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NCT Number: NCT07337356

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

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.

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Key information

Conditions

Delirium Confusion Machine Learning Mental Disorders Nervous System Diseases Neurobehavioral Manifestations Neurocognitive Disorders Neurologic Manifestations Pathological Conditions, Signs and Symptoms Prediction Models Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

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

Treatment and study plan

Primary outcomes

  1. Number of participants with delirium as assessed by DSM-5

    Time frame: 7th day after ICU admission

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

Secondary outcomes

  1. Accuracy

    Time frame: 7th day after ICU admission

    Zero is equivalent to the minimum accuracy, while a value of 1 represents perfect accuracy

  2. Precision

    Time frame: 7th day after ICU admission

    The proportion of truly positive samples among those predicted as positive; the closer the score is to 1, the higher the precision

  3. Recall

    Time frame: 7th day after ICU admission

    The proportion of truly positive samples that are correctly predicted; the closer the score is to 1, the higher the diagnostic sensitivity

  4. F1-score

    Time frame: 7th day after ICU admission

    The harmonic mean of precision and recall; the higher the score, the better the diagnostic performance of the model

Study contacts

Contact information is provided by the study sponsor or research team.

weiqing Zhang Ph.D, Ph.D

CONTACT

[email protected]

8618521525300

Sponsors and collaborators

Lead sponsor

Ruijin Hospital

Other

Registry information

Official study title

Research on the Development and Validation of an Early Prediction Model for Delirium Based on Machine Vision Analysis

Important dates

Study start
2026
Primary completion
2026
Study completion
2027
First posted
Jan 13, 2026
Registry last updated
Jan 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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