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

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ClinicalTrials.gov Identifier: NCT05303051
Recruitment Status : Not yet recruiting
First Posted : March 31, 2022
Last Update Posted : July 7, 2022
Sponsor:
Collaborator:
Azumio Inc.
Information provided by (Responsible Party):
University of California, San Francisco

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.

Condition or disease Intervention/treatment Phase
Diabetes Device: Application Validation Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 6006 participants
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening
Estimated Study Start Date : July 2022
Estimated Primary Completion Date : April 2023
Estimated Study Completion Date : December 2023

Arm Intervention/treatment
Experimental: 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.
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 .

Experimental: 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.
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 .




Primary Outcome Measures :
  1. 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. [ Time Frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement ]
    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.

  2. 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. [ Time Frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement ]
    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.

  3. Assess the performance of the DNN score in different ethnicity and skin tones [ Time Frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement ]
    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 :
  1. The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement based on > 2 PPG measurements. [ Time Frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement ]
    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.

  2. The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based on >2 PPG measurements. [ Time Frame: PPG measurements and DNN score to be obtained within one month oh HBA1c measurement ]
    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.

  3. Retrain the DNN algorithm [ Time Frame: Retraining to occur after complete collection of PPG measurements and HBA1c data. The investigators estimate this will occur one year after enrollment. ]
    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



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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05303051


Contacts
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Contact: Geoff Tison, MD, MPH (415) 353-2873 geoff.tison@ucsf.edu
Contact: Mattheus Ramsis, MD mattheus.ramsis@ucsf.edu

Locations
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United States, California
University of California, San Francisco
San Francisco, California, United States, 94143
Contact: Geoff Tison, MD MPH       geoff.tison@ucsf.edu   
Sub-Investigator: Mattheus Ramsis, MD         
Sponsors and Collaborators
University of California, San Francisco
Azumio Inc.
Investigators
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Principal Investigator: Geoff Tison, MD, MPH University of California, San Franscisco
Publications:
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Responsible Party: University of California, San Francisco
ClinicalTrials.gov Identifier: NCT05303051    
Other Study ID Numbers: 21-35207
First Posted: March 31, 2022    Key Record Dates
Last Update Posted: July 7, 2022
Last Verified: July 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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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
Additional relevant MeSH terms:
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Diabetes Mellitus
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases