Professor and Pediatric Endocrinologist, Chief Medical Officer
Pediatric Endocrinology
Dr. Mark Clements is Chief Medical Officer at Glooko Inc and is a Professor of Pediatrics at the University of Missouri-Kansas City School of Medicine. He is also a clinical researcher of new diabetes treatments and technologies, having served as a principal investigator or co-investigator in more than 30 clinical studies and patient registries. He has served as chair for the Type 1 Diabetes Exchange Clinic Registry, and currently serves as data science co-lead for the Type 1 Diabetes Exchange Quality Improvement Collaborative. Dr. Clements’ research interests include studying “big data in type 1 diabetes (including electronic health records data, national and international registry data), understanding the relationship between glucose variability and diabetes-related complications, the identification of novel predictors of risk for diabetes-related complications, prevention of type 1 diabetes (through participation in TrialNet), the use of advanced machine learning and natural language processing to quality-improve type 1 diabetes care, the development of mHealth/digital health interventions to improve type 1 diabetes care, and health system interventions to promote data mobility in type 1 diabetes care.

Moderator of 1 Session

Presenter of 4 Presentations

Using Digital Health Technology to Prevent and Treat Diabetes

Session Type
Plenary Session
Date
Fri, 29.04.2022
Session Time
13:00 - 14:30
Room
Hall 116
Lecture Time
13:24 - 13:30

The Use of AI, Machine Learning, and Digital Therapeutics for Behavioral Modification

Session Type
Industry Symposium
Date
Wed, 27.04.2022
Session Time
16:15 - 17:45
Room
Hall 115
Lecture Time
17:05 - 17:35

Big data in diabetes centers and hospitals – what do we do with it?

Session Type
Plenary Session
Date
Thu, 28.04.2022
Session Time
09:00 - 10:00
Room
Hall 116
Lecture Time
09:00 - 09:20

Abstract

Abstract Body

Diabetes centers, healthcare systems, and individuals with diabetes generate many types of data about diabetes-related outcomes and about self-management behaviors, comorbid medical conditions, and clinical care-related events. Yet only a small fraction of these data are used by clinicians regularly for decisionmaking. Risk-based management protocols can help diabetes centers to improve both the quality and cost-efficiency of care. These protocols may be driven by biomarkers of risk extracted or derived from electronic health records, diabetes self-management devices (or the cloud services that receive their data), and digital patient reported outcomes platforms; protocols may alternately be driven by forecasting of negative outcomes via Artificial Intelligence/Machine Learning approaches. The participation by diabetes centers in Learning Health Networks, with data sharing to a central data repository, can accelerate Big-Data-driven quality-improvement of care delivery. The presenter will review examples of risk-based management approaches using each technique, including novel biomarker-based risk indices like the 6 Habits of self-management, the Diabetes Care Index, and the Risk Indexes for Poor Glycemic Control and for Diabetic Ketoacidosis (RI-PGC and RI-DKA, respectively). The presenter will further examine the current state of algorithms and AI/ML to manage population health in diabetes clinics, including a population health dashboard to reduce deterioration in glycemic control in the post-diagnostic period for type 1 diabetes, a precision medicine project for type 2 diabetes incorporating multiple -omics biomarkers, and the Rising T1DE Alliance, which seeks to implement multiple ML models to predict outcomes in clinical care, and to test remote patient monitoring along with multiple digital and behavorial health interventions to improve those predicted outcomes via a risk-based management approach.

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REMOTE PATIENT MONITORING IN YOUTH WITH TYPE 1 DIABETES (T1D) PREDICTED TO EXPERIENCE A RISE IN A1C%: COMPARISON TO A CLINIC-DERIVED, PROPENSITY SCORE-MATCHED CONTROLS

Session Type
Oral Presentations Session
Date
Sat, 30.04.2022
Session Time
13:00 - 14:30
Room
Hall 120
Lecture Time
13:24 - 13:32

Abstract

Background and Aims

To determine if 1) remote patient monitoring (RPM) can improve glycemic control among youth with T1D who are predicted to experience a rise in A1c% using logistic regression and 2) propensity-score (PS) matching (PSM) RPM youth to non-RPM youth can control for confounding bias.

Methods

We collected data from 2-18-year-olds attending a tertiary care network of diabetes clinics in the Midwestern USA from 11/2018-9/2021. Eligible youth had baseline A1c% ≥7.2, predicted 90-day rise in A1c% ≥0.3 via advanced machine learning, and follow-up A1c% measured 70-180 days after baseline. Criteria used to calculate PS included sex, ethnicity, race, insurance, technologies, age, T1D duration, baseline A1c%, and predicted 90-day A1c% change. We compared each RPM-youth with three matched, non-RPM youth identified using a PS within ±0.05.

Results

We matched 201 non-RPM youth to 67 RPM youth. The final cohort was 60% female, 4% Hispanic, 76% White, 54% private insurance, 37% on CGM and insulin pump, median age 13.4 years (IQR=10.3,16.0), T1D duration 44.6 months (17.6,84.7), baseline A1c% 7.9 (7.5,8.8), and predicted 90-day A1c% change 0.39 (0.33,0.48). After PSM, we found no significant differences for RPM and non-RPM youth (p’s=0.10-0.99), suggesting this method may be appropriate for creating a balanced clinic-derived control sample. However, 64% of RPM youth experienced no rise in A1c ≥0.3% compared to 53% of non-RPM youth (p=0.10).

Conclusions

Youth receiving RPM may experience improved glycemic control relative to a clinic-derived control sample based on PSM, but these results require verification in a large multisite traditionally controlled study.

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