Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening
Study Details
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
Study Design
Arms and Interventions
| 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 .
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| 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 .
|
Outcome Measures
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. [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.
- 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. [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.
- Assess the performance of the DNN score in different ethnicity and skin tones [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
- The area under the receiver operating characteristic (AUROC) of the DNN Score as compared with one HBA1c measurement based on > 2 PPG measurements. [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.
- The Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value of the DNN Score as compared with one HBA1c measurement based on >2 PPG measurements. [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.
- Retrain the DNN algorithm [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
Eligibility Criteria
Criteria
Inclusion Criteria:
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Age > 18 years old
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Participants without a prior diagnosis of DM
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Participants with a recently measured HBA1c one month before enrollment or scheduled to undergo a HBA1c measurement within one month after enrollment
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Participants not scheduled for HBA1c and are willing to undergo a lab measured HBA1c
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Participants without risk factors for DM
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Participants with > 1 of the following risk factors for DM:
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Age > 40 years old
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Obesity (BMI > 30)
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Family history: Any first degree relative with a hx of DM
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Lifestyle risk factors (exercise, smoking, and sleep duration)
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Ownership of a smart phone
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Able to provide informed consent
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Willingness to provide PPG waveforms
Exclusion Criteria:
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Participants with a history of DM
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Participants with a prior HBA1c > 6.5%
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Inability to collect PPG signals (digit amputation, excessive tremors, etc)
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Lack of ownership of a smartphone
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Inability or unwillingness to consent and/or follow requirements of the study
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | University of California, San Francisco | San Francisco | California | United States | 94143 |
Sponsors and Collaborators
- University of California, San Francisco
- Azumio Inc.
Investigators
- Principal Investigator: Geoff Tison, MD, MPH, University of California, San Franscisco
Study Documents (Full-Text)
None provided.More Information
Publications
- 21-35207