Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis.

Background: The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain.

Numerical and clinical precision in hypoglycemia of the intermittent FreeStyle Libre glucose monitoring through an NFC-Bluetooth transmitter associated with the xDrip+ algorithm in diabetic patients under insulin therapy.

Introduction: There are data capture devices that attach to the FreeStyle Libre sensor and convert its communication from NFC (Near-field communication) to Bluetooth technology, generating real-time continuous glucose monitoring. The accuracy of hypoglycemia measurements displayed by smartphone apps using this device has not been established.

Methods: Study of diagnostic tests.

Artificial pancreas systems: experiences from concept to commercialisation.

Introduction: Automated insulin delivery (AID) systems, known as artificial pancreas or closed-loop glucose control systems, have been developed to improve the glycemic outcomes of people with type 1 diabetes. These systems use a control algorithm that automatically modifies the amount of insulin infused into a patient based on real-time blood glucose measurements. This study presents a summary of key clinical and technical issues related to the development of the first commercial AID systems and their evolution into commercial biomedical devices.

Design and Usability of an Avatar-Based Learning Program to Support Diabetes Education: Quality Improvement Study in Colombia.

Background: This quality improvement study, entitled Avatar-Based LEarning for Diabetes Optimal Control (ABLEDOC), explored the feasibility of delivering an educational program to people with diabetes in Colombia. The aim was to discover how this approach could be used to improve awareness and understanding of the condition, the effects of treatment, and strategies for effective management of blood-glucose control.

Methods: Individuals with diabetes were recruited by Colombian endocrinologists to a human-centered study to codesign the educational program, using the Double Diamond model.

Efficacy of the mHealth application in patients with type 2 diabetes transitioning from inpatient to outpatient care: A randomized controlled clinical trial.

Introduction: No studies have assessed the efficacy of telemedicine using a platform for recording and adjusting insulin doses in patients with diabetes mellitus type 2 (DM2) transitioning from inpatient to outpatient care. This study aimed to assess, in a population of patients with DM2, discharged from a tertiary referral hospital, whether treatment based on the use of an mHealth application was associated with better glycemic control at the 3-month follow-up, than standard care.

Methods: This open, randomized, controlled clinical trial included adult DM2 patients who were transitioning from inpatient to outpatient care.

Is a cloud-based platform useful for diabetes management in Colombia? The Tidepool experience.

Background: There are several medical devices used in Colombia for diabetes management, most of which have an associated telemedicine platform to access the data. In this work, we present the results of a pilot study evaluating the use of the Tidepool telemedicine platform for providing remote diabetes health services in Colombia across multiple devices.

Method: Individuals with Type 1 and Type 2 diabetes using multiple diabetes devices were recruited to evaluate the user experience with Tidepool over three months.

Factors associated with clinically significant hypoglycemia in patients with type 1 diabetes using sensor-augmented pump therapy with predictive low-glucose management: A multicentric study on iberoamerica.

Background And Aims: Despite using sensor-augmented pump therapy (SAPT) with predictive low-glucose management (PLGM), hypoglycemia is still an issue in patients with type 1 Diabetes (T1D). Our aim was to determine factors associated with clinically significant hypoglycemia (<54 mg/dl) in persons with T1D treated with PLGM-SAPT.

Method: ology: This is a multicentric prospective real-life study performed in Colombia, Chile and Spain.

Impact of sensor-augmented pump therapy with predictive low-glucose management on hypoglycemia and glycemic control in patients with type 1 diabetes mellitus: 1-year follow-up.

Aims: To describe real-life experience with sensor-augmented pump therapy with predictive low-glucose management (SAPT-PLGM), in terms of hypoglycemia and glycemic control after one year of follow-up in T1D patients with hypoglycemia as the main indication of therapy.

Methods: Retrospective cohort study under real life conditions. Baseline and one-year follow-up variables of glycemic control, hypoglycemia and glycemic variability were compared.

Defining High Glycemic Variability in Type 1 Diabetes: Comparison of Multiple Indexes to Identify Patients at Risk of Hypoglycemia.

International consensus on the use of continuous glucose monitoring (CGM) recommends coefficient of variation (CV) as the metric of choice to express glycemic variability (GV) with a cutoff of 36% to define unstable diabetes. Even though, CV is associated with hypoglycemia in type 2 diabetes patients, the evidence on the use of one particular measure of GV in type 1 diabetes (T1DM) patients as a predictor of hypoglycemia is limited. A cohort of T1DM ambulatory patients was evaluated using CGM.

Hypoglycemia Incidence and Factors Associated in a Cohort of Patients With Type 2 Diabetes Hospitalized in General Ward Treated With Basal Bolus Insulin Regimen Assessed by Continuous Glucose Monitoring.

Introduction: Continuous glucose monitoring (CGM) is a better tool to detect hyper and hypoglycemia than capillary point of care in insulin-treated patients during hospitalization. We evaluated the incidence of hypoglycemia in patients with type 2 diabetes (T2D) treated with basal bolus insulin regimen using CGM and factors associated with hypoglycemia.

Methods: Post hoc analysis of a prospective cohort study.

The different methods of assessing glycemic variability, quality of glycemic control and glycemic risk cannot be interpreted as equivalent in clinical practice.

Objective: Several methods are available to calculate glycemic variability (GV), quality of glycemic control (QGC) and glycemic risk (GR). However, clinicians do not easily interpret these data. This study evaluates whether the results of the different methods can be interpreted as equivalent.

Different Indexes of Glycemic Variability as Identifiers of Patients with Risk of Hypoglycemia in Type 2 Diabetes Mellitus.

Introduction: Recent publications frequently introduce new indexes to measure glycemic variability (GV), quality of glycemic control, or glycemic risk; however, there is a lack of evidence supporting the use of one particular parameter, especially in clinical practice.

Methods: A cohort of type 2 diabetes mellitus (T2DM) patients in ambulatory care were followed using continuous glucose monitoring sensors (CGM). Mean glucose (MG), standard deviation, coefficient of variation (CV), interquartile range, CONGA1, 2, and 4, MAGE, M value, J index, high blood glucose index, and low blood glucose index (LBGI) were estimated.

Impact of a Basal-Bolus Insulin Regimen on Metabolic Control and Risk of Hypoglycemia in Patients With Diabetes Undergoing Peritoneal Dialysis.

Introduction: Clinical interventional studies in diabetes mellitus usually exclude patients undergoing peritoneal dialysis (PD). This study evaluates the impact of an educational program and a basal-bolus insulin regimen on the blood glucose level control and risk of hypoglycemia in this population.

Methods: A before-and-after study was conducted in type 1 and type 2 DM patients undergoing PD at the Renal Therapy Services (RTS) clinic network, Bogota, Colombia.

Computing the risk of postprandial hypo- and hyperglycemia in type 1 diabetes mellitus considering intrapatient variability and other sources of uncertainty.

Objective: The objective of this article was to develop a methodology to quantify the risk of suffering different grades of hypo- and hyperglycemia episodes in the postprandial state.

Methods: Interval predictions of patient postprandial glucose were performed during a 5-hour period after a meal for a set of 3315 scenarios. Uncertainty in the patient's insulin sensitivities and carbohydrate (CHO) contents of the planned meal was considered.