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1.
Endocr Pract ; 2020 Mar 11.
Artigo em Inglês | MEDLINE | ID: mdl-32160040

RESUMO

Objective: Software updatable insulin pumps, such as the t:slim X2 pump from Tandem Diabetes Care, enable access to new technology as soon as it is commercialized. The remote software update process allows for minimal interruption in therapy compared to purchasing a new pump, however little quantitative data exists on the software update process nor on pre/post therapeutic outcomes. We examined real-world usage and impact of a remote software updatable predictive low-glucose suspend (PLGS) technology designed to reduce hypoglycemic events in people with insulin-dependent diabetes. Methods: Approximately 15,000 U.S. Tandem pump users remotely updated their t:slim X2 software to Basal-IQ PLGS technology since its commercial release. We performed a retrospective analysis of users who uploaded at least 21 days of pre/post PLGS update usage data to the Tandem t:connect web application between August 28, 2018 and October 21, 2019 (n=6,170). Insulin delivery and sensor-glucose values were analyzed per recent international consensus and ADA guidelines. Software update performance was also assessed. Results: Median software update time was 5.36 minutes. Overall glycemic outcomes for pre and post software update showed a decrease in sensor time <70 mg/dL from 2.14 to 1.18% (-1.01, 95% CI -0.97, -1.05, p<0.001), with overall sensor time 70-180 mg/dL increasing from 57.8 to 58.5% (0.64, 95% CI 0.04, 1.24, p<0.001). These improvements were sustained at 3, 6 and 9 months after the update. Conclusion: Introduction of a software updatable PLGS algorithm for the Tandem t:slim X2 insulin pump resulted in sustained reductions of hypoglycemia.

2.
Diabetes Technol Ther ; 22(S1): S47-S62, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-32069159
3.
Diabetes Care ; 43(3): 607-615, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31937608

RESUMO

OBJECTIVE: Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system. RESEARCH DESIGN AND METHODS: This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA1c <10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L. RESULTS: Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n = 65) versus SAP (n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference -1.7% [95% CI -2.4, -1.0]; P < 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference -3.0% [95% CI -6.1, 0.1]; P < 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device. CONCLUSIONS: In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.

4.
N Engl J Med ; 381(18): 1707-1717, 2019 10 31.
Artigo em Inglês | MEDLINE | ID: mdl-31618560

RESUMO

BACKGROUND: Closed-loop systems that automate insulin delivery may improve glycemic outcomes in patients with type 1 diabetes. METHODS: In this 6-month randomized, multicenter trial, patients with type 1 diabetes were assigned in a 2:1 ratio to receive treatment with a closed-loop system (closed-loop group) or a sensor-augmented pump (control group). The primary outcome was the percentage of time that the blood glucose level was within the target range of 70 to 180 mg per deciliter (3.9 to 10.0 mmol per liter), as measured by continuous glucose monitoring. RESULTS: A total of 168 patients underwent randomization; 112 were assigned to the closed-loop group, and 56 were assigned to the control group. The age range of the patients was 14 to 71 years, and the glycated hemoglobin level ranged from 5.4 to 10.6%. All 168 patients completed the trial. The mean (±SD) percentage of time that the glucose level was within the target range increased in the closed-loop group from 61±17% at baseline to 71±12% during the 6 months and remained unchanged at 59±14% in the control group (mean adjusted difference, 11 percentage points; 95% confidence interval [CI], 9 to 14; P<0.001). The results with regard to the main secondary outcomes (percentage of time that the glucose level was >180 mg per deciliter, mean glucose level, glycated hemoglobin level, and percentage of time that the glucose level was <70 mg per deciliter or <54 mg per deciliter [3.0 mmol per liter]) all met the prespecified hierarchical criterion for significance, favoring the closed-loop system. The mean difference (closed loop minus control) in the percentage of time that the blood glucose level was lower than 70 mg per deciliter was -0.88 percentage points (95% CI, -1.19 to -0.57; P<0.001). The mean adjusted difference in glycated hemoglobin level after 6 months was -0.33 percentage points (95% CI, -0.53 to -0.13; P = 0.001). In the closed-loop group, the median percentage of time that the system was in closed-loop mode was 90% over 6 months. No serious hypoglycemic events occurred in either group; one episode of diabetic ketoacidosis occurred in the closed-loop group. CONCLUSIONS: In this 6-month trial involving patients with type 1 diabetes, the use of a closed-loop system was associated with a greater percentage of time spent in a target glycemic range than the use of a sensor-augmented insulin pump. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases; iDCL ClinicalTrials.gov number, NCT03563313.).


Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina , Insulina/administração & dosagem , Pâncreas Artificial , Adolescente , Adulto , Idoso , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Desenho de Equipamento , Feminino , Hemoglobina A Glicada/análise , Humanos , Hipoglicemiantes/efeitos adversos , Insulina/efeitos adversos , Sistemas de Infusão de Insulina/efeitos adversos , Masculino , Pessoa de Meia-Idade , Pâncreas Artificial/efeitos adversos , Adulto Jovem
5.
Diabetes Technol Ther ; 21(9): 485-492, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31225739

RESUMO

Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) "higher protein" pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (-32.6 mg/dL; 95% CI -48.4 to -17.2; P < 0.001) and regular pasta (-43.2 mg/dL, 95% CI -58.7 to -27.7; P < 0.001). The difference between the two types of pastas did not reach statistical significance (-11 mg/dL; 95% CI -24.1 to 3.4; P = 0.17). Total glucose area under the curve was also significantly higher for white rice compared with both pastas (P < 0.001 for both comparisons). Conclusions: This exploratory study concluded that different food types of similar macronutrient content (e.g., rice and pasta) generate significantly different postprandial glycemic responses in persons with T1D. These results provide useful insights into the impact of food choices on and optimization of glucose control. Clinical Trial Registry: clinicaltrials.gov NCT03362151.

7.
Diabetes Technol Ther ; 21(2): 73-80, 2019 02.
Artigo em Inglês | MEDLINE | ID: mdl-30649925

RESUMO

BACKGROUND: Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller. METHODS: Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use. RESULTS: The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70-180 mg/dL, and 1.7% [median] time <70 mg/dL). CONCLUSIONS: CGMs and insulin pumps can be interchangeable in the same Mobile AP system, as long as these devices achieve certain levels of reliability and wireless connection stability.


Assuntos
Glicemia/análise , Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina , Insulina/administração & dosagem , Pâncreas Artificial , Adolescente , Adulto , Idoso , Algoritmos , Automonitorização da Glicemia , Diabetes Mellitus Tipo 1/sangue , Feminino , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Masculino , Pessoa de Meia-Idade , Reprodutibilidade dos Testes , Smartphone , Resultado do Tratamento , Adulto Jovem
8.
Diabetes Technol Ther ; 21(1): 35-43, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30547670

RESUMO

BACKGROUND: There is an unmet need for a modular artificial pancreas (AP) system for clinical trials within the existing regulatory framework to further AP research projects from both academia and industry. We designed, developed, and tested the interoperable artificial pancreas system (iAPS) smartphone app that can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms while running on an unlocked smartphone. METHODS: After algorithm verification, hazard and mitigation analysis, and complete system verification of iAPS, six adults with type 1 diabetes completed 1 week of sensor-augmented pump (SAP) use followed by 48 h of AP use with the iAPS, a Dexcom G5 CGM, and either a Tandem or Insulet insulin pump in an investigational device exemption study. The AP system was challenged by participants performing extensive walking without exercise announcement to the controller, multiple large meals eaten out at restaurants, two overnight periods, and multiple intentional connectivity interruptions. RESULTS: Even with these intentional challenges, comparison of the SAP phase with the AP study showed a trend toward improved time in target glucose range 70-180 mg/dL (78.8% vs. 83.1%; P = 0.31), and a statistically significant reduction in time below 70 mg/dL (6.1% vs. 2.2%; P = 0.03). The iAPS system performed reliably and showed robust connectivity with the peripheral devices (99.8% time connected to CGM and 94.3% time in closed loop) while requiring limited user intervention. CONCLUSIONS: The iAPS system was safe and effective in regulating glucose levels under challenging conditions and is suitable for use in unconstrained environments.


