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

NCT ID: NCT05303051

Last Updated: 2025-04-08

Basic Information

• Recruitment Status: WITHDRAWN
• Clinical Phase: NA
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-06-01
• Study Completion Date: 2025-04-01

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.

Conditions

Diabetes

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

Study Population

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.

Group Type: EXPERIMENTAL

Application Validation

Intervention Type: DEVICE

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 .

Alternative Sample Group

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.

Group Type: EXPERIMENTAL

Application Validation

Intervention Type: DEVICE

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 .

Interventions

Application Validation

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 .

Intervention Type: DEVICE

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Azumio Inc.

UNKNOWN

Sponsor Role: collaborator

Bristol-Myers Squibb

INDUSTRY

Sponsor Role: collaborator

University of California, San Francisco

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Geoff Tison, MD, MPH

Role: PRINCIPAL_INVESTIGATOR

University of California, San Franscisco

Locations

University of California, San Francisco

San Francisco, California, United States

Countries

United States

References

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.

Reference Type: BACKGROUND

PMID: 32807931 (View on PubMed)

Other Identifiers

21-35207

Identifier Type: -

Identifier Source: org_study_id