RESUMO
BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
Assuntos
Hiperglicemia , Hipoglicemia , Adulto , Humanos , Glicemia , Automonitorização da Glicemia , Hipoglicemia/diagnóstico , Hiperglicemia/diagnóstico , GlucoseRESUMO
Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 4 to November 6, 2021. This meeting brought together speakers to discuss various developments within the field of diabetes technology. Meeting topics included blood glucose monitoring, continuous glucose monitoring, novel sensors, direct-to-consumer telehealth, metrics for glycemia, software for diabetes, regulation of diabetes technology, diabetes data science, artificial pancreas, novel insulins, insulin delivery, skin trauma, metabesity, precision diabetes, diversity in diabetes technology, use of diabetes technology in pregnancy, and green diabetes. A live demonstration on a mobile app to monitor diabetic foot wounds was presented.
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Diabetes Mellitus Tipo 1 , Diabetes Mellitus , Glicemia , Automonitorização da Glicemia , Diabetes Mellitus/tratamento farmacológico , Diabetes Mellitus Tipo 1/tratamento farmacológico , Feminino , Humanos , Insulina/uso terapêutico , Sistemas de Infusão de Insulina , Gravidez , TecnologiaRESUMO
BACKGROUND: Artificial pancreas (AP) systems reduce the treatment burden of Type 1 Diabetes by automatically regulating blood glucose (BG) levels. While many disturbances stand in the way of fully closed-loop (automated) control, unannounced meals remain the greatest challenge. Furthermore, different types of meals can have significantly different glucose responses, further increasing the uncertainty surrounding the meal. METHODS: Effective attenuation of a meal requires quick and accurate insulin delivery because of slow insulin action relative to meal effects on BG. The proposed Variable Hump (VH) model adapts to meals of varying compositions by inferring both meal size and shape. To appropriately address the uncertainty of meal size, the model divides meal absorption into two disjoint regions: a region with coarse meal size predictions followed by a fine-grain region where predictions are fine-tuned by adapting to the meal shape. RESULTS: Using gold-standard triple tracer meal data, the proposed VH model is compared to three simpler second-order response models. The proposed VH model increased model fit capacity by 22% and prediction accuracy by 12% relative to the next best models. A 47% increase in the accuracy of uncertainty predictions was also found. In a simple control scenario, the controller governed by the proposed VH model provided insulin just as fast or faster than the controller governed by the other models in four out of the six meals. While the controllers governed by the other models all delivered at least a 25% excess of insulin at their worst, the VH model controller only delivered 9% excess at its worst. CONCLUSIONS: The VH Model performed best in accuracy metrics and succeeded over the other models in providing insulin quickly and accurately in a simple implementation. Use in an AP system may improve prediction accuracy and lead to better control around mealtimes.
Assuntos
Diabetes Mellitus Tipo 1 , Pâncreas Artificial , Algoritmos , Glicemia , Automonitorização da Glicemia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Humanos , Hipoglicemiantes , Insulina , Sistemas de Infusão de Insulina , RefeiçõesRESUMO
The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.
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Diabetes Mellitus Tipo 1 , Pâncreas Artificial , Glicemia , Diabetes Mellitus Tipo 1/diagnóstico , Diabetes Mellitus Tipo 1/tratamento farmacológico , Humanos , Hipoglicemiantes , Insulina , Sistemas de Infusão de InsulinaRESUMO
Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.
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Insulina , Pâncreas Artificial , Glicemia , Humanos , Hipoglicemiantes , Sistemas de Infusão de InsulinaRESUMO
Diabetes Technology Society hosted its annual Diabetes Technology Meeting on November 12 to November 14, 2020. This meeting brought together speakers to cover various perspectives about the field of diabetes technology. The meeting topics included artificial intelligence, digital health, telemedicine, glucose monitoring, regulatory trends, metrics for expressing glycemia, pharmaceuticals, automated insulin delivery systems, novel insulins, metrics for diabetes monitoring, and discriminatory aspects of diabetes technology. A live demonstration was presented.
