AIM: In postcardiac surgery patients, we assessed the performance of a system for intensive intravenous insulin therapy using continuous glucose monitoring (CGM) and enhanced model predictive control (eMPC) algorithm. METHODS: Glucose control in eMPC-CGM group (n = 12) was compared with a control (C) group (n = 12) treated by intravenous insulin infusion adjusted according to eMPC protocol with a variable sampling interval alone. In the eMPC-CGM group glucose measured with a REAL-Time CGM system (Guardian RT) served as input for the eMPC adjusting insulin infusion every 15 minutes. The accuracy of CGM was evaluated hourly using reference arterial glucose and Clarke error-grid analysis (C-EGA). Target glucose range was 4.4-6.1 mmol/L. RESULTS: Of the 277 paired CGM-reference glycemic values, 270 (97.5%) were in clinically acceptable zones of C-EGA and only 7 (2.5%) were in unacceptable D zone. Glucose control in eMPC-CGM group was comparable to C group in all measured values (average glycemia, percentage of time above, within, and below target range,). No episode of hypoglycemia (<2.9 mmol) occurred in eMPC-CGM group compared to 2 in C group. CONCLUSION: Our data show that the combination of eMPC algorithm with CGM is reliable and accurate enough to test this approach in a larger study population.

• MeSH
• algoritmy MeSH
• diabetes mellitus 1. typu krev   chirurgie   MeSH
• intravenózní infuze MeSH
• inzulin aplikace a dávkování   MeSH
• jednotky intenzivní péče MeSH
• krevní glukóza analýza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• pooperační období MeSH
• senioři MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Background: Type-1 diabetes is a condition caused by the lack of insulin hormone, which leads to an excessive increase in blood glucose level. The glucose kinetics process is difficult to control due to its complex and nonlinear nature and with state variables that are difficult to measure. Methods: This paper proposes a method for automatically calculating the basal and bolus insulin doses for patients with type-1 diabetes using reinforcement learning with feedforward controller. The algorithm is designed to keep the blood glucose stable and directly compensate for the external events such as food intake. Its performance was assessed using simulation on a blood glucose model. The usage of the Kalman filter with the controller was demonstrated to estimate unmeasurable state variables. Results: Comparison simulations between the proposed controller with the optimal reinforcement learning and the proportional-integral-derivative controller show that the proposed methodology has the best performance in regulating the fluctuation of the blood glucose. The proposed controller also improved the blood glucose responses and prevented hypoglycemia condition. Simulation of the control system in different uncertain conditions provided insights on how the inaccuracies of carbohydrate counting and meal-time reporting affect the performance of the control system. Conclusion: The proposed controller is an effective tool for reducing postmeal blood glucose rise and for countering the effects of external known events such as meal intake and maintaining blood glucose at a healthy level under uncertainties.

• MeSH
• algoritmy * MeSH
• biologické modely MeSH
• diabetes mellitus 1. typu krev   farmakoterapie   MeSH
• inzulin aplikace a dávkování   MeSH
• kinetika MeSH
• krevní glukóza metabolismus   MeSH
• lidé MeSH
• počítačem asistovaná terapie statistika a číselné údaje   MeSH
• počítačová simulace MeSH
• posilování (psychologie) MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH

Východisko. Zvýšení glykémie nad normální mez je u kriticky nemocných pacientů častým jevem. Řada studií dokazuje, že u některých skupin nemocných vede normalizace glykémie intenzifikovanou inzulínovou terapií k výraznému snížení mortality, délky hospitalizace i počtu komplikací. Cílem této pilotní studie bylo porovnat kompenzaci glykémie s použitím počítačového plně automatického prediktivního kontrolního algoritmu s variabilním intervalem zadávání glykémie (eMPC) oproti rutinnímu protokolu pro kontrolu glykémie u kardiochirurgických pacientů (RP) v peri- a pooperačním období. Metody a výsledky. Do studie bylo zařazeno celkem 20 pacientů (14 mužů a 6 žen, průměrný věk 68±10 let, BMI 28,3±5,0 kg/m2). Deset pacientů bylo randomizováno pro léčbu s použitím eMPC protokolu a 10 pacientů za použití RP. Všichni pacienti podstoupili plánovanou kardiochirurgickou operaci a byli léčeni kontinuální infuzí s inzulínem se snahou udržení glykémie v rozmezí 4,4–6,1 mmol/l po dobu 24 hodin. Průměrná hladina glukózy byla signifikantně nižší v eMPC skupině než v RP skupině (5,80±0,45 vs. 7,23±0,84 mmol/l, p<0,05), celková průměrná doba v cílovém rozmezí glykémie byla delší v eMPC než RP skupině (67,6±8,7 % vs. 27,6±15,8 %, p<0,05), zatímco průměrná doba nad cílovým rozmezím byla v eMPC skupině významně kratší. Průměrná rychlost infůze inzulínu byla vyšší u eMPC než u RP skupiny (4,18±1,19 vs. 3,24±1,43 IU/hod., p<0,05). Průměrný interval odběrů glykémie byl signifikantně kratší u eMPC než u RP skupiny (1,51±0,24 vs. 2,03±0,16 hod., p<0,05). V žádné ze skupin se nevyskytla těžší hypoglykémie. Závěry. Výsledky naší pilotní studie dokazují, že eMPC algoritmus je efektivnější při kompenzaci glykémie v peri- a pooperačním období u pacientů po kardiochirurgické operaci a srovnatelně bezpečný oproti rutinnímu protokolu v udržení glykémie.

Background. Increased blood glucose levels are frequently observed in critically ill patients. Recent studies have shown that the normalization of glycemia by intensive insulin therapy decreases mortality, length of the hospitalization and number of complications. Methods and Results. The aim of this pilot study was to compare blood glucose control by an automated model predictive control algorithm with variable sampling rate (eMPC) with routine glucose management protocol (RP) in peri- and postoperative period in cardiac surgery patients. 20 patients were included into this study (14 men and 6 women, mean age 68±10 let, BMI 28.3±5.0 kg/m2). 10 patients were randomized for treatment using eMPC algorithm and 10 patients for routine protocol. All patients underwent elective cardiac surgery and were treated with continuous insulin infusion to maintain glycemia in target range 4.4–6.1 mmol/l. The study duration was 24 hours. Mean blood glucose was significantly lower in eMPC vs. RP group (5.80±0.45 vs. 7.23±0.84 mmol/l, p<0.05). Percentage of time in target range was significantly higher in eMPC vs. RP group (67.6±8.7 % vs. 27.6±15.8 %, p<0.05). Percentage of time above the target range was higher in RP vs. eMPC group. Average insulin infusion rate was higher in eMPC vs. RP group (4.18±1.19 vs. 3.24±1.43 IU/hour, p<0.05). Average sampling interval was significantly shorter in eMPC vs. RP group (1.51±0.24 vs. 2.03±0.16 hour, p<0.05). No severe hypoglycaemia in either group occurred during the study. Conclusions. The results of our pilot study suggest that eMPC algorithm is more effective in maintaining euglycemia in peri- and post-operative period in patients after cardiac surgery and comparably safe as compared to RP.

• MeSH
• algoritmy MeSH
• dospělí MeSH
• financování organizované MeSH
• index tělesné hmotnosti MeSH
• interpretace statistických dat MeSH
• inzulin aplikace a dávkování   farmakologie   terapeutické užití   MeSH
• inzulinová rezistence fyziologie   MeSH
• kardiochirurgické výkony metody   ošetřování   MeSH
• klinické protokoly MeSH
• krevní glukóza analýza   metabolismus   MeSH
• lidé MeSH
• mortalita MeSH
• perioperační péče metody   MeSH
• pilotní projekty MeSH
• počítače statistika a číselné údaje   trendy   využití   MeSH
• pooperační komplikace prevence a kontrola   terapie   MeSH
• pooperační péče metody   MeSH
• primární prevence MeSH
• randomizované kontrolované studie jako téma statistika a číselné údaje   MeSH
• senioři MeSH
• výsledek terapie MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH

During the last 2 decades, the treatment of hyperglycemia in critically ill patients has become one of the most discussed topics in the intensive medicine field. The initial data suggesting significant benefit of normalization of blood glucose levels in critically ill patients using intensive intravenous insulin therapy have been challenged or even neglected by some later studies. At the moment, the need for glucose control in critically ill patients is generally accepted yet the target glucose values are still the subject of ongoing debates. In this review, we summarize the current data on the benefits and risks of tight glucose control in critically ill patients focusing on the novel technological approaches including continuous glucose monitoring and its combination with computer-based algorithms that might help to overcome some of the hurdles of tight glucose control. Since increased risk of hypoglycemia appears to be the major obstacle of tight glucose control, we try to put forward novel approaches that may help to achieve optimal glucose control with low risk of hypoglycemia. If such approaches can be implemented in real-world practice the entire concept of tight glucose control may need to be revisited.

• MeSH
• algoritmy MeSH
• hypoglykemika terapeutické užití   MeSH
• inzulin terapeutické užití   MeSH
• jednotky intenzivní péče MeSH
• krevní glukóza * MeSH
• kritický stav MeSH
• lidé MeSH
• péče o pacienty v kritickém stavu metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

BACKGROUND: Glycaemia control (GC) remains an important therapeutic goal in critically ill patients. The enhanced Model Predictive Control (eMPC) algorithm, which models the behaviour of blood glucose (BG) and insulin sensitivity in individual ICU patients with variable blood samples, is an effective, clinically proven computer based protocol successfully tested at multiple institutions on medical and surgical patients with different nutritional protocols. eMPC has been integrated into the B.Braun Space GlucoseControl system (SGC), which allows direct data communication between pumps and microprocessor. The present study was undertaken to assess the clinical performance and safety of the SGC for glycaemia control in critically ill patients under routine conditions in different ICU settings and with various nutritional protocols. METHODS: The study endpoints were the percentage of time the BG was within the target range 4.4 - 8.3 mmol.l(-1), the frequency of hypoglycaemic episodes, adherence to the advice of the SGC and BG measurement intervals. BG was monitored, and insulin was given as a continuous infusion according to the advice of the SGC. Nutritional management (enteral, parenteral or both) was carried out at the discretion of each centre. RESULTS: 17 centres from 9 European countries included a total of 508 patients, the median study time was 2.9 (1.9-6.1) days. The median (IQR) time-in-target was 83.0 (68.7-93.1) % of time with the mean proposed measurement interval 2.0 ± 0.5 hours. 99.6% of the SGC advices on insulin infusion rate were accepted by the user. Only 4 episodes (0.01% of all BG measurements) of severe hypoglycaemia <2.2 mmol.l(-1) in 4 patients occurred (0.8%; 95% CI 0.02-1.6%). CONCLUSION: Under routine conditions and under different nutritional protocols the Space GlucoseControl system with integrated eMPC algorithm has exhibited its suitability for glycaemia control in critically ill patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01523665.

• MeSH
• inzulin aplikace a dávkování   MeSH
• jednotky intenzivní péče * MeSH
• krevní glukóza účinky léků   metabolismus   MeSH
• kritický stav terapie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• péče o pacienty v kritickém stavu metody   MeSH
• senioři MeSH
• systémy pro podporu klinického rozhodování * přístrojové vybavení   MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• klinické zkoušky MeSH
• multicentrická studie MeSH
• pozorovací studie MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa epidemiologie   MeSH

Alarmy u systémů pro kontinuální monitoraci glykemie (CGM) představují velmi důležitý prvek, umožňující pacientům, kteří tyto systémy využívají, udržet glykemii v cílovém rozmezí a vyvarovat se případných exkurzí do hypoglykemie nebo hyperglykemie. Právě možnost upozornit pacienta na překročení hranice cílového pásma znamená pro CGM hlavní výhodu oproti selfmonitoringu glykemie pomocí osobních glukometrů, ale také (zatím) systému pro intermitentní scanování glykemie. Existuje však překvapivě velmi málo studií, které by se zabývaly specificky vztahem mezi konkrétním nastavením alarmů a glykemickou kompenzací. Proto nejsou momentálně k dispozici žádná doporučení ani návod, jak alarmy u CGM optimálně nastavit. Z omezeného množství studií vyplývá, že nastavení hranice alarmu pro hypoglykemii na hodnotu vyšší než 4,0 mmol/l je provázeno nižší frekvencí a dobou trvání hypoglykemií, přičemž u pacientů s poruchou rozpoznávání hypoglykemií může být vhodné tuto hranici dočasně navýšit až na hodnotu 6 mmol/l.

