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

Diabetes Clinical Trial

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05303051
• Other study ID #: 21-35207
• Status: Active, not recruiting
• Phase: N/A
• Start date: June 1, 2023
• Est. completion date: July 2024

Study information

• Verified date: January 2024
• Source: University of California, San Francisco
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 6006
• Est. completion date: July 2024
• Est. primary completion date: July 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Diabetes
• Diabetes Mellitus

Intervention

Device:
• 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 .

Locations

Country	Name	City	State
United States	University of California, San Francisco	San Francisco	California

Sponsors (3)

Lead Sponsor	Collaborator
University of California, San Francisco	Azumio Inc., Bristol-Myers Squibb

Country where clinical trial is conducted

United States, 

References & Publications (1)

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Primary	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.	PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Primary	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.	PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Secondary	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.	PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Secondary	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.	PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Secondary	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	Retraining to occur after complete collection of PPG measurements and HBA1c data. The investigators estimate this will occur one year after enrollment.