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Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening

Primary Purpose

Diabetes

Status
Not yet recruiting
Phase
Not Applicable
Locations
United States
Study Type
Interventional
Intervention
Application Validation
Sponsored by
University of California, San Francisco
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional diagnostic trial for Diabetes

Eligibility Criteria

18 Years - undefined (Adult, Older Adult)All SexesAccepts Healthy Volunteers

Inclusion Criteria:

  • Age > 18 years old
  • Participants without a prior diagnosis of DM
  • Participants with a recently measured HBA1c one month before enrollment or scheduled to undergo a HBA1c measurement within one month after enrollment
  • Participants not scheduled for HBA1c and are willing to undergo a lab measured HBA1c
  • Participants without risk factors for DM
  • Participants with > 1 of the following risk factors for DM:
  • Age > 40 years old
  • Obesity (BMI > 30)
  • Family history: Any first degree relative with a hx of DM
  • Lifestyle risk factors (exercise, smoking, and sleep duration)
  • Ownership of a smart phone
  • Able to provide informed consent
  • Willingness to provide PPG waveforms

Exclusion Criteria:

  • Participants with a history of DM
  • Participants with a prior HBA1c > 6.5%
  • Inability to collect PPG signals (digit amputation, excessive tremors, etc)
  • Lack of ownership of a smartphone
  • Inability or unwillingness to consent and/or follow requirements of the study

Sites / Locations

  • University of California, San Francisco

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Experimental

Arm Label

Study Population

Alternative Sample Group

Arm Description

The investigators will conduct an electronic medical record (EMR) query of individuals in the University of California, San Francisco (UCSF) primary care clinics without a prior diagnosis of DM and who are undergoing, or who have recently undergone, a lab measured HBA1c before or after 1 month of enrollment. sample size estimation for testing the estimated AUROC in the validation sample vs. the null value of AUC 0.7. The investigators will target an enrollment of 5006 subjects in order to obtain a pre-specified AUROC 95% confidence interval width of 0.07 (i.e. AUROC = 0.76 [95%CI 0.725, 0.795]). The investigators assume that ~4% of the cohort will have undiagnosed diabetes based on national prevalence estimates.

The investigators also aim to perform a sensitivity analysis to estimate the DNN performance in a target general population without a diabetes diagnosis. The investigators will recruit patients from the UCSF EHR system without a history of diabetes, no prior HBA1c measured, and no history of known diabetic risk factors. The investigators will target an enrollment of 1000 subjects in order to obtain a pre-specified AUROC 95% confidence interval width of 0.18 (i.e. AUROC = 0.76 [95%CI 0.67, 0.85]). The investigators assume that ~3% of the cohort will have undiagnosed diabetes based on national prevalence estimates.

Outcomes

Primary Outcome Measures

The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement, based an average of two PPG measurements.
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported a as a DNN score. The investigators will assess the DNN performance by the the area under the receiver operating characteristic (AUROC) of the DNN Score as compared with the HBA1c based on the DNN score from an average of 2 PPG measurements.
The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based an average of two PPG measurements.
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported as a DNN score. The investigators will assess the DNN performance by the Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with the HBA1c based on the DNN score from an average of 2 PPG measurements.
Assess the performance of the DNN score in different ethnicity and skin tones
The investigators will aim to recruit individuals of different races/ethnicities and skin tones to assess the performance of the DNN score in different races/ethnicities.

Secondary Outcome Measures

The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement based on > 2 PPG measurements.
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported a as a DNN score. The investigators will assess the DNN performance the area under the receiver operating characteristic (AUROC) of the DNN Score of > 2 PPG measurements as compared with the HBA1c.
The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based on >2 PPG measurements.
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported a as a DNN score. The investigators will assess the DNN performance by the Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score of > 2 PPG measurements as compared with the HBA1c.
Retrain the DNN algorithm
By collecting PPG waveform data in patients with laboratory-confirmed diabetes, the investigators will be able to train the algorithm using the more specific diagnosis of laboratory-confirmed diabetes. The investigators will assess the performance of the DNN Score once retrained using HbA1c. The DNN will be trained using similar approaches as the investigators have previously published

Full Information

First Posted
March 10, 2022
Last Updated
June 5, 2023
Sponsor
University of California, San Francisco
Collaborators
Azumio Inc., Bristol-Myers Squibb
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1. Study Identification

Unique Protocol Identification Number
NCT05303051
Brief Title
Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening
Official Title
Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening
Study Type
Interventional

2. Study Status

Record Verification Date
June 2023
Overall Recruitment Status
Not yet recruiting
Study Start Date
July 2023 (Anticipated)
Primary Completion Date
July 2024 (Anticipated)
Study Completion Date
July 2024 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
University of California, San Francisco
Collaborators
Azumio Inc., Bristol-Myers Squibb

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
Yes
Device Product Not Approved or Cleared by U.S. FDA
Yes
Product Manufactured in and Exported from the U.S.
No

5. Study Description

Brief Summary
The Validation of the Diabetes Deep Neural Network Score (DNN score) for Screening for Type 2 Diabetes Mellitus (diabetes) is a single center, unblinded, observational study to clinically validating a previously developed remote digital biomarker, identified as the DNN score, to screen for diabetes. The previously developed DNN score provides a promising avenue to detect diabetes in these high-risk communities by leveraging photoplethysmography (PPG) technology on the commercial smartphone camera that is highly accessible. Our primary aim is to prospectively clinically validate the PPG DNN algorithm against the reference standards of glycated hemoglobin (HbA1c) for the presence of prevalent diabetes. Our vision is that this clinical trial may ultimately support an application to the Food and Drug Administration so that it can be incorporated into guideline-based screening.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Diabetes

7. Study Design

Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Non-Randomized
Enrollment
6006 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Study Population
Arm Type
Experimental
Arm Description
The investigators will conduct an electronic medical record (EMR) query of individuals in the University of California, San Francisco (UCSF) primary care clinics without a prior diagnosis of DM and who are undergoing, or who have recently undergone, a lab measured HBA1c before or after 1 month of enrollment. sample size estimation for testing the estimated AUROC in the validation sample vs. the null value of AUC 0.7. The investigators will target an enrollment of 5006 subjects in order to obtain a pre-specified AUROC 95% confidence interval width of 0.07 (i.e. AUROC = 0.76 [95%CI 0.725, 0.795]). The investigators assume that ~4% of the cohort will have undiagnosed diabetes based on national prevalence estimates.
Arm Title
Alternative Sample Group
Arm Type
Experimental
Arm Description
The investigators also aim to perform a sensitivity analysis to estimate the DNN performance in a target general population without a diabetes diagnosis. The investigators will recruit patients from the UCSF EHR system without a history of diabetes, no prior HBA1c measured, and no history of known diabetic risk factors. The investigators will target an enrollment of 1000 subjects in order to obtain a pre-specified AUROC 95% confidence interval width of 0.18 (i.e. AUROC = 0.76 [95%CI 0.67, 0.85]). The investigators assume that ~3% of the cohort will have undiagnosed diabetes based on national prevalence estimates.
Intervention Type
Device
Intervention Name(s)
Application Validation
Intervention Description
After creating accounts, participants in both groups will download the Azumio Instant Diabetes Test and provide a Photoplethysmography (PPG) waveforms by placing their index finger over their smartphone camera for 20 seconds to provide PPG waveform data for the study .
Primary Outcome Measure Information:
Title
The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement, based an average of two PPG measurements.
Description
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported a as a DNN score. The investigators will assess the DNN performance by the the area under the receiver operating characteristic (AUROC) of the DNN Score as compared with the HBA1c based on the DNN score from an average of 2 PPG measurements.
Time Frame
PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Title
The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based an average of two PPG measurements.
Description
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported as a DNN score. The investigators will assess the DNN performance by the Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with the HBA1c based on the DNN score from an average of 2 PPG measurements.
Time Frame
PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Title
Assess the performance of the DNN score in different ethnicity and skin tones
Description
The investigators will aim to recruit individuals of different races/ethnicities and skin tones to assess the performance of the DNN score in different races/ethnicities.
Time Frame
PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Secondary Outcome Measure Information:
Title
The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement based on > 2 PPG measurements.
Description
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported a as a DNN score. The investigators will assess the DNN performance the area under the receiver operating characteristic (AUROC) of the DNN Score of > 2 PPG measurements as compared with the HBA1c.
Time Frame
PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Title
The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based on >2 PPG measurements.
Description
Participants will provide seven total PPG measurements by their own smartphone camera. After PPG measurements are obtained, the DNN algorithm will be deployed and be reported a as a DNN score. The investigators will assess the DNN performance by the Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score of > 2 PPG measurements as compared with the HBA1c.
Time Frame
PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Title
Retrain the DNN algorithm
Description
By collecting PPG waveform data in patients with laboratory-confirmed diabetes, the investigators will be able to train the algorithm using the more specific diagnosis of laboratory-confirmed diabetes. The investigators will assess the performance of the DNN Score once retrained using HbA1c. The DNN will be trained using similar approaches as the investigators have previously published
Time Frame
Retraining to occur after complete collection of PPG measurements and HBA1c data. The investigators estimate this will occur one year after enrollment.

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria: Age > 18 years old Participants without a prior diagnosis of DM Participants with a recently measured HBA1c one month before enrollment or scheduled to undergo a HBA1c measurement within one month after enrollment Participants not scheduled for HBA1c and are willing to undergo a lab measured HBA1c Participants without risk factors for DM Participants with > 1 of the following risk factors for DM: Age > 40 years old Obesity (BMI > 30) Family history: Any first degree relative with a hx of DM Lifestyle risk factors (exercise, smoking, and sleep duration) Ownership of a smart phone Able to provide informed consent Willingness to provide PPG waveforms Exclusion Criteria: Participants with a history of DM Participants with a prior HBA1c > 6.5% Inability to collect PPG signals (digit amputation, excessive tremors, etc) Lack of ownership of a smartphone Inability or unwillingness to consent and/or follow requirements of the study
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Geoff Tison, MD, MPH
Phone
(415) 353-2873
Email
geoff.tison@ucsf.edu
First Name & Middle Initial & Last Name or Official Title & Degree
Mattheus Ramsis, MD
Email
mattheus.ramsis@ucsf.edu
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Geoff Tison, MD, MPH
Organizational Affiliation
University of California, San Franscisco
Official's Role
Principal Investigator
Facility Information:
Facility Name
University of California, San Francisco
City
San Francisco
State/Province
California
ZIP/Postal Code
94143
Country
United States
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Geoff Tison, MD MPH
Email
geoff.tison@ucsf.edu
First Name & Middle Initial & Last Name & Degree
Mattheus Ramsis, MD

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
32807931
Citation
Avram R, Olgin JE, Kuhar P, Hughes JW, Marcus GM, Pletcher MJ, Aschbacher K, Tison GH. A digital biomarker of diabetes from smartphone-based vascular signals. Nat Med. 2020 Oct;26(10):1576-1582. doi: 10.1038/s41591-020-1010-5. Epub 2020 Aug 17.
Results Reference
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Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening

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