RESUMEN
Protocol for a three-arm randomized controlled trial investigating telemonitoring with decision support in insulin-treated people with type 2 diabetes.
Asunto(s)
Diabetes Mellitus Tipo 2 , Insulina , Telemedicina , Humanos , Automonitorización de la Glucosa Sanguínea , Sistemas de Apoyo a Decisiones Clínicas , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Hipoglucemiantes/uso terapéutico , Insulina/uso terapéutico , Ensayos Clínicos Controlados Aleatorios como AsuntoRESUMEN
BACKGROUND: Treatment inertia and prescription complexity are among reasons that people with type 2 diabetes (T2D) do not reach glycemic targets. This study investigated feasibility of a new approach to basal insulin initiation, where the dose needed to reach a glycemic target is estimated from two weeks of insulin and continuous glucose monitoring (CGM) data. METHODS: This was an exploratory single arm study with a maximum length of 84 days. Eight insulin naïve people with T2D, planning to initiate basal insulin, wore a CGM throughout the study period. A predetermined regime was followed for the first two weeks after which the end dose was estimated. The clinician decided whether to follow this advice and continued the titration until target was reached using a twice weekly stepwise titration algorithm. The primary outcome was the comparison between the estimated and the actual end doses. RESULTS: Median age of participants was 57 years (range: 50-77 years), duration of diabetes was 16 years (range: 5-29 years), and Bodi Mass Index (BMI) was 30.2 kg/m2 (range: 22.0-36.0 kg/m2). The median study end dose was 37 U (range: 20-123 U). The estimated end dose was smaller than or equal to the study end dose in all cases, with median error of 26.7% (range: 0.0%-75.8% underestimation). No self-monitoring of blood glucose values were below 70 mg/dL and no severe hypoglycemia occurred. CONCLUSION: While accuracy may be improved, it was found safe to predict the study end dose of insulin degludec from two weeks of data.
Asunto(s)
Diabetes Mellitus Tipo 2 , Insulina , Anciano , Glucemia , Automonitorización de la Glucosa Sanguínea , Diabetes Mellitus Tipo 2/tratamiento farmacológico , Estudios de Factibilidad , Hemoglobina Glucada/análisis , Humanos , Hipoglucemiantes , Persona de Mediana EdadRESUMEN
With the fast growth of diabetes prevalence, the disease is now considered an epidemic. Diabetes is characterized by elevated glucose levels, that may be treated with insulin. Tight control of glucose is essential for prevention of complications and patients' well-being. In this paper we model the fasting glucose-insulin dynamics in type 2 diabetes, aiming at controlling the glucose level. Relevant clinical data are typically sparse and have a sampling period much greater than the fast dynamics in the glucose-insulin dynamics in humans. We adapt a physiological model such that important slow non-linear dynamics are identifiable and test the resulting model on deterministic simulated data and sparse, slow sampled clinical data.
Asunto(s)
Glucemia/análisis , Diabetes Mellitus Tipo 2/sangre , Insulina/sangre , Modelos Biológicos , Ayuno , HumanosRESUMEN
Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.