Comment on Pantalone et al. The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model. Diabetes Care 2020;43:1910–1919

Carotid Disease and Retinal Optical Coherence Tomography Angiography Parameters in Type 2 Diabetes: The Fremantle Diabetes Study Phase II

We were interested in the recent article from Pantalone et al. (1), who reported that 23.7% of 6,973 subjects with poorly controlled type 2 diabetes (HbA1c >9%) attained HbA1c below 8% within 1 year, based on the electronic health record

Read More Comment on Pantalone et al. The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model. Diabetes Care 2020;43:1910–1919

Response to Comment on Pantalone et al. The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model. Diabetes Care 2020;43:1910–1919

Carotid Disease and Retinal Optical Coherence Tomography Angiography Parameters in Type 2 Diabetes: The Fremantle Diabetes Study Phase II

We would like to thank Larroumet et al. (1) for their interest in our article (2) and for sharing their institution’s significant experience (3). Certainly, admission to a specialized ward can durably improve the HbA1c control in patients with uncontrolled

Read More Response to Comment on Pantalone et al. The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model. Diabetes Care 2020;43:1910–1919

Pairing wearables data with self-reported symptoms could improve COVID-19 prediction

A man wearing a Fitbit Charge 2 device

Symptom-based COVID-19 screening can be improved by incorporating data collected from wearable sensors into prediction algorithms, an approach that could complement ongoing testing efforts by spotting symptomatic and pre-symptomatic individuals early, according to a research letter recently published in nature

Read More Pairing wearables data with self-reported symptoms could improve COVID-19 prediction

Gestational diabetes mellitus prediction? A unique fatty acid profile study

Gestational diabetes mellitus prediction? A unique fatty acid profile study

This unique study describes the patterns and ratios between fatty acids as seen in early pregnancy in women at risk of GDM when compared to NHC. We have chosen to discuss ratios in addition to absolute values, given the high

Read More Gestational diabetes mellitus prediction? A unique fatty acid profile study

Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment

Integrated Skin Transcriptomics and Serum Multiplex Assays Reveal Novel Mechanisms of Wound Healing in Diabetic Foot Ulcers

IntroductionType 2 diabetes is a complex disease, characterized by hyperglycemia associated with varying degrees of insulin resistance and impaired insulin secretion and influenced by nongenetic and genetic factors. Despite this, glucose-lowering treatment is similar for most people. Current type 2

Read More Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment

A combined risk score enhances prediction of type 1 diabetes among susceptible children

A combined risk score enhances prediction of type 1 diabetes among susceptible children

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UKLauric A. Ferrat, Seth A. Sharp, Michael N. Weedon & Richard A. OramHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USAKendra Vehik, Jeffrey P. Krischer, Sarah Austin-Gonzalez, Maryouri Avendano, Sandra Baethke, Brant

Read More A combined risk score enhances prediction of type 1 diabetes among susceptible children

The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model

Type 1 Diabetes and COVID-19: Preliminary Findings From a Multicenter Surveillance Study in the U.S.

Research Design and MethodsStudy CohortThe EHR system at CC was used to identify a cohort of adult patients (≥18 years) with a diagnosis of T2D and who were determined to have uncontrolled T2D as reflected by an A1C >9% between

Read More The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model

Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System

Type 1 Diabetes and COVID-19: Preliminary Findings From a Multicenter Surveillance Study in the U.S.

AbstractOBJECTIVE To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia

Read More Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System