Nocturnal hypoglycaemias in type 1 diabetic patients: what can we learn with continuous glucose monitoring?

AIM: In type 1 diabetic patients (T1DM), nocturnal hypoglycaemias (NH) are a serious complication of T1DM treatment; self-monitoring of blood glucose (SMBG) is recommended to detect them. However, the majority of NH remains undetected on an occasional SMBG done during the night. An alternative strategy is the Continuous glucose monitoring (CGMS), which retrospectively shows the glycaemic profile. The aims of this retrospective study were to evaluate the true incidence of NH in T1DM, the best SMBG time to predict NH, the relationship between morning hyperglycaemia and NH (Somogyi phenomenon) and the utility of CGMS to reduce NH. METHODS: Eighty-eight T1DM who underwent a CGMS exam were included. Indications for CGMS evaluation, hypoglycaemias and correlation with morning hyperglycaemias were recorded. The efficiency of CGMS to reduce the suspected NH was evaluated after 6-9 months. RESULTS: The prevalence of NH was 67% (32% of them unsuspected). A measured hypoglycaemia at bedtime (22-24 h) had a sensitivity of 37% to detect NH (OR=2.37, P=0.001), while a single measure < or =4 mmol/l at 3-hour had a sensitivity of 43% (OR=4.60, P<0.001). NH were not associated with morning hyperglycaemias but with morning hypoglycaemias (OR=3.95, P<0.001). After 6-9 months, suspicions of NH decreased from 60 to 14% (P<0.001). CONCLUSION: NH were highly prevalent and often undetected. SMBG at bedtime, which detected hypoglycaemia had sensitivity almost equal to that of 3-hour and should be preferred because it is easier to perform. Somogyi phenomenon was not observed. CGMS is useful to reduce the risk of NH in 75% of patients.

Nocturnal hypoglycaemias in type 1 diabetic patients : what can we learn with continuous glucose monitoring ?

Rapport de synthèse : Hypoglycémies nocturnes chez les patients diabétiques de type 1 : que pouvons-nous apprendre de la mesure de la glycémie en continu ? But : les hypoglycémies nocturnes sont une complication majeure du traitement des patients diabétiques de type 1; des autocontrôles de la glycémie capillaire sont donc recommandés pour les détecter. Cependant, la majorité des hypoglycémies nocturnes ne sont pas décelées par un autocontrôle glycémique durant la nuit. La mesure de la glycémie en continu (CGMS) est une alternative intéressante. Les buts de cette étude rétrospective étaient d'évaluer la véritable incidence des hypoglycémies nocturnes chez des patients diabétiques de type 1, la meilleure période pour effectuer un autocontrôle permettant de prédire une hypoglycémie nocturne, la relation entre les hyperglycémies matinales et les hypoglycémies nocