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. METHODS: The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data. RESULTS: A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production. CONCLUSION: This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.

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. Numerical accuracy was evaluated, utilizing the absolute difference with respect to capillary glucometry (ISO 15197:2015 standard) and clinical accuracy, using the Clarke and Parkes (Consensus) error grids, for glucose measurements less than 70mg/dL performed with the FreeStyle Libre system and with the digital estimation xDrip+ app, in diabetic patients managed with insulin therapy. RESULTS: Twenty-seven patients were included (TIR 73.4%, TBR70 5.6%), who contributed 83 hypoglycemic events. Numerical accuracy was adequate in similar proportions with the FreeStyle Libre system compared to the xDrip+ app (81.92% vs. 68.67%, p=0.0630). The clinical accuracy evaluation showed that 92.8% of the measurements for xDrip+ and 98.8% for FreeStyle libre met the criteria according to the Parkes (Consensus) grid (p=0.0535); and 79.5% and 91.6% of the measurements met the criteria according to the Clarke grid (p=0.0273), being higher with FreeStyle libre. CONCLUSIONS: The use of the NFC-Bluetooth transmitter (Miao-Miao) associated with the xDrip+ app does not improve numerical or clinical accuracy for detecting hypoglycemic events in diabetic patients managed with insulin therapy, compared to the FreeStyle Libre device.

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. Participants contributed to two phases. The first phase focused on gathering unmet educational needs and choice of curriculum. Three prototypes were developed as a result. During phase 2, a different group of participants engaged with the program for several weeks, before reporting back. RESULTS: Thirty-six participants completed a Web survey during phase 1, and five were also interviewed by telephone. The majority (33 of 36; 91%) were receptive to the prospect of educational interventions and ranked the chosen topic of hypoglycemia highly. In phase 2, the three prototypes were tested by 17 participants, 10 of whom also gave feedback in focus groups. The response was overwhelmingly positive, with 16 of 17 (94%) stating they would use a program like this again. The 3D version was the most highly rated. CONCLUSIONS: Immersive, avatar-based programs, delivered through smartphone, have the potential to deliver educational information that is trusted, engaging, and useful. Future work includes expansion of the curriculum, evaluation with a larger group, and exploration of the prospective role of artificial intelligence in personalizing this form of educational intervention.

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. AREAS COVERED: Highlights of each AID system are summarized through timelines, ranging from the definition of the core strategy of the control algorithm to the practical application and subsequent commercial approval. Tabulated information regarding the conducted main clinical studies is also presented. EXPERT OPINION: Insulin therapy has evolved up to the current commercial AID systems available, which have provided patients access to a safer and more effective therapy owing to automatic adjustments to insulin. However, this technology is relatively new and can be significantly improved. Limitations include the resistance of healthcare providers, high costs, and the availability of this treatment. The future of this technology is directed toward obtaining fully automatic control systems.

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. The efficacy and safety of patient management with and without mHealth was compared at the 3-month follow-up. The primary outcome was the change in the Glycosylated hemoglobin (HbA1c) levels. The secondary outcomes were the rates of hypoglycemic and hyperglycemic events and treatment satisfaction measured using the Insulin Treatment Satisfaction Questionnaire (ITSQ). RESULTS: In total, 86 patients (41 using mHealth) were included in the clinical trial. HbA1c levels showed a significant decrease in both groups. The mean HbA1c level was significantly lower in the mHealth group. Patients using mHealth showed decreased incidence rate ratios of hypoglycemia 3.0 mmol/L [<54 mg/dl], hypoglycemia ranging from 3.0 to 3.8 mmol/L [54 to 70 mg/dl] and severe hypoglycemia. The level of satisfaction assessed using the ITSQ was higher in the mHealth group. CONCLUSION: Using mHealth in patients with DM2 transitioning from inpatient to outpatient care improves metabolic control and may reduce the hypoglycemia rates.

Practical constraint definition in safety schemes for artificial pancreas systems.

INTRODUCTION: Artificial pancreas systems usually define an insulin-on-board constraint (IOB¯) for safety schemes to limit the insulin infusion and avoid hypoglycemia during the closed-loop performance. Several methods have been proposed with impractical considerations requiring information from the prandial events or complex procedures for ambulatory use. METHODS: This paper presents a simple method that consists of two novel rules that allow finding an IOB¯ based only on common clinical parameters that do not require patient intervention. The method robustness was evaluated using a control system coupled to a safety layer under demanding scenarios implemented on the FDA-approved simulator for preclinical studies. RESULTS: The method maintains a safe performance, even in the face of interpatient variability, hybrid and fully automatic implementations of an artificial pancreas system, and uncertain settings. Both proposed rules work as effectively or even better and without the patient intervention than other methods that have already been clinically validated. CONCLUSION: This method can be used to define a constant IOB¯ that ensures performance and safety of the control system, even under scenarios with incorrect clinical data. Unlike other methods, this method only requires reliable information that is easily obtained from the patient, such as their total daily dose of insulin or body mass.

Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability.

The aim of control strategies for artificial pancreas systems is to calculate the insulin doses required by a subject with type 1 diabetes to regulate blood glucose levels by reducing hyperglycemia and avoiding the induction of hypoglycemia. Several control formulations developed for this end involve a safety constraint given by the insulin on board (IOB) estimation. This constraint has the purpose of reducing hypoglycemic episodes caused by insulin stacking. However, intrapatient variability constantly changes the patient's response to insulin, and thus, an adaptive method is required to restrict the control action according to the current situation of the subject. In this work, the control action computed by an impulsive model predictive controller is modulated with a safety layer to satisfy an adaptive IOB constraint. This constraint is established with two main steps. First, upper and lower IOB bounds are generated with an interval model that accounts for parameter uncertainty, and thus, define the possible system responses. Second, the constraint is selected according to the current value of glycemia, an estimation of the plant-model mismatch, and their corresponding first and second time derivatives to anticipate the changes of both glucose levels and physiological variations. With this strategy satisfactory results were obtained in an adult cohort where random circadian variability and sensor noise were considered. A 92% time in normoglycemia was obtained, representing an increase of time in range compared to previous MPC strategies, and a reduction of time in hypoglycemia to 0% was achieved without dangerously increasing the time in hyperglycemia.

Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis.

BACKGROUND: Artificial pancreas is a well-known research topic devoted to achieving better glycemic outcomes that has been attracting increasing attention over the years. However, there is a lack of systematic, chronological, and synthesizing studies that show the background of the knowledge generation in this field. This study implements a bibliometric analysis to recognize the main documents, type of publications, research categories, countries, keywords, organizations, and authors related to this topic. METHODS: Web of Science core collection database was accessed from 2000 to 2020 in order to select high-quality scientific documents based on a specific search query. Bibexcel, MS Excel, Power BI, R-Studio, VOSviewer, and CorText software were used for a descriptive and network analysis based on the local database obtained. Bibliometric parameters as the h-index, frequencies, co-authorship and co-ocurrences were computed. RESULTS: A total of 756 documents were included that show a growing scientific production on this topic with an increasing contribution from engineering. Outstanding authors, organizations, and countries were identified. An analysis of trends in research was conducted according to the scientific categories of the Web of Science database to identify the main research interests of the last 2 decades and the emerging areas with greater prominence in the coming years. A keyword network analysis allowed to identify the main stages in the development of the AP research over time. CONCLUSIONS: Results reveal a comprehensive background of the knowledge generation for the AP topic during the last 2 decades, which has been strengthened with international collaborations and a remarkable interdisciplinarity between endocrinology and engineering, giving rise to a growing number of research areas over time, where computer science and medical informatics stand out as the main emerging research areas.

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. Two endocrinologists used the Tidepool software to maintain a weekly communication with participants reviewing the devices data remotely. Demographic, clinical, psychological and usability data were collected at several stages of the study. RESULTS: Six participants, from ten at the baseline (five MDI and five CSII), completed this pilot study. Three different diabetes devices were employed by the participants: a glucose meter (Abbot), an intermittently-scanned glucose monitor (Abbot), and an insulin pump (Medtronic). A score of 81.3 in the system usability scale revealed that overall, most participants found the system easy to use, especially the web interface. The system also compared highly favourably against the proprietary platforms. The ability to upload and share data and communicate remotely with the clinicians was raised consistently by participants. Clinicians cited the lockdown imposed by the Covid-19 pandemic as a valuable test for this platform. Inability to upload data from mobile devices was identified as one of the main limitations. CONCLUSION: Tidepool has the potential to be used as a tool to facilitate remote diabetes care in Colombia. Users, both participants and clinicians, agreed to recommend the use of platforms like Tidepool to achieve better disease management and communication with the health care team. Some improvements were identified to enhance the user experience.

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. Patients with T1D treated with PLGM-SAPT, using sensor ≥70% of time, were included. Data regarding pump and sensor use patterns and carbohydrate intake from 28 consecutive days were collected. A bivariate and multivariate Poisson regression analysis was carried out, to evaluate the association between the number of events of <54 mg/dl with the clinical variables and patterns of sensor and pump use. RESULTS: 188 subjects were included (41 ± 13.8 years-old, 23 ± 12 years disease duration, A1c 7.2% ± 0.9). The median of events <54 mg/dl was four events/patient/month (IQR 1-10), 77% of these events occurred during day time. Multivariate analysis showed that the number of events of hypoglycemia were higher in patients with previous severe hypoglycemia (IRR1.38; 95% CI 1.19-1.61; p < 0.001), high glycemic variability defined as Coefficient of Variation (CV%) > 36% (IRR 2.09; 95%CI 1.79-2.45; p < 0.001) and hypoglycemia unawareness. A protector effect was identified for adequate sensor calibration (IRR 0.77; 95%CI 0.66-0.90; p:0.001), and the use of bolus wizard >60% (IRR 0.74; 95%CI 0.58-0.95; p:0.017). CONCLUSION: In spite of using advanced SAPT, clinically significant hypoglycemia is still a non-negligible risk. Only the identification and intervention of modifiable factors could help to prevent and reduce hypoglycemia in clinical practice.

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. Hypoglycemia was documented in terms of incidence rate and percentage of time <54 mg/dL (3.0 mmol/L) and <70 mg/dL (3.9 mmol/L). Factors evaluated included glycemic variability analyzed during the first 6 days of basal bolus therapy. RESULTS: A total of 34 hospitalized patients with T2D in general ward were included, with admission A1c of 9.26 ± 2.62% (76.8 ± 13 mmol/mol) and mean blood glucose of 254 ± 153 mg/dL. There were two events of hypoglycemia below 54 mg/dL (3.0 mmol/L) and 11 events below 70 mg/dL (3.9 mmol/L) with an incidence of hypoglycemic events of 0.059 and 0.323 per patient, respectively. From second to fifth day of treatment the percentage of time in range (140-180 mg/dL, 7.8-10.0 mmol/L) increased from 72.1% to 89.4%. Factors related to hypoglycemic events <70 mg/dL (3.9 mmol/L) were admission mean glucose (IRR 0.86, 95% CI 0.79, 0.95, P < .01), glycemic variability measured as CV (IRR 3.12, 95% CI 1.33, 7.61, P < .01) and SD, and duration of stay. CONCLUSIONS: Basal bolus insulin regimen is effective and the overall incidence of hypoglycemia detected by CGM is low in hospitalized patients with T2D. Increased glycemic variability as well as the decrease in mean glucose were associated with events <70 mg/dL (3.9 mmol/L).

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. RESULTS: Fifty patients were included, 31 on prior treatment with SAPT with low-glucose suspend (LGS) feature and 19 on multiple dose insulin injections (MDI). Mean HbA1c decreased in the MDI group (8.24%-7.08%; p = 0.0001). HbA1c change was not significant in the SAPT-LGS group. Area under the curve (AUC) below 70 mg/dl improved in both SAPT-LGS and MDI groups while AUC, %time and events below 54 mg/dl decreased in SAPT-LGS group. Glycemic variability improved in the MDI group. Less patients presented severe hypoglycemia with SAPT-PLGM in both groups, however the change was non-significant. CONCLUSIONS: Under real life conditions, SAPT-PLGM reduced metrics of hypoglycemia in patients previously treaded with MDI and SAPT-LGS without deteriorating glycemic control in SAPT-LGS patients, while improving it in patients treated with MDI.

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

Background: 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. Methods: A cohort of T1DM ambulatory patients was evaluated using CGM. Number and incidence rate of events <54 and <70 mg/dL were calculated. Bivariate and multivariate analysis of different glycemic indexes and clinical variables were performed to identify those associated with hypoglycemia. Receiver operating characteristic (ROC) curve analysis for each of the glycemic indexes was performed to define the best index and its optimal cutoff threshold to discriminate patients with events of hypoglycemia. Results: Seventy-three patients were included. A total of 128 events <54 mg/dL were recorded in 34 patients, and 350 events <70 mg/dL were registered in 51 patients. CV was the only variable significantly associated with hypoglycemia <54 mg/dL in the multivariate analysis (adjusted relative risk [aRR] 1.44, 95% confidence interval [CI]: 1.10-1.88, P = 0.008). CV, HbA1c (glycated hemoglobin), and mean glucose were associated with events <70 mg/dL. ROC curve analysis showed that, among GV metrics, CV had the best performance to discriminate patients with events <54 mg/dL (area under the curve [AUC] 0.87, 95% CI: 0.79-0.95) and events <70 mg/dL (AUC 0.79, 95% CI: 0.68-0.90) with optimal cutoff thresholds values of 34% and 31%, respectively. Among glycemic risk (GR) indexes, low blood glucose index (LBGI) showed the best performance. Conclusions: This analysis shows that CV is the best GV index, and LBGI the best GR index, to identify patients at risk of clinically significant hypoglycemia and hypoglycemia alert events in T1DM patients.

Efficacy and safety of sensor-augmented pump therapy (SAPT) with predictive low-glucose management in patients diagnosed with type 1 diabetes mellitus previously treated with SAPT and low glucose suspend / Eficacia y seguridad del tratamiento con bomba de insulina con sensor (SAPT) con gestión predictiva de la hipoglucemia en pacientes con diagnóstico de diabetes mellitus tipo 1 tratados previamente con SAPT y suspensión por hipoglucemia

Background: Sensor-augmented insulin pump therapy (SAPT) with low-glucose suspend (LGS) is an effective and safe alternative for treating patients with type 1 diabetes mellitus (T1DM). New predictive low-glucose management (PLGM) systems decrease the severity and duration of hypoglycemic events. However, evidence of benefits in patients previously treated with SAPT-LGS is limited. Methods: A prospective before-after study was conducted in patients with T1DM treated with SAPT-LGS, who were switched to the Minimed(R) 640G system with SmartGuard(R) to assess the impact on A1c levels, severe hypoglycemia (SH), hypoglycemia unawareness (HU), and area under the curve (AUC) <70mg/dL after three months of follow-up. Results: Fifty-five patients with T1DM with a mean age of 37.9 (IQR 6, 79) years and a mean baseline A1c level of 7.52±1.11% were enrolled. After three months under PLGM, A1c levels significantly decreased to 7.18±0.91% (p=0.004). SH rate decreased from 2.47 (CI 0.44, 4.90) to 0.87 (CI 0.22, 1.52) events/patient-year (Incidence rate ratio 0.353, 95% CI 0.178, 0.637), AUC <70mg/dL decreased from 0.59±0.76 to 0.35±0.65mg/dL x minute (p=0.030). HU determined by Clarke questionnaire resolved in 23 out of 30 patients (p=0.002). Conclusions: This study suggests that SAPT with PLGM decreases the frequency of SH, HU, exposure to glucose levels below 70mg/dL, and A1c levels. Based on these results, this therapy should be considered in T1DM patients previously treated with SAPT-LGS with persistent SH and HU. Further clinical trials comparing the efficacy and safety of these features are required

Introducción: La terapia con bomba de insulina integrada a sistema de monitoreo continuo con suspensión en hipoglucemia (SAPT-LGS) es una alternativa efectiva y segura para el tratamiento en pacientes con diabetes tipo 1 (DM1). La función de suspensión antes del límite bajo (PLGM) reduce la gravedad y la duración de los eventos hipoglucémicos. Sin embargo, la evidencia del beneficio en pacientes tratados previamente con SAPT-LGS es limitada. Métodos: Se realizó un estudio longitudinal antes y después con pacientes DM1 tratados con SAPT-LGS que se cambiaron al sistema Minimed(R) 640G con SmartGuard(R), con el fin de evaluar el impacto en los niveles de A1c, hipoglucemia severa (HS), hipoglucemia asintomática (HA) y área bajo la curva (AUC) <70mg/dl después de tres meses de seguimiento. Resultados: Se incluyeron 55 pacientes con DM1, de 37.9 (IQR 6, 79) años, A1c basal de 7.52±1.11%. A los 3 meses bajo PLGM, la A1c se redujo significativamente a 7.18%±0.91% (p=0.004). La tasa de HS se redujo de 2.47 (CI 0.44,4.90) a 0.87 (CI 0.22,1.52) eventos/año del paciente (índice de incidencia 0.353 IC 95%, 0.178, 0.637), el AUC <70mg/dl se redujo de 0,59±0,76 a 0,35±0,65mg/dl x minuto (p = 0,030). HA determinado por el cuestionario Clarke resolvió en 23 de 30 pacientes (p=0,002). Conclusiones: Este estudio sugiere que PLGM reduce la frecuencia de HS, HA, la exposición a niveles de glucosa por debajo de 70mg/dl y A1c. Con base a estos resultados, esta terapia debería considerarse en pacientes con DM1 tratados previamente con SAPT-LGS que persisten con HS e HA. Se requieren ensayos clínicos adicionales que comparen la eficacia y la seguridad de estas características

Efficacy and safety of sensor-augmented pump therapy (SAPT) with predictive low-glucose management in patients diagnosed with type 1 diabetes mellitus previously treated with SAPT and low glucose suspend.

BACKGROUND: Sensor-augmented insulin pump therapy (SAPT) with low-glucose suspend (LGS) is an effective and safe alternative for treating patients with type 1 diabetes mellitus (T1DM). New predictive low-glucose management (PLGM) systems decrease the severity and duration of hypoglycemic events. However, evidence of benefits in patients previously treated with SAPT-LGS is limited. METHODS: A prospective before-after study was conducted in patients with T1DM treated with SAPT-LGS, who were switched to the Minimed® 640G system with SmartGuard® to assess the impact on A1c levels, severe hypoglycemia (SH), hypoglycemia unawareness (HU), and area under the curve (AUC) <70mg/dL after three months of follow-up. RESULTS: Fifty-five patients with T1DM with a mean age of 37.9 (IQR 6, 79) years and a mean baseline A1c level of 7.52±1.11% were enrolled. After three months under PLGM, A1c levels significantly decreased to 7.18±0.91% (p=0.004). SH rate decreased from 2.47 (CI 0.44, 4.90) to 0.87 (CI 0.22, 1.52) events/patient-year (Incidence rate ratio 0.353, 95% CI 0.178, 0.637), AUC <70mg/dL decreased from 0.59±0.76 to 0.35±0.65mg/dL x minute (p=0.030). HU determined by Clarke questionnaire resolved in 23 out of 30 patients (p=0.002). CONCLUSIONS: This study suggests that SAPT with PLGM decreases the frequency of SH, HU, exposure to glucose levels below 70mg/dL, and A1c levels. Based on these results, this therapy should be considered in T1DM patients previously treated with SAPT-LGS with persistent SH and HU. Further clinical trials comparing the efficacy and safety of these features are required.

Reduction of glycemic variability with Degludec insulin in patients with unstable diabetes.

INTRODUCTION: Degludec (IDeg) is an ultralong-acting insulin, with stable pharmacodynamic profile which leads to lower fluctuations in glucose levels. The effect of IDeg has not been specifically assessed in patients with unstable diabetes, defined as increased glycemic variability (GV). METHODS: A prospective before-after pilot study was conducted, including patients managed at Hospital Universitario San Ignacio in Bogotá, Colombia. The impact of the switch from a Glargine or Detemir insulin to a basal insulin regimen with IDeg for 12 weeks on GV measured by continuous glucose monitoring, on A1c levels, and on the incidence of episodes of global and nocturnal hypoglycemia was assessed in a group of patients with (coefficient of variation >34%) or without increased basal GV using a Generalised Estimating Equation (GEE) analysis. RESULTS: 60 patients with basal bolus therapy and history of hypoglycemia were included. 18 patients had High GV (HGV). In this group a significant reduction of 11.1% of CV (95% CI: 6.3, 15.9, p = 0.01) was found. GEE analysis confirmed a higher impact over time on patients with HGV (p < 0.001). The percentage of patients with at least 1 episode of hypoglycemia decreased from 66.6% to 22.2% (p = 0.02) and from 37.14% to 5.71% (p < 0.01) for global and nocturnal hypoglycemia, respectively. Changes were not significant in patients with low GV. A reduction of A1c was observed in both groups (p < 0.001). CONCLUSIONS: The results suggest that treatment with IDeg reduces GV, A1c levels and the incidence of global and nocturnal hypoglycemia events in patients with HGV, but not in patients with low GV.

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. Hypoglycemia incidence (<54 mg/dl) was calculated. Area under the curve (AUC) was determined for different indexes as identifiers of patients with risk of hypoglycemia (IRH). Optimal cutoff thresholds were determined from analysis of the receiver operating characteristic curves. RESULTS: CGM data for 657 days from 140 T2DM patients (4.69 average days per patient) were analyzed. Hypoglycemia was present in 50 patients with 144 hypoglycemic events in total (incidence rate of 0.22 events per patient/day). In the multivariate analysis, both CV (OR 1.20, 95% CI 1.12-1.28, P < .001) and LBGI (OR 4.83, 95% CI 2.41-9.71, P < .001) were shown to have a statistically significant association with hypoglycemia. The highest AUC were for CV (0.84; 95% CI 0.77-0.91) and LBGI (0.95; 95% CI 0.92-0.98). The optimal cutoff threshold for CV as IRH was 34%, and 3.4 for LBGI. CONCLUSION: This analysis shows that CV can be recommended as the preferred parameter of GV to be used in clinical practice for T2DM patients. LBGI is the preferred IRH between glycemic risk indexes.

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. An intervention was instituted consisting of a three-month educational program and a basal-bolus detemir (Levemir, NovoNordisk) and aspart (Novorapid, NovoNordisk) insulin regimen. Prior to the intervention and at the end of treatment were conducted measures of HbA1c levels and continuous glucose monitoring (CGM). RESULTS: Forty-seven patients were recruited. Mean HbA1c level decreased from 8.41% ± 0.83 to 7.68% ± 1.32 (mean difference -0.739, 95% CI -0.419, -1.059; P < .0001). Of subjects, 52% achieved HbA1c levels <7.5% at the end of study. Mean blood glucose level reduced from 194.0 ± 42.5 to 172.9 ± 31.8 mg/dl ( P = .0015) measured by CGM. Significant differences were not observed in incidence of overall ( P = .7739), diurnal ( P = .3701), or nocturnal ( P = .5724) hypoglycemia episodes nor in area under the curve (AUC) <54 mg/dl ( P = .9528), but a reduction in AUC >180 ( P < .01) and AUC >250 ( P = .01) was evidenced for total, diurnal, and nocturnal episodes. CONCLUSIONS: An intervention consisting of an educational program and a basal-bolus insulin regimen in type 1 and type 2 diabetes mellitus patients undergoing PD caused a decrease in HbA1c levels, and mean blood glucose levels as measured from CGM with no significant increases in hypoglycemia episodes.

Improving the computational effort of set-inversion-based prandial insulin delivery for its integration in insulin pumps.

OBJECTIVE: Set-inversion-based prandial insulin delivery is a new model-based bolus advisor for postprandial glucose control in type 1 diabetes mellitus (T1DM). It automatically coordinates the values of basal-bolus insulin to be infused during the postprandial period so as to achieve some predefined control objectives. However, the method requires an excessive computation time to compute the solution set of feasible insulin profiles, which impedes its integration into an insulin pump. In this work, a new algorithm is presented, which reduces computation time significantly and enables the integration of this new bolus advisor into current processing features of smart insulin pumps. METHODS: A new strategy was implemented that focused on finding the combined basal-bolus solution of interest rather than an extensive search of the feasible set of solutions. Analysis of interval simulations, inclusion of physiological assumptions, and search domain contractions were used. Data from six real patients with T1DM were used to compare the performance between the optimized and the conventional computations. RESULTS: In all cases, the optimized version yielded the basal-bolus combination recommended by the conventional method and in only 0.032% of the computation time. Simulations show that the mean number of iterations for the optimized computation requires approximately 3.59 s at 20 MHz processing power, in line with current features of smart pumps. CONCLUSIONS: A computationally efficient method for basal-bolus coordination in postprandial glucose control has been presented and tested. The results indicate that an embedded algorithm within smart insulin pumps is now feasible. Nonetheless, we acknowledge that a clinical trial will be needed in order to justify this claim.