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Diabetes Mellitus Tipo 1 , Diabetes Mellitus , Inteligência Artificial , Glicemia , Automonitorização da Glicemia , Diabetes Mellitus/tratamento farmacológico , Humanos , TecnologiaRESUMO
The last ten years of efforts in developing automated insulin dosing systems have led to one hybrid closed-loop device in the US market with more in the late stages of development. Much of the focus has been on algorithms, including closed-loop, detection of sensor and pump faults, and safety. There has been less discussion in the open literature about user interface design and related options. This article provides perspectives on automated insulin delivery (AID) system design by analyzing commonly used devices, such as bicycles and car entertainment systems. The recent Boeing 737 Max 8 disasters are used to highlight related challenges with AID systems. The role that system engineers can play in the do it yourself artificial pancreas system movement is also discussed. The human-in-the-loop remains by far the most important "component" of any AID system.
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Diabetes Mellitus Tipo 1 , Pâncreas Artificial , Glicemia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Sistemas de Infusão de InsulinaRESUMO
BACKGROUND: Initial Food and Drug Administration-approved artificial pancreas (AP) systems will be hybrid closed-loop systems that require prandial meal announcements and will not eliminate the burden of premeal insulin dosing. Multiple model probabilistic predictive control (MMPPC) is a fully closed-loop system that uses probabilistic estimation of meals to allow for automated meal detection. In this study, we describe the safety and performance of the MMPPC system with announced and unannounced meals in a supervised hotel setting. RESEARCH DESIGN AND METHODS: The Android phone-based AP system with remote monitoring was tested for 72 h in six adults and four adolescents across three clinical sites with daily exercise and meal challenges involving both three announced (manual bolus by patient) and six unannounced (no bolus by patient) meals. Safety criteria were predefined. Controller aggressiveness was adapted daily based on prior hypoglycemic events. RESULTS: Mean 24-h continuous glucose monitor (CGM) was 157.4 ± 14.4 mg/dL, with 63.6 ± 9.2% of readings between 70 and 180 mg/dL, 2.9 ± 2.3% of readings <70 mg/dL, and 9.0 ± 3.9% of readings >250 mg/dL. Moderate hyperglycemia was relatively common with 24.6 ± 6.2% of readings between 180 and 250 mg/dL, primarily within 3 h after a meal. Overnight mean CGM was 139.6 ± 27.6 mg/dL, with 77.9 ± 16.4% between 70 and 180 mg/dL, 3.0 ± 4.5% <70 mg/dL, 17.1 ± 14.9% between 180 and 250 mg/dL, and 2.0 ± 4.5%> 250 mg/dL. Postprandial hyperglycemia was more common for unannounced meals compared with announced meals (4-h postmeal CGM 197.8 ± 44.1 vs. 140.6 ± 35.0 mg/dL; P < 0.001). No participants met safety stopping criteria. CONCLUSIONS: MMPPC was safe in a supervised setting despite meal and exercise challenges. Further studies are needed in a less supervised environment.
Assuntos
Glicemia/análise , Diabetes Mellitus Tipo 1/tratamento farmacológico , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Pâncreas Artificial , Adolescente , Adulto , Automonitorização da Glicemia , Diabetes Mellitus Tipo 1/sangue , Feminino , Humanos , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Masculino , Resultado do Tratamento , Adulto JovemRESUMO
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.
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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-IdadeRESUMO
The artificial pancreas (AP) is a closed-loop device with the potential to reduce the complications associated with type 1 diabetes mellitus by maintaining euglycemia in patients. The AP encompasses an algorithm that determines the amount of insulin (and other hormones) to be administered to the patient via a continuous subcutaneous insulin infusion pump using information provided by a continuous glucose monitor and other sensors. As the AP approaches commercialization, special attention must be given to safety within all the individual components, including physiological changes in the patient, as well as safety issues that can arise when these components are combined into a single system. Therefore, we analyzed the specific hazards applicable to the AP with the aim of exposing areas of safety that are yet to be addressed.