Alarms in continuous glucose monitoring systems (CGM) represent a very important feature enabling to patients with diabetes who use these systems to keep their blood glucose level in the target range and to avoid excursion to hypoglycemia or hyperglycemia. The possibility to warn the patient that the target range has been crossed means one of the main advantages of CGM over the selfmonitoring of blood glucose with personal glucometers, but also (so far) flash glucose monitoring systems. However, there is surprisingly few studies concerning specifically the relationship between the alarms settings and glucose control. Therefore, there are currently no recommendations nor guidelines for optimal settings of alarms in CGM. Limited number of studies suggest that the setting of the hypoglycemia alarm to a level higher than 4 mmol/L is associated with lower frequency and shorter duration of hypoglycemia, and may be temporarily increased to 6 mmol/L in patients with impaired hypoglycemia awareness.

• MeSH
• algoritmy MeSH
• diabetes mellitus 1. typu * farmakoterapie   MeSH
• hyperglykemie prevence a kontrola   MeSH
• hypoglykemie diagnóza   prevence a kontrola   MeSH
• inzulinové infuzní systémy MeSH
• krevní glukóza MeSH
• lidé MeSH
• selfmonitoring glykemie * metody   normy   přístrojové vybavení   MeSH
• Check Tag
• lidé MeSH

BACKGROUND AND OBJECTIVE: Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit. METHODS: We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction. RESULTS: To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%. CONCLUSIONS: When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity.

• MeSH
• algoritmy MeSH
• diabetes mellitus 1. typu * MeSH
• inzulin MeSH
• krevní glukóza MeSH
• lidé MeSH
• selfmonitoring glykemie * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

This pilot study deals with the possibilities of a Continuous Glucose Monitoring System (CGMS, Minimed- Medtronic) to optimize insulin substitution. Ten persons with type 1 diabetes mellitus treated by means of an insulin pump entered the study and eight of them completed the protocol. CGMS was introduced for a period of 5 days. The standard dinner (60 g of carbohydrates) and overnight fasting were designed to ensure standard night conditions in all persons in the study while maintaining their usual daily eating routine, physical exercise and assessment of prandial insulin boluses. The only adaptation of basal rates of insulin pump was performed on day 3. Comparison of the mean plasma glucose concentration (0:00-24:00 hrs) between day 2 (before adaptation) and day 4 (following adaptation) was made. An independent comparison of the mean plasma glucose concentration between the night from day 2 till day 3 (22:00-6:00 hrs) and the night from day 4 till day 5 (22:00-6:00 hrs) was performed. The mean plasma glucose investigated by means of CGMS improved in the 24-hour period in 5 out of 8 persons and in the night fasting period (22:00 to 6 hrs) in 6 out of 8 persons. The CGMS is a useful means for assessment of the effectiveness of basal rate and prandial insulin doses in persons with type 1 diabetes treated by means of an insulin pump. However, further studies are necessary to improve the algorithm for insulin substitution.

• MeSH
• ambulantní monitorování MeSH
• diabetes mellitus 1. typu krev   farmakoterapie   MeSH
• dospělí MeSH
• hypoglykemika aplikace a dávkování   MeSH
• inzulin aplikace a dávkování   MeSH
• inzulinové infuzní systémy MeSH
• krevní glukóza analýza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

