Research on the Development and Validation of an Early Prediction Model for Delirium
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorRuijin Hospital
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Age ≥ 18 years, expected ICU stay ≥ 24 hours, and informed consent to participate in this study;
Disqualifiers
Patients with severe facial trauma/deformities that prevent complete expression acquisition, and patients with a history of emotional problems (such as anxiety, depression, etc.).
Trial 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.
Trial design
Design model
Case-control
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
795 Participants
are grouped into 2 trial groupsGroup A: observational
Group B: control
Trial outcomes
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
Time frame
7th day after ICU admissionSecondary outcomes
1
Accuracy
Zero is equivalent to the minimum accuracy, while a value of 1 represents perfect accuracy
Time frame
7th day after ICU admission2
Precision
The proportion of truly positive samples among those predicted as positive; the closer the score is to 1, the higher the precision
Time frame
7th day after ICU admission3
Recall
The proportion of truly positive samples that are correctly predicted; the closer the score is to 1, the higher the diagnostic sensitivity
Time frame
7th day after ICU admission4
F1-score
The harmonic mean of precision and recall; the higher the score, the better the diagnostic performance of the model
Time frame
7th day after ICU admissionLocations
Sponsors and contacts
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Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: