Sensor-augmented pump therapy lowers HbA(1c) in suboptimally controlled Type?1 diabetes; a randomized controlled trial

To investigate the efficacy of sensor-augmented pump therapy vs. multiple daily injection therapy in patients with suboptimally controlled Type 1 diabetes.

STAR 3 randomized controlled trial to compare sensor-augmented insulin pump therapy with multiple daily injections in the treatment of type 1 diabetes: research design, methods, and baseline characteristics of enrolled subjects.

BACKGROUND: Sensor-augmented pump therapy (SAPT) integrates real-time continuous glucose monitoring (RT-CGM) with continuous subcutaneous insulin infusion (CSII) and offers an alternative to multiple daily injections (MDI). Previous studies provide evidence that SAPT may improve clinical outcomes among people with type 1 diabetes. Sensor-Augmented Pump Therapy for A1c Reduction (STAR) 3 is a multicenter randomized controlled trial comparing the efficacy of SAPT to that of MDI in subjects with type 1 diabetes. METHODS: Subjects were randomized to either continue with MDI or transition to SAPT for 1 year. Subjects in the MDI cohort were allowed to transition to SAPT for 6 months after completion of the study. SAPT subjects who completed the study were also allowed to continue for 6 months. The primary end point was the difference between treatment groups in change in hemoglobin A1c (HbA1c) percentage from baseline to 1 year of treatment. Secondary end points included percentage of subjects with HbA1c < or =7% and without severe hypoglycemia, as well as area under the curve of time spent in normal glycemic ranges. Tertiary end points include percentage of subjects with HbA1c < or =7%, key safety end points, user satisfaction, and responses on standardized assessments. RESULTS: A total of 495 subjects were enrolled, and the baseline characteristics similar between the SAPT and MDI groups. Study completion is anticipated in June 2010. CONCLUSIONS: Results of this randomized controlled trial should help establish whether an integrated RT-CGM and CSII system benefits patients with type 1 diabetes more than MDI.

Pilot Study of clinician attitudes to insulin pump therapy: international differences and the need for a greater understanding of the patient perspective

OBJECTIVES: To identify and survey health care professionals (HCPs) attitudes to insulin pump therapy (CSII).

METHODS: Eight specialists were interviewed to explore the attitudes and beliefs about CSII. Responses were analysed thematically and used to inform the design of a new 22-item questionnaire: the Attitudes to Pump Therapy (APT) Survey. The APT was pilottested among 95 HCPs (54% male; 75.5% diabetologists/DSNs, 13.8% general practitioners) at the International Diabetes Federation (IDF) conference, 2006. Results were analysed using non-parametric statistics with bonferroni correction.

RESULTS: Analyses of interview data identified 9 themes: biomedical, perceived control of care/diabetes, technology, quality of life, financial resources, training, education & support, suitability, and evidence-base. Items were designed to reflect these themes with responses scored on a 5-point Likert scale (strongly agree—strongly disagree). No statistically significant differences
were found by gender, HCP speciality, country (and continent) of origin or proportion of patients using CSII. Most notable differences were found in relation to gross domestic product (GDP) and the potential for pump therapy to achieve tight blood glucose control (lower GDP = more agreement: p = 0.001), and result in diabetic ketoacidosis (DKA) (lower GDP = less agreement: p < 0.005). Ranked mean scores showed a split between biomedical/clinical items (N = 11) and items concerned with patient experience (N = 11). Attitudes about biomedical/clinical issues were generally clear (i.e. for 7/11 items, the mean score was “agree”) but less decisive about patient experience (i.e. for 8/11 items, the mean score was “neither agree nor disagree”).

CONCLUSION: Few subgroup differences existed, but those that did may be explained by lack of access to treatment (directly corresponding to GDP). Clinicians’ were generally clear in their attitudes regarding biomedical aspects but less so regarding patient experience. Research focusing on patient-reported outcomes is likely to offer clinicians a greater understanding of the patients’ perspective of insulin pump therapy.

Severe hypoglycaemia in type 1 diabetes mellitus: underlying drivers and potential strategies for successful prevention

Hypoglycaemia remains an over-riding factor limiting optimal glycaemic control in type 1 diabetes. Severe hypoglycaemia is prevalent in almost half of those with long-duration diabetes and is one of the most feared diabetes-related complications. In this review, we present an overview of the increasing body of literature seeking to elucidate the underlying pathophysiology of severe hypoglycaemia and the limited evidence behind the strategies employed to prevent episodes. Drivers of severe hypoglycaemia including impaired counter-regulation, hypoglycaemia-associated autonomic failure, psychosocial and behavioural factors and neuroimaging correlates are discussed. Treatment strategies encompassing structured education, insulin analogue regimens, continuous subcutaneous insulin infusion pumps, continuous glucose sensing and beta-cell replacement therapies have been employed, yet there is little randomized controlled trial evidence demonstrating effectiveness of new technologies in reducing severe hypoglycaemia. Optimally designed interventional trials evaluating these existing technologies and using modern methods of teaching patients flexible insulin use within structured education programmes with the specific goal of preventing severe hypoglycaemia are required. Individuals at high risk need to be monitored with meticulous collection of data on awareness, as well as frequency and severity of all hypoglycaemic episodes.