INTRODUCTION: The aim of the study was to assess the differences in key parameters of type 1 diabetes (T1D) control associated with treatment and monitoring modalities including newly introduced hybrid closed-loop (HCL) algorithm in children and adolescents with T1D (CwD) using the data from the population-wide pediatric diabetes registry ČENDA. METHODS: CwD younger than 19 years with T1D duration >1 year were included and divided according to the treatment modality and type of CGM used: multiple daily injection (MDI), insulin pump without (CSII) and with HCL function, intermittently scanned continuous glucose monitoring (isCGM), real-time CGM (rtCGM), and intermittent or no CGM (noCGM). HbA1c, times in glycemic ranges, and glucose risk index (GRI) were compared between the groups. RESULTS: Data of a total of 3,251 children (mean age 13.4 ± 3.8 years) were analyzed. 2,187 (67.3%) were treated with MDI, 1,064 (32.7%) with insulin pump, 585/1,064 (55%) with HCL. The HCL users achieved the highest median TIR 75.4% (IQR 6.3) and lowest GRI 29.1 (7.8), both p < 0.001 compared to other groups, followed by MDI rtCGM and CSII groups with TIR 68.8% (IQR 9.0) and 69.0% (7.5), GRI 38.8 (12.5) and 40.1 (8.5), respectively (nonsignificant to each other). These three groups did not significantly differ in their HbA1c medians (51.8 [IQR 4.5], 50.7 [4.5], and 52.7 [5.7] mmol/mol, respectively). NoCGM groups had the highest HbA1c and GRI and lowest TIR regardless of the treatment modality. CONCLUSION: This population-based study shows that the HCL technology is superior to other treatment modalities in CGM-derived parameters and should be considered as a treatment of choice in all CwD fulfilling the indication criteria.

• MeSH
• diabetes mellitus 1. typu * farmakoterapie   MeSH
• dítě MeSH
• glykovaný hemoglobin MeSH
• hypoglykemika terapeutické užití   MeSH
• inzulin terapeutické užití   MeSH
• krevní glukóza MeSH
• lidé MeSH
• mladiství MeSH
• regulace glykemie MeSH
• selfmonitoring glykemie MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• Publikační typ
• časopisecké články MeSH

OBJECTIVE: We aim to demonstrate that low dose growth hormone (GH) administered in i.v. pulses every 3h is able to normalize IGF-I levels in subjects with prolonged critical illness, after multiple trauma. We also ask whether it is possible to control glycaemia during such a treatment and how alanylglutamine (AG) supplementation influences plasma glutamine concentration. METHODS: We used a prospective double-blind (group 1 vs. 2), randomized trial with an open-label control arm (group 3). Thirty multiple trauma patients (median age: 36, 42, 46 years) were randomized on day 4 after trauma to receive (group 1, n=10) i.v. AG supplementation (0.3 g/kg day from day 4 till 17) and i.v. GH (0.05 mg/kg day divided into 8 boluses, maximum dose at 3 AM, administered on days 7-17) or AG and placebo (group 2, n=10). Group 3 (n=10) received isocaloric isonitrogenous (proteins 1.5 g/kg day) nutrition without AG. Glycaemia was controlled by i.v. insulin infusion according to a routine protocol. RESULTS: GH treatment caused an increase of IGF-I (from median 169 on day 4 to 493 ng/ml on day 17), IGFBP-3 (from 2.4 to 3.2 microg/ml) and a fall in IGFBP-1 (from 11.5 to 3.1 microg/ml), whilst in both groups 2 and 3 these indices remained unchanged. At the end of the study (day 17) IGF-I and IGFBP-1 differed significantly among groups (p=0.008 resp. p=0.010, Kruskal-Wallis). Plasma glutamine remained below the normal range through the study in all groups (median: 0.18-0.30 mM), but had a tendency to rise in group 2 in contrast with a fall in groups 1 and 3 (NS). Group 1 required more insulin (p<0.01) than did the control group but median glycaemia was only 0.4-0.5 mM higher in group 1 (6.5 mM) than in groups 2 and 3 (6.1 resp. 6.0 mM). CONCLUSIONS: GH (0.05 g/kg day) administered in i.v. pulses is able to normalize IGF-I levels in subjects with prolonged critical illness after trauma. During this treatment, the standard dose of AG prevents worsening of plasma glutamine deficiency and glucose control is possible using routine algorithms, but it requires higher insulin doses.

• MeSH
• dipeptidy aplikace a dávkování   MeSH
• dospělí MeSH
• dvojitá slepá metoda MeSH
• financování organizované MeSH
• glutamin krev   MeSH
• insulinu podobný růstový faktor I analýza   MeSH
• krevní glukóza metabolismus   MeSH
• kritický stav MeSH
• lidé středního věku MeSH
• lidé MeSH
• polytrauma farmakoterapie   MeSH
• růstový hormon aplikace a dávkování   MeSH
• způsoby aplikace léků MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• randomizované kontrolované studie MeSH