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
- Algorithms MeSH
- Diabetes Mellitus, Type 1 blood surgery MeSH
- Infusions, Intravenous MeSH
- Insulin administration & dosage MeSH
- Intensive Care Units MeSH
- Blood Glucose analysis MeSH
- Middle Aged MeSH
- Humans MeSH
- Postoperative Period MeSH
- Aged MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't 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
- Algorithms MeSH
- Adult MeSH
- Financing, Organized MeSH
- Body Mass Index MeSH
- Data Interpretation, Statistical MeSH
- Insulin administration & dosage pharmacology therapeutic use MeSH
- Insulin Resistance physiology MeSH
- Cardiac Surgical Procedures methods nursing MeSH
- Clinical Protocols MeSH
- Blood Glucose analysis metabolism MeSH
- Humans MeSH
- Mortality MeSH
- Perioperative Care methods MeSH
- Pilot Projects MeSH
- Computers statistics & numerical data trends utilization MeSH
- Postoperative Complications prevention & control therapy MeSH
- Postoperative Care methods MeSH
- Primary Prevention MeSH
- Randomized Controlled Trials as Topic statistics & numerical data MeSH
- Aged MeSH
- Treatment Outcome MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female 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
- Algorithms * MeSH
- Models, Biological MeSH
- Diabetes Mellitus, Type 1 blood drug therapy MeSH
- Insulin administration & dosage MeSH
- Kinetics MeSH
- Blood Glucose metabolism MeSH
- Humans MeSH
- Therapy, Computer-Assisted statistics & numerical data MeSH
- Computer Simulation MeSH
- Reinforcement, Psychology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Evaluation Study 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
- Algorithms MeSH
- Hypoglycemic Agents therapeutic use MeSH
- Insulin therapeutic use MeSH
- Intensive Care Units MeSH
- Blood Glucose * MeSH
- Critical Illness MeSH
- Humans MeSH
- Critical Care methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review 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
- Monitoring, Ambulatory MeSH
- Diabetes Mellitus, Type 1 blood drug therapy MeSH
- Adult MeSH
- Hypoglycemic Agents administration & dosage MeSH
- Insulin administration & dosage MeSH
- Insulin Infusion Systems MeSH
- Blood Glucose analysis MeSH
- Middle Aged MeSH
- Humans MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't 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
- Insulin administration & dosage MeSH
- Intensive Care Units * MeSH
- Blood Glucose drug effects metabolism MeSH
- Critical Illness therapy MeSH
- Middle Aged MeSH
- Humans MeSH
- Critical Care methods MeSH
- Aged MeSH
- Decision Support Systems, Clinical * instrumentation MeSH
- Check Tag
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Clinical Trial MeSH
- Multicenter Study MeSH
- Observational Study MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Europe epidemiology 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
- Algorithms MeSH
- Diabetes Mellitus, Type 1 * drug therapy MeSH
- Hyperglycemia prevention & control MeSH
- Hypoglycemia diagnosis prevention & control MeSH
- Insulin Infusion Systems MeSH
- Blood Glucose MeSH
- Humans MeSH
- Blood Glucose Self-Monitoring * methods standards instrumentation MeSH
- Check Tag
- Humans 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
- Algorithms MeSH
- Diabetes Mellitus, Type 1 * MeSH
- Insulin MeSH
- Blood Glucose MeSH
- Humans MeSH
- Blood Glucose Self-Monitoring * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
In contrast to patients with diabetes mellitus, data on consequences of hypoglycemia in critically ill patients are sparse. The purpose of this review is to summarize available data on prevalence of hypoglycemia, risk factors, and possible consequences of hypoglycemia in critically ill patients. RECENT FINDINGS: There is strong evidence that strict glycemic control is beneficial for critically ill patients. Recent attempts to confirm these findings have not succeeded. Instead, they have increased the fear for negative consequences of hypoglycemia. Hypoglycemia is four to seven times more frequent in patients treated with strict glycemic control. Risk factors for hypoglycemia are a change in nutrition without adjustment of insulin treatment, diabetes mellitus, sepsis, shock, liver failure, and the need for renal replacement therapy. Consequences of hypoglycemia in critically ill patients are not well defined, but overall current evidence suggests that beneficial effects of strict glycemic control outweigh possible negative effects of hypoglycemia. SUMMARY: Hypoglycemia should be avoided in critically ill patients, but not at the cost of less stringent glycemic control. Strict glycemic control with a low incidence of hypoglycemia can be achieved with a validated (computerized) algorithm and increased surveillance in patients with an increased risk for hypoglycemia.
Léčba inzulinem je již mnoho let nedílnou součástí léčby diabetiků 2. typu, a to zejména těch s delším trváním onemocnění. Podle platných mezinárodních i českých doporučení je možné inzulin podávat již jako lék druhé volby při nedostatečné kompenzaci pacienta na monoterapii metforminem. V praxi je u mnoha pacientů s diabetem 2. typu podávání inzulinu často zahájeno později, než by bylo ideální. Důvodem jsou mimo jiné obavy pacientů a často i lékařů nediabetologů z nežádoucích účinků inzulinu. Přestože se v současné době výrazně rozšířily možnosti léčby diabetu 2. typu o nové lékové skupiny účinně snižující glykemii bez výraznějšího rizika hypoglykemie, zůstává dlouhodobě působící inzulin stále nejúčinnějším způsobem snížení hyperglykemie nalačno, což vede k celkovému poklesu glykemií i později během dne. V tomto článku podáváme přehled dlouhodobě působících inzulinů dostupných v současné době na našem trhu případně s blízkou perspektivou uvedení na trh. Diskutujeme rozdíly mezi jednotlivými preparáty a jejich klinické důsledky ve vztahu k výběru konkrétního dlouhodobě působícího inzulinu pro konkrétního pacienta.
Insulin therapy has been for many years an inseparable part of the treatment of patients with type 2 diabetes, in particular those with longer diabetes duration. Current national and international guidelines list insulin treatment as a possible second choice therapy in patient with unsatisfactory glucose control on monotherapy with metformin. In reality, insulin therapy is often initiated later than it optimally should be. The reasons include among others the fear of patients and sometimes also of physicians from the side effects of insulin. Even though the options of antidiabetic treatment has been diversified by the addition of novel groups of antidiabetics with good efficacy and low risk of hypoglycemia, long acting insulin therapy still remains the most effective way of decreasing fasting hyperglycemia with the effect lasting further throughout the day. In this paper we summarize the current knowledge concerning long-acting insulins available on the Czech market or the ones that should be available in the near future. We discuss the differences among available long-acting insulins and their clinical consequences with respect to the selection of particular insulin for particular patient.
- MeSH
- Biosimilar Pharmaceuticals administration & dosage therapeutic use MeSH
- Diabetes Mellitus, Type 2 * drug therapy MeSH
- Insulin, Long-Acting * administration & dosage classification therapeutic use MeSH
- Hypoglycemia prevention & control MeSH
- Insulin Detemir therapeutic use MeSH
- Insulin Glargine therapeutic use MeSH
- Humans MeSH
- Blood Glucose Self-Monitoring MeSH
- Body Weight MeSH
- Check Tag
- Humans MeSH
