PREsurgical Cognitive Evaluation Via Digital clockfacEdrawing
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: digital cognitive testing.
- Who it may be relevant to
- Registry conditions: Cognitive Dysfunction. Basic parameters: from 65 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
This study leverages a modernized digital version of a well-known cognitive screening tool to examine pre and post operative cognitive function after surgery in adults age 65 years or more. Machine learning algorithms will be applied to the hospital wide standard of care cognitive metric to identify risk for post-operative cognitive complications.
Detailed description
This proposal innovatively leverages a brief but informative digital test with machine learning to examine the subtlety of pre-surgery cognition within an extremely large number of older individuals screened preoperatively within an academic tertiary medical center. It also incorporates a unique group of well characterized non-surgery peers for demographic matching to assist with normal versus abnormal machine learning analyses.
Interventions
- Behavioral digital cognitive testing
The digital testing is hypothesized to identify latent features for differentiating cognitively impaired presurgical patient subgroups
Primary outcome measures
- Control and pre-surgery differences between digital behaviors [Time frame: up to one year]
Secondary outcome measures (4)
- Predictive validity of digital behaviors on outcome [Time frame: up to 1 year]
- Change over time in digital behavior between groups [Time frame: up to 6-weeks]
- Change over time in digital behavior between groups [Time frame: up to 3-months]
- Change over time in digital behavior between groups [Time frame: up to one year]
Eligibility criteria
Inclusion criteria
- >/= 65 years of age
- screening within the University of Florida (UF) Health Preoperative clinic
- presurgical cognitive screening with the digital Clock Drawing Tool (dCDT)
Exclusion criteria
- < 65 years of age
- did not complete screening within the UF Health Preoperative clinic
- did not complete the presurgical cognitive screening with the digital Clock Drawing Tool (dCDT)
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
United States · 1 center
- UF Health — Gainesville
Publications
- Bandyopadhyay S, Wittmayer J, Libon DJ, Tighe P, Price C, Rashidi P. Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands. Sci Rep. 2023 May 6;13(1):7384. doi: 10.1038/s41598-023-34518-9. PMID 37149670
- Bandyopadhyay S, Dion C, Libon DJ, Price C, Tighe P, Rashidi P. Variational autoencoder provides proof of concept that compressing CDT to extremely low-dimensional space retains its ability of distinguishing dementia. Sci Rep. 2022 May 14;12(1):7992. doi: 10.1038/s41598-022-12024-8. PMID 35568709
Identifiers
NCT: NCT03175302 · IRB201700747-N · R01AG055337 · OCR18881