Clinical trial NCT05303051: 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
Overall status: WITHDRAWN
Phase: NA
Conditions: Diabetes
Lead sponsor: University of California, San Francisco
Sponsor class: OTHER
Collaborators: name: Azumio Inc.
name: Bristol-Myers Squibb
First posted: 2022-03-31
Enrollment: 0
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.


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

Primary outcomes:
measure: 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.
timeFrame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
measure: 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.
timeFrame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
measure: Assess the performance of the DNN score in different ethnicity and skin tones
timeFrame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
Secondary outcomes:
measure: The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement based on > 2 PPG measurements.
timeFrame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
measure: The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based on >2 PPG measurements.
timeFrame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement
measure: Retrain the DNN algorithm
timeFrame: Retraining to occur after complete collection of PPG measurements and HBA1c data. The investigators estimate this will occur one year after enrollment.
Other outcomes:
Locations:
city: San Francisco
country: United States
facility: University of California, San Francisco
state: California
Study officials:
affiliation: University of California, San Franscisco
name: Geoff Tison, MD, MPH
role: PRINCIPAL_INVESTIGATOR
References:
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.