An Actor-Critic based controller for glucose regulation in type 1 diabetes

A novel adaptive approach for glucose control in individuals with type 1 diabetes under sensor-augmented pump therapy is proposed. The controller, is based on Actor-Critic (AC) learning and is inspired by the principles of reinforcement learning and optimal control theory. The main characteristics of the proposed controller are (i) simultaneous adjustment of both the insulin basal rate and the bolus dose, (ii) initialization based on clinical procedures, and (iii) real-time personalization. The effectiveness of the proposed algorithm in terms of glycemic control has been investigated in silico in adults, adolescents and children under open-loop and closed-loop approaches, using announced meals with uncertainties in the order of ±25% in the estimation of carbohydrates. The results show that glucose regulation is efficient in all three groups of patients, even with uncertainties in the level of carbohydrates in the meal. The percentages in the A+B zones of the Control Variability Grid Analysis (CVGA) were 100% for adults, and 93% for both adolescents and children. The AC based controller seems to be a promising approach for the automatic adjustment of insulin infusion in order to improve glycemic control. After optimization of the algorithm, the controller will be tested in a clinical trial.

Dispositivi per l'assistenza "home care" dei pazienti diabetici

L’ obiettivo della tesi proposta è volto ad illustrare come la malattia diabetica può essere gestita a livello domiciliare attraverso dispositivi di monitoraggio della glicemia sempre più innovativi. La malattia diabetica è un disturbo metabolico che ha come manifestazione principale un aumento del livello di zucchero nel sangue (glicemia) dovuto ad una ridotta produzione di insulina, l’ormone secreto dal pancreas per utilizzare gli zuccheri e gli altri componenti del cibo e trasformarli in energia. È una delle patologie croniche a più ampia diffusione nel mondo, in particolare nei Paesi industrializzati, e costituisce una delle più rilevanti e costose malattie sociali della nostra epoca, soprattutto per il suo carattere di cronicità, per la tendenza a determinare complicanze nel lungo periodo e per il progressivo spostamento dell’insorgenza verso età giovanili. Le tecnologie applicate alla terapia del diabete hanno consentito negli ultimi vent’anni di raggiungere traguardi molto importanti, soprattutto per quanto riguarda l’ottimizzazione del controllo assiduo dei valori glicemici cercando di mantenerli il più costante possibile e ad un livello simile a quello fisiologico. La comunicazione medico-paziente è stata rivoluzionata dalla telemedicina che, offrendo la possibilità di una comunicazione agevole, permette di ottimizzare l’utilizzo dei dati raccolti attraverso l’automonitoraggio glicemico e di facilitare gli interventi educativi. I glucometri, che misurano la glicemia ‘capillare’, insieme ai microinfusori, sistemi di erogazione dell’insulina sia in maniera continua (fabbisogno basale), che ‘a domanda’ (boli prandiali), hanno sostanzialmente modificato l’approccio e la gestione del diabete da parte del medico, ma soprattutto hanno favorito al paziente diabetico un progressivo superamento delle limitazioni alle normali attività della vita imposte dalla malattia. Con il monitoraggio continuo della glicemia 24 ore su 24 infatti, si ha avuto il vantaggio di avere a disposizione un elevato numero di misurazioni puntiformi nell’arco della giornata attraverso sensori glicemici, che applicati sulla pelle sono in grado di ‘rilevare’ il valore di glucosio a livello interstiziale, per diversi giorni consecutivi e per mezzo di un trasmettitore wireless, inviano le informazioni al ricevitore che visualizza le letture ottenute dal sensore. In anni recenti, il concetto di SAP (Sensor-Augmented Insulin Pump) Therapy, è stato introdotto a seguito di studi che hanno valutato l’efficacia dell’utilizzo della pompa ad infusione continua di insulina (CSII, continuous subcutaneous insulin infusion) associato ai sistemi di monitoraggio in continuo della glicemia (CGM, continuous glucose monitoring) per un significativo miglioramento del controllo glicemico e degli episodi sia di ipoglicemia sia di iperglicemia prolungata. Oggi, grazie ad una nuova funzione è possibile interrompere automaticamente l’erogazione di insulina da parte del microinfusore quando la glicemia, rilevata dal sensore, scende troppo velocemente e raggiunge un limite di allarme. Integrare lettura della glicemia, infusione e sospensione automatica dell’erogazione di insulina in caso di ipoglicemia ha ovviamente aperto la porta al pancreas artificiale.

An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models

Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.