Assuntos
Automonitorização da Glicemia/métodos , Diabetes Mellitus Tipo 1/terapia , Sistemas de Infusão de Insulina , Aplicativos Móveis , Pâncreas Artificial , Adulto , Algoritmos , Glicemia/efeitos dos fármacos , Diabetes Mellitus Tipo 1/sangue , Feminino , Humanos , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Masculino , Pesquisa , Smartphone , Resultado do Tratamento
9.
Curr Diab Rep ; 18(10): 88, 2018 08 29.
Artigo em Inglês | MEDLINE | ID: mdl-30159816

RESUMO

PURPOSE OF REVIEW: To provide a current review of closed-loop insulin delivery or artificial pancreas (AP) as therapy for people with type 1 diabetes mellitus (T1D) RECENT FINDINGS: The Medtronic Minimed 670G AP system has been in use in clinical practice since March 2017. Currently, Medtronic is conducting a large randomized clinical trial to evaluate its efficacy further in T1D. Simultaneously, the NIH has funded four research consortia to accelerate progress to approval of other AP and decision support systems. Several research groups are currently developing next-generation AP systems, with a number of companies moving toward releasing closed-loop systems in the future. AP systems are also being tested in select populations such as hypoglycemia-unaware T1D and pregnant T1D. AP research is rapidly advancing. The clinical range of AP will be expanded in the next decade.


Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Sistemas de Infusão de Insulina , Glicemia/metabolismo , Ensaios Clínicos como Assunto , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/terapia , Humanos , Hipoglicemiantes/uso terapêutico , Pâncreas Artificial
10.
Diabetes Care ; 41(10): 2155-2161, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30089663

RESUMO

OBJECTIVE: This study evaluated a new insulin delivery system designed to reduce insulin delivery when trends in continuous glucose monitoring (CGM) glucose concentrations predict future hypoglycemia. RESEARCH DESIGN AND METHODS: Individuals with type 1 diabetes (n = 103, age 6-72 years, mean HbA1c 7.3% [56 mmol/mol]) participated in a 6-week randomized crossover trial to evaluate the efficacy and safety of a Tandem Diabetes Care t:slim X2 pump with Basal-IQ integrated with a Dexcom G5 sensor and a predictive low-glucose suspend algorithm (PLGS) compared with sensor-augmented pump (SAP) therapy. The primary outcome was CGM-measured time <70 mg/dL. RESULTS: Both study periods were completed by 99% of participants; median CGM usage exceeded 90% in both arms. Median time <70 mg/dL was reduced from 3.6% at baseline to 2.6% during the 3-week period in the PLGS arm compared with 3.2% in the SAP arm (difference [PLGS - SAP] = -0.8%, 95% CI -1.1 to -0.5, P < 0.001). The corresponding mean values were 4.4%, 3.1%, and 4.5%, respectively, represent-ing a 31% reduction in the time <70 mg/dL with PLGS. There was no increase in mean glucose concentration (159 vs. 159 mg/dL, P = 0.40) or percentage of time spent >180 mg/dL (32% vs. 33%, P = 0.12). One severe hypoglycemic event occurred in the SAP arm and none in the PLGS arm. Mean pump suspension time was 104 min/day. CONCLUSIONS: The Tandem Diabetes Care Basal-IQ PLGS system significantly reduced hypoglycemia without rebound hyperglycemia, indicating that the system can benefit adults and youth with type 1 diabetes in improving glycemic control.


Assuntos
Automonitorização da Glicemia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemia/prevenção & controle , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina , Insulina/administração & dosagem , Adolescente , Adulto , Idoso , Algoritmos , Glicemia/análise , Automonitorização da Glicemia/métodos , Criança , Estudos Cross-Over , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/diagnóstico , Sistemas de Liberação de Medicamentos , Feminino , Humanos , Hipoglicemia/sangue , Hipoglicemia/induzido quimicamente , Masculino , Pessoa de Meia-Idade , Monitorização Ambulatorial , Adulto Jovem
11.
Diabetes Technol Ther ; 20(7): 455-464, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-29958023

RESUMO

BACKGROUND: We investigated the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the responsiveness of the controller's insulin delivery based on the confidence intervals around predictions of glucose trends. This constitutes a dynamic adaptation of the controller's parameters in contrast with the widespread AP implementation of individualized fixed controller tuning. MATERIALS AND METHODS: After 1 week of sensor-augmented pump (SAP) use, subjects completed a 48-h AP admission that included three meals/day (carbohydrate range 29-57 g/meal), a 1-h unannounced brisk walk, and two overnight periods. Endpoints included sensor glucose percentage time 70-180, <70, >180 mg/dL, number of hypoglycemic events, and assessment of the trust index versus standard eMPC glucose predictions. RESULTS: Baseline characteristics for the 15 subjects who completed the study (mean ± SD) were age 46.1 ± 17.8 years, HbA1c 7.2% ± 1.0%, diabetes duration 26.8 ± 17.6 years, and total daily dose (TDD) 35.5 ± 16.4 U/day. Mean sensor glucose percent time 70-180 mg/dL (88.0% ± 8.0% vs. 74.6% ± 9.4%), <70 mg/dL (1.5% ± 1.9% vs. 7.8% ± 6.0%), and number of hypoglycemic events (0.6 ± 0.6 vs. 6.3 ± 3.4), all showed statistically significant improvement during AP use compared with the SAP run-in (P < 0.001). On average, the trust index enhanced controller responsiveness to predicted hyper- and hypoglycemia by 26% (P < 0.005). CONCLUSIONS: In this population of well-controlled patients, we conclude that eMPC with trust index AP achieved nearly 90% time in the target glucose range. Additional studies will further validate these results.


Assuntos
Automonitorização da Glicemia , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Hipoglicemia/diagnóstico , Sistemas de Infusão de Insulina , Pâncreas Artificial , Adulto , Diabetes Mellitus Tipo 1/tratamento farmacológico , Feminino , Humanos , Hipoglicemia/sangue , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Masculino , Pessoa de Meia-Idade
12.
J Diabetes Sci Technol ; 12(3): 657-664, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29415563

RESUMO

OBJECTIVE: The objective was to investigate the relationship of body mass index (BMI) to differing glycemic responses to psychological stress in patients with type 1 diabetes. METHODS: Continuous blood glucose monitor (CGM) data were collected for 1 week from a total of 37 patients with BMI ranging from 21.5-39.4 kg/m2 (mean = 28.2 ± 4.9). Patients reported daily stress levels (5-point Likert-type scale, 0 = none, 4 = extreme), physical activity, carbohydrate intake, insulin boluses and basal rates. Daily reported carbohydrates, total insulin bolus, and average blood glucose (BG from CGM) were compared among patients based on their BMI levels on days with different stress levels. In addition, daily averages of a model-based "effectiveness index" (quantifying the combined impact of insulin and carbohydrate on glucose levels) were defined and compared across stress levels to capture meal and insulin independent glycemic changes. RESULTS: Analyses showed that patient BMI likely moderated stress related glycemic changes. Linear mixed effect model results were significant for the stress-BMI interaction on both behavioral and behavior-independent glycemic changes. Across participants, under stress, an increase was observed in daily carbohydrate intake and effectiveness index at higher BMI. There was no significant interactive effect on daily insulin or average BG. CONCLUSION: Findings suggest that (1) stress has both behavioral and nonbehavioral glycemic effects on T1D patients and (2) the direction and magnitude of these effects are potentially influenced by level of stress and patient BMI. Possibly responsible for these observed effects are T1D/BMI related alterations in endocrine response.


Assuntos
Glicemia/análise , Índice de Massa Corporal , Diabetes Mellitus Tipo 1/sangue , Estresse Psicológico/sangue , Adulto , Automonitorização da Glicemia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Feminino , Índice Glicêmico , Humanos , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Masculino , Pessoa de Meia-Idade , Pâncreas Artificial
13.
Diabetes Technol Ther ; 20(4): 257-262, 2018 04.
Artigo em Inglês | MEDLINE | ID: mdl-29431513

RESUMO

BACKGROUND: The safety and feasibility of the OmniPod personalized model predictive control (MPC) algorithm in adult, adolescent, and pediatric patients with type 1 diabetes were investigated. METHODS: This multicenter, observational trial included a 1-week outpatient sensor-augmented pump open-loop phase and a 36-h inpatient hybrid closed-loop (HCL) phase with announced meals ranging from 30 to 90 g of carbohydrates and limited physical activity. Patients aged 6-65 years with HbA1c between 6.0% and 10.0% were eligible. The investigational system included a modified version of OmniPod, the Dexcom G4 505 Share® AP System, and the personalized MPC algorithm running on a tablet computer. Primary endpoints included sensor glucose percentage of time in hypoglycemia <70 mg/dL and hyperglycemia >250 mg/dL. Additional glycemic targets were assessed. RESULTS: The percentage of time <70 mg/dL during the 36-h HCL phase was mean (standard deviation): 0.7 (1.7) in adults receiving 80% meal bolus (n = 24), and 0.7 (1.2) in adults (n = 10), 2.0 (2.4) in adolescents (n = 12), and 2.0 (2.6) in pediatrics (n = 12) receiving 100% meal bolus. The overall hypoglycemia rate was 0.49 events/24 h. The percentage of time >250 mg/dL was 8.0 (7.5), 3.6 (3.7), 4.9 (6.3), and 6.7 (5.6) in the study groups, respectively. Percentage of time in the target range of 70-180 mg/dL was 69.5 (14.4), 73.0 (15.0), 72.6 (15.5), and 70.1 (12.3), respectively. CONCLUSIONS: The OmniPod personalized MPC algorithm performed well and was safe during day and night use in adult, adolescent, and pediatric patients with type 1 diabetes. Longer term studies will assess the safety and performance of the algorithm under free living conditions with extended use.


Assuntos
Automonitorização da Glicemia/instrumentação , Glicemia/análise , Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina/efeitos adversos , Insulina/administração & dosagem , Adolescente , Adulto , Idoso , Algoritmos , Criança , Diabetes Mellitus Tipo 1/sangue , Estudos de Viabilidade , Feminino , Hemoglobina A Glicada , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Masculino , Pessoa de Meia-Idade , Adulto Jovem
14.
J Diabetes Sci Technol ; 12(3): 599-607, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29390915

RESUMO

BACKGROUND: As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient's glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures. METHODS: An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed. RESULTS: In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58). CONCLUSIONS: As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875.


Assuntos
Algoritmos , Diabetes Mellitus Tipo 1/tratamento farmacológico , Pâncreas Artificial/efeitos adversos , Adulto , Estudos Cross-Over , Cetoacidose Diabética/etiologia , Cetoacidose Diabética/prevenção & controle , Falha de Equipamento , Feminino , Humanos , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Sistemas de Infusão de Insulina/efeitos adversos , Masculino , Pessoa de Meia-Idade
16.
J Diabetes Sci Technol ; 11(6): 1070-1079, 2017 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-29032732

RESUMO

BACKGROUND: Continuous glucose monitoring (CGM) systems are increasingly becoming essential components in type 1 diabetes mellitus (T1DM) management. Current CGM technology requires frequent calibration to ensure accurate sensor performance. The accuracy of these systems is of great importance since medical decisions are made based on monitored glucose values and trends. METHODS: In this work, we introduce a calibration strategy that is augmented with a weekly updating feature. During the life cycle of the sensor, the calibration mechanism periodically estimates the parameters of a calibration model to fit self-monitoring blood glucose (SMBG) measurements. At the end of each week of use, an optimization problem that minimizes the sum of squared residuals between past reference and predicted blood glucose values is solved remotely to identify personalized calibration parameters. The newly identified parameters are used to initialize the calibration mechanism of the following week. RESULTS: The proposed method was evaluated using two sets of clinical data both consisting of 6 weeks of Dexcom G4 Platinum CGM data on 10 adults with T1DM (over 10 000 hours of CGM use), with seven SMBG data points per day measured by each subject in an unsupervised outpatient setting. Updating the calibration parameters using the history of calibration data indicated a positive trend of improving CGM performance. CONCLUSIONS: Although not statistically significant, the updating framework showed a relative improvement of CGM accuracy compared to the non-updating, static calibration method. The use of information collected for longer periods is expected to improve the performance of the sensor over time.


Assuntos
Algoritmos , Automonitorização da Glicemia/métodos , Glicemia/metabolismo , Diabetes Mellitus Tipo 1/diagnóstico , Processamento de Sinais Assistido por Computador , Adulto , Biomarcadores/sangue , Glicemia/efeitos dos fármacos , Automonitorização da Glicemia/instrumentação , Automonitorização da Glicemia/normas , Calibragem , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 1/tratamento farmacológico , Desenho de Equipamento , Feminino , Humanos , Hipoglicemiantes/uso terapêutico , Análise dos Mínimos Quadrados , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Reprodutibilidade dos Testes , Fatores de Tempo , Transdutores
17.
Diabetes Care ; 40(12): 1719-1726, 2017 12.
Artigo em Inglês | MEDLINE | ID: mdl-29030383

RESUMO

OBJECTIVE: Artificial pancreas (AP) systems are best positioned for optimal treatment of type 1 diabetes (T1D) and are currently being tested in outpatient clinical trials. Our consortium developed and tested a novel adaptive AP in an outpatient, single-arm, uncontrolled multicenter clinical trial lasting 12 weeks. RESEARCH DESIGN AND METHODS: Thirty adults with T1D completed a continuous glucose monitor (CGM)-augmented 1-week sensor-augmented pump (SAP) period. After the AP was started, basal insulin delivery settings used by the AP for initialization were adapted weekly, and carbohydrate ratios were adapted every 4 weeks by an algorithm running on a cloud-based server, with automatic data upload from devices. Adaptations were reviewed by expert study clinicians and patients. The primary end point was change in hemoglobin A1c (HbA1c). Outcomes are reported adhering to consensus recommendations on reporting of AP trials. RESULTS: Twenty-nine patients completed the trial. HbA1c, 7.0 ± 0.8% at the start of AP use, improved to 6.7 ± 0.6% after 12 weeks (-0.3, 95% CI -0.5 to -0.2, P < 0.001). Compared with the SAP run-in, CGM time spent in the hypoglycemic range improved during the day from 5.0 to 1.9% (-3.1, 95% CI -4.1 to -2.1, P < 0.001) and overnight from 4.1 to 1.1% (-3.1, 95% CI -4.2 to -1.9, P < 0.001). Whereas carbohydrate ratios were adapted to a larger extent initially with minimal changes thereafter, basal insulin was adapted throughout. Approximately 10% of adaptation recommendations were manually overridden. There were no protocol-related serious adverse events. CONCLUSIONS: Use of our novel adaptive AP yielded significant reductions in HbA1c and hypoglycemia.


Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Hemoglobina A Glicada/metabolismo , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Adulto , Glicemia , Automonitorização da Glicemia , Feminino , Humanos , Hipoglicemia/tratamento farmacológico , Sistemas de Infusão de Insulina , Masculino , Pessoa de Meia-Idade , Pâncreas Artificial
18.
Diabetes Technol Ther ; 19(12): 744-748, 2017 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-29077488

RESUMO

We sought to determine the real-life experiences of individuals traveling long distance (across five or more time-zones) with type 1 diabetes (T1D). Five hundred three members of the T1D Exchange online community ( www.myglu.org ) completed a 45-question survey about their travel experiences flying long distance. The cohort was stratified by duration of T1D and whether or not participants used continuous subcutaneous insulin infusion (CSII) therapy and/or a continuous glucose monitor (CGM). In the last 5 years, 71% of participants had flown long distance. When asked about their perceived "fear of flying," CSII users (with and without a CGM) reported their primary anxiety was "losing supplies," while non-CSII users described concerns over "unstable blood glucose (highs and lows)" (P < 0.05). In addition, 74% of participants reported more hypoglycemia and/or hyperglycemia while traveling overseas and 9% had avoided international travel altogether because of problems related to diabetes management. Furthermore, 22% of participants had run out of insulin at some point during a trip and 37% reported inadequate attention in current sources of information to the unpredictability of self-management needs while traveling. Especially problematic for individuals traveling with T1D are a lack of resources adequately addressing (1) protocols for emergencies while abroad, (2) how to navigate airport security, and (3) managing basal insulin rates when crossing time zones. A strong need exists for easily accessible, free resources for traveling with T1D that is tailored to both device use and duration of the disease.


Assuntos
Viagem Aérea/psicologia , Diabetes Mellitus Tipo 1/psicologia , Adolescente , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Coortes , Diabetes Mellitus Tipo 1/tratamento farmacológico , Feminino , Humanos , Sistemas de Infusão de Insulina , Masculino , Pessoa de Meia-Idade , Adulto Jovem
19.
Mil Med ; 182(9): e1769-e1772, 2017 09.
Artigo em Inglês | MEDLINE | ID: mdl-28885935

RESUMO

BACKGROUND: In our U.S. Department of Defense hospital system, pediatric endocrinology and radiology resources to evaluate bone age radiographs are limited. Our tertiary care center provides expert specialty support to remotely stationed beneficiaries at more than 30 Department of Defense medical facilities using a well-established, asynchronous, Health Insurance Portability and Accountability Act compliant system that allows for physician-to-physician teleconsultation. Up to 14% of these teleconsultations are for endocrinology assessment, many of which include bone age analysis. We sought to evaluate the feasibility of using an automated bone age analysis program using the file format most commonly provided to us, lossy JPEG image files saved at lower quality, to improve access to our consultation services. METHODS: All patients seen in the Tripler Army Medical Center pediatric endocrinology clinic, who were being evaluated for poor growth during the 2-month study period and who had a bone age film performed at Tripler Army Medical Center during that time, were eligible to have their deidentified bone age films analyzed. We imported lossy JPEG bone age image files from our hospital web viewer to BoneXpert, version 2.1, using a fully automated, custom built system that reconstructed each file's true resolution and then packaged the original image into a Digital Imaging and Communications in Medicine header. The original JPEG files were saved at 70% quality. Bone age readings were compared between our pediatric endocrinologists (ENDO), pediatric radiologists (RADS), and BoneXpert (BONE). Additionally, adult height prediction from ENDO and BONE were compared. FINDINGS: 35 bone age images were evaluated over a 2-month period. Most patients were being evaluated for idiopathic short stature or growth hormone deficiency. Analysis of variance showed no significant differences in mean bone age readings between the 3 groups (mean bone age reading = 9.0, 9.1, and 9.1 years for ENDO, RADS, and BONE, respectively, p = 0.827). Mean (SD) differences between physician and software bone age readings were -0.09 (0.89) years (ENDO) and -0.03 (1.01) years (RADS). Mean difference for adult height predictions was only -0.2 cm (p = 0.806). DISCUSSION: Automated analysis of lossy JPEG files of bone age images using the BoneXpert software appears to be feasible and accurate. Larger studies are needed to validate these results.


Assuntos
Determinação da Idade pelo Esqueleto/instrumentação , Tomografia Computadorizada por Raios X/normas , Adolescente , Determinação da Idade pelo Esqueleto/métodos , Análise de Variância , Criança , Pré-Escolar , Feminino , Humanos , Masculino , Desenho de Programas de Computador , Tomografia Computadorizada por Raios X/métodos , Estados Unidos
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