PREsurgical Cognitive Evaluation Via Digital clockfacEdrawing (PRECEDE)

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.

Study Overview

• Status: Recruiting
• Conditions: Cognitive Dysfunction
• Intervention / Treatment: Behavioral: digital cognitive testing

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.

• Study Type: Observational
• Enrollment (Estimated): 25240

Contacts and Locations

• Study Contact: Name: Catherine Price, Ph.D.; Phone Number: 352-494-6999; Email: cep23@phhp.ufl.edu
• Study Contact Backup: Name: Amy Gunnett, RN; Phone Number: 352-273-8911; Email: agunnett@anest.ufl.edu

Study Locations

• United States
  • Florida
    • Gainesville, Florida, United States, 32610
      • Recruiting
      • UF Health
      • Contact: Catherine Price, Ph.D.; Phone Number: 352-494-6999; Email: cep23@phhp.ufl.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 65 years and older (Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients >/= 65 years of age scheduled for screening within the UF Health Preoperative clinic

Description

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)

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Surgical group; Baseline preoperative digital cognitive testing performance in adults to predict frequency and severity of clinician reported outcomes within the first three months post-surgery.	Behavioral: digital cognitive testing; The digital testing is hypothesized to identify latent features for differentiating cognitively impaired presurgical patient subgroups
Control; Non-surgery matched peers with the same testing.	Behavioral: digital cognitive testing; The digital testing is hypothesized to identify latent features for differentiating cognitively impaired presurgical patient subgroups

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Control and pre-surgery differences between digital behaviors	Measure range of digital outcome differences	up to one year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Predictive validity of digital behaviors on outcome	Digital tools will predict clinician reported events	up to 1 year
Change over time in digital behavior between groups	Surgery group and control group differences from baseline to 6-weeks	up to 6-weeks
Change over time in digital behavior between groups	Surgery group and control group differences from baseline to 3-months	up to 3-months
Change over time in digital behavior between groups	Surgery group and control group differences from baseline to one year	up to one year

Collaborators and Investigators

• Sponsor: University of Florida
• Collaborators: National Institute on Aging (NIA); National Center for Advancing Translational Sciences (NCATS)
• Investigators: Principal Investigator: Catherine Price, Ph.D., University of Florida; Principal Investigator: Patrick Tighe, MD, MS, University of Florida

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): June 28, 2018
• Primary Completion (Estimated): May 31, 2027
• Study Completion (Estimated): May 31, 2027

Study Registration Dates

• First Submitted: June 1, 2017
• First Submitted That Met QC Criteria: June 1, 2017
• First Posted (Actual): June 5, 2017

Study Record Updates

• Last Update Posted (Actual): July 31, 2025
• Last Update Submitted That Met QC Criteria: July 28, 2025
• Last Verified: July 1, 2025

More Information

Terms related to this study

• Keywords: neurocognitive impairment; cognitive processes; perioperative cognition
• Additional Relevant MeSH Terms: Mental Disorders; Neurocognitive Disorders; Cognition Disorders; Cognitive Dysfunction
• Other Study ID Numbers: IRB201700747-N; R01AG055337 (U.S. NIH Grant/Contract); OCR18881 (Other Identifier: University of Florida)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No