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Background/Objective: One in five youth with T1D experience worsening A1c values between quarterly visits. We evaluated the effectiveness of Remote Patient Monitoring (RPM), a direct-to-consumer telehealth intervention offering problem-solving and education to assess glucose patterns for youth predicted by a machine learning model to experience a significant rise in A1c 70-180 days following routine clinical visits.
Methods: Patients received care at a tertiary diabetes clinic in the U.S. Midwest. Supervised machine learning was used to develop a random forest-based model to predict 90-day change in A1c. Clinic staff reviewed weekly lists of patients with a predicted 90-day rise in A1c of ≥3 mmol/mol. From these lists, 69 patients under 20 years old with baseline A1c ≥55 mmol/mol were enrolled in RPM. Youth received 1-6 brief telehealth sessions with trained interventionists over 90-day before their next routine clinic visit. Families reviewed device data with interventionists during each session and received personalized insulin regimen adjustments and problem-solving support.
Results: Study cohort was 77% white, 8% Hispanic, 54% female, 36% on insulin pump & CGM, median age 14.32 years (IQR = 11.28, 16.41), baseline A1c 64 mmol/mol (58, 73), and follow-up A1c 67 mmol/mol (61,79). Sixty-two percent of the 69 RPM patients did not have A1c rise ≥3 mmol/mol compared to 53% of the 524 non-RPM patients (P = .155).
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