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

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07337356

Enrollment

795

Registered

2026-01-13

Start date

2026-02-01

Completion date

2027-02-01

Last updated

2026-01-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Delirium, Machine Learning, Prediction Models

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.

Interventions

None listed

Study design

Observational model

CASE_CONTROL

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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.).

Design outcomes

Primary

Measure	Time frame	Description
Number of participants with delirium as assessed by DSM-5	7th day after ICU admission	Zero is equivalent to no delirium and a high score means a higher occurrence of delirium

Secondary

Measure	Time frame	Description
Accuracy	7th day after ICU admission	Zero is equivalent to the minimum accuracy, while a value of 1 represents perfect accuracy
Precision	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
Recall	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
F1-score	7th day after ICU admission	The harmonic mean of precision and recall; the higher the score, the better the diagnostic performance of the model

Contacts

Primary Contactweiqing Zhang Ph.D, Ph.D

weiq.zh@163.com8618521525300

Outcome results