Assuntos
Pâncreas Artificial/efeitos adversos , Automonitorização da Glicemia , Glucagon/administração & dosagem , Guias como Assunto , Humanos , Injeções Subcutâneas , Sistemas de Infusão de InsulinaRESUMO
OBJECTIVE: A fully closed-loop insulin-only system was developed to provide glucose control in patients with type 1 diabetes without requiring announcement of meals or activity. Our goal was to assess initial safety and efficacy of this system. RESEARCH DESIGN AND METHODS: The multiple model probabilistic controller (MMPPC) anticipates meals when the patient is awake. The controller used the subject's basal rates and total daily insulin dose for initialization. The system was tested at two sites on 10 patients in a 30-h inpatient study, followed by 15 subjects at three sites in a 54-h supervised hotel study, where the controller was challenged by exercise and unannounced meals. The system was implemented on the UVA DiAs system using a Roche Spirit Combo Insulin Pump and a Dexcom G4 Continuous Glucose Monitor. RESULTS: The mean overall (24-h basis) and nighttime (11 PM-7 AM) continuous glucose monitoring (CGM) values were 142 and 125 mg/dL during the inpatient study. The hotel study used a different daytime tuning and manual announcement, instead of automatic detection, of sleep and wake periods. This resulted in mean overall (24-h basis) and nighttime CGM values of 152 and 139 mg/dL for the hotel study and there was also a reduction in hypoglycemia events from 1.6 to 0.91 events/patient/day. CONCLUSIONS: The MMPPC system achieved a mean glucose that would be particularly helpful for people with an elevated A1c as a result of frequent missed meal boluses. Current full closed loop has a higher risk for hypoglycemia when compared with algorithms using meal announcement.
Assuntos
Diabetes Mellitus Tipo 1/terapia , Hiperglicemia/prevenção & controle , Hipoglicemia/prevenção & controle , Refeições , Pâncreas Artificial/efeitos adversos , Acelerometria , Atividades Cotidianas , Adulto , Algoritmos , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Exercício Físico , Estudos de Viabilidade , Feminino , Seguimentos , Hospitalização , Humanos , Hipoglicemia/epidemiologia , Hipoglicemia/etiologia , Masculino , Teste de Materiais , Risco , Lanches , Estados Unidos/epidemiologia , Adulto JovemRESUMO
OBJECTIVE: As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)-based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively. RESEARCH DESIGN AND METHODS: A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.0 ± 1.7% HbA1c) over 2 weeks and compared with SAP therapy for 2 weeks in a crossover, unblinded outpatient study with remote monitoring in both study arms. RESULTS: AP improved percent time 70-140 mg/dL (48.1 vs. 39.2%; P = 0.016) and time 70-180 mg/dL (71.6 vs. 65.2%; P = 0.008) and decreased median glucose (141 vs. 153 mg/dL; P = 0.036) and glycemic variability (SD 52 vs. 55 mg/dL; P = 0.044) while decreasing percent time <70 mg/dL (1.3 vs. 2.7%; P = 0.001). AP also improved overnight control, as measured by mean glucose at 0600 h (140 vs. 158 mg/dL; P = 0.02). IIS failures (1.26 ± 1.44 vs. 0.78 ± 0.78 events; P = 0.13) and sensor failures (0.84 ± 0.6 vs. 1.1 ± 0.73 events; P = 0.25) were similar between AP and SAP arms. Higher percent time in closed loop was associated with better glycemic outcomes. CONCLUSIONS: Zone-MPC significantly and safely improved glycemic control in a home-use environment despite prolonged CGM and IIS wear. This project represents the first home-use AP study attempting to provoke and detect component failure while successfully maintaining safety and effective glucose control.
Assuntos
Diabetes Mellitus Tipo 1/terapia , Pâncreas Artificial , Adolescente , Adulto , Glicemia/metabolismo , Estudos Cross-Over , Feminino , Hemoglobinas Glicadas/metabolismo , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Sistemas de Infusão de Insulina , Masculino , Pacientes Ambulatoriais , Smartphone , Adulto JovemRESUMO
Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis-a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios.
Assuntos
Automonitorização da Glicemia , Glicemia , Diabetes Mellitus Tipo 1 , Humanos , Hipoglicemiantes , Insulina , Sistemas de Infusão de InsulinaRESUMO
OBJECTIVE: The objective of this study was to determine the safety, feasibility, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system compared with predictive low-glucose insulin suspension (PLGS) alone in overnight glucose control. RESEARCH DESIGN AND METHODS: A 42-night trial was conducted in 30 individuals with type 1 diabetes in the age range 15-45 years. Participants were randomly assigned each night to either PHHM or PLGS and were blinded to the assignment. The system suspended the insulin pump on both the PHHM and PLGS nights for predicted hypoglycemia but delivered correction boluses for predicted hyperglycemia on PHHM nights only. The primary outcome was the percentage of time spent in a sensor glucose range of 70-180 mg/dL during the overnight period. RESULTS: The addition of automated insulin delivery with PHHM increased the time spent in the target range (70-180 mg/dL) from 71 ± 10% during PLGS nights to 78 ± 10% during PHHM nights (P < 0.001). The average morning blood glucose concentration improved from 163 ± 23 mg/dL after PLGS nights to 142 ± 18 mg/dL after PHHM nights (P < 0.001). Various sensor-measured hypoglycemic outcomes were similar on PLGS and PHHM nights. All participants completed 42 nights with no episodes of severe hypoglycemia, diabetic ketoacidosis, or other study- or device-related adverse events. CONCLUSIONS: The addition of a predictive hyperglycemia minimization component to our existing PLGS system was shown to be safe, feasible, and effective in overnight glucose control.
Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Hiperglicemia/tratamento farmacológico , Hipoglicemia/tratamento farmacológico , Hipoglicemiantes/administração & dosagem , Sistemas de Infusão de Insulina , Insulina/administração & dosagem , Adolescente , Adulto , Glicemia/metabolismo , Automonitorização da Glicemia , Método Duplo-Cego , Estudos de Viabilidade , Feminino , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Masculino , Adulto JovemRESUMO
The development of a closed-loop artificial pancreas to regulate the blood glucose concentration of individuals with type 1 diabetes has been a focused area of research for over 50 years, with rapid progress during the past decade. The daily control challenges faced by someone with type 1 diabetes include asymmetric objectives and risks, and one-sided manipulated input action with frequent relatively fast disturbances. The major automation steps toward a closed-loop artificial pancreas include (i) monitoring and overnight alarms for hypoglycemia (low blood glucose); (ii) overnight low glucose suspend (LGS) systems to prevent hypoglycemia; and (iii) fully closed-loop systems that adjust insulin (and perhaps glucagon) to maintain desired blood glucose levels day and night. We focus on the steps that we used to develop and test a probabilistic, risk-based, model predictive control strategy for a fully closed-loop artificial pancreas. We complete the paper by discussing ramifications of lessons learned for chemical process systems applications.
RESUMO
OBJECTIVE: Nocturnal hypoglycemia can cause seizures and is a major impediment to tight glycemic control, especially in young children with type 1 diabetes. We conducted an in-home randomized trial to assess the efficacy and safety of a continuous glucose monitor-based overnight predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS: In two age-groups of children with type 1 diabetes (11-14 and 4-10 years of age), a 42-night trial for each child was conducted wherein each night was assigned randomly to either having the PLGS system active (intervention night) or inactive (control night). The primary outcome was percent time <70 mg/dL overnight. RESULTS: Median time at <70 mg/dL was reduced by 54% from 10.1% on control nights to 4.6% on intervention nights (P < 0.001) in 11-14-year-olds (n = 45) and by 50% from 6.2% to 3.1% (P < 0.001) in 4-10-year-olds (n = 36). Mean overnight glucose was lower on control versus intervention nights in both age-groups (144 ± 18 vs. 152 ± 19 mg/dL [P < 0.001] and 153 ± 14 vs. 160 ± 16 mg/dL [P = 0.004], respectively). Mean morning blood glucose was 159 ± 29 vs. 176 ± 28 mg/dL (P < 0.001) in the 11-14-year-olds and 154 ± 25 vs. 158 ± 22 mg/dL (P = 0.11) in the 4-10-year-olds, respectively. No differences were found between intervention and control in either age-group in morning blood ketosis. CONCLUSIONS: In 4-14-year-olds, use of a nocturnal PLGS system can substantially reduce overnight hypoglycemia without an increase in morning ketosis, although overnight mean glucose is slightly higher.
Assuntos
Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Cetoacidose Diabética/sangue , Cetoacidose Diabética/prevenção & controle , Hipoglicemia/sangue , Monitorização Fisiológica/métodos , Sono , Automonitorização da Glicemia/métodos , Criança , Pré-Escolar , Ritmo Circadiano , Diabetes Mellitus Tipo 1/diagnóstico , Feminino , Humanos , Hipoglicemia/diagnóstico , Hipoglicemiantes/uso terapêutico , MasculinoRESUMO
Soon after the discovery that insulin regulates blood glucose by Banting and Best in 1922, the symptoms and risks associated with hypoglycemia became widely recognized. This article reviews devices to warn individuals of impending hypo- and hyperglycemia; biosignals used by these devices include electroencephalography, electrocardiography, skin galvanic resistance, diabetes alert dogs, and continuous glucose monitors (CGMs). While systems based on other technology are increasing in performance and decreasing in size, CGM technology remains the best method for both reactive and predictive alarming of hypo- or hyperglycemia.
Assuntos
Automonitorização da Glicemia/instrumentação , Glicemia/análise , Diabetes Mellitus Tipo 1/sangue , Hiperglicemia/diagnóstico , Hipoglicemia/diagnóstico , Monitorização Ambulatorial/métodos , Algoritmos , Humanos , Hiperglicemia/sangue , Hipoglicemia/sangueRESUMO
Continuous glucose monitors (CGMs) provide real-time interstitial glucose concentrations that are essential for automated treatment of individuals with type 1 diabetes. Miscalibration, noise spikes, dropouts, or pressure applied to the site (e.g., lying on the site while sleeping) can cause inaccurate glucose signals, which could lead to inappropriate insulin dosing decisions. These studies focus on the problem of pressure-induced sensor attenuations (PISAs) that occur overnight and can cause undesirable pump shut-offs in a predictive low glucose suspend system. The algorithm presented here uses real-time CGM readings without knowledge of meals, insulin doses, activity, sensor recalibrations, or fingerstick measurements. The real-time PISA detection technique was tested on outpatient "in-home" data from a predictive low-glucose suspend trial with over 1125 nights of data. A total of 178 sets were created by using different parameters for the PISA detection algorithm to illustrate its range of available performance. The tracings were reviewed via a web-based analysis tool by an engineer with an extensive expertise on analyzing clinical datasets and ~3% of the CGM readings were marked as PISA events which were used as the gold standard. It is shown that 88.34% of the PISAs were successfully detected by the algorithm, and the percentage of false detections could be reduced to 1.70% by altering the algorithm parameters. Use of the proposed PISA detection method can result in a significant decrease in undesirable pump suspensions overnight, and may lead to lower overnight mean glucose levels while still achieving a low risk of hypoglycemia.