time points (8:00, 14:00, 20:00) with an interval of ≥ 6 hours between each assessment, with at least two positive results;

control

Admitted to the ICU during the same period, with negative results on consecutive daily CAM-ICU assessments for 3 days (three assessments per day as the observational group).

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients (≥18 years old) admitted to the ICU of Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, including elderly patients, those post-surgery, with sepsis, and trauma, which are common high-risk populations for delirium.

You may qualify if:

• Age ≥ 18 years, expected ICU stay ≥ 24 hours, and informed consent to participate in this study;

You may not qualify if:

• Patients with severe facial trauma/deformities that prevent complete expression acquisition, and patients with a history of emotional problems (such as anxiety, depression, etc.).

Sponsors & Collaborators

• Ruijin Hospital: lead

MeSH Terms

Conditions

Delirium

Condition Hierarchy (Ancestors)

Confusion; Neurobehavioral Manifestations; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Neurocognitive Disorders; Mental Disorders

Central Study Contacts

weiqing Zhang Ph.D, Ph.D

CONTACT

8618521525300; weiq.zh@163.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 7 Days
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 4, 2026
• First Posted: January 13, 2026
• Study Start: February 1, 2026
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): February 1, 2027
• Last Updated: January 13, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share