University of California, San Francisco
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.
Diabetes
Application Validation
Not Applicable
| Study Type : | Interventional |
| Estimated Enrollment : | 6006 participants |
| Masking : | None (Open Label) |
| Primary Purpose : | Diagnostic |
| Official Title : | Validation of the Diabetes Deep Neural Network Score for Diabetes Mellitus Screening |
| Actual Study Start Date : | June 1, 2023 |
| Estimated Primary Completion Date : | July 2024 |
| Estimated Study Completion Date : | July 2024 |
| Arm | Intervention/treatment |
|---|---|
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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 |
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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 |
| Ages Eligible for Study: | 18 Years |
| Sexes Eligible for Study: | All |
| Accepts Healthy Volunteers: | Accepts Healthy Volunteers |
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Not yet recruiting
University of California, San Francisco
San Francisco, California, United States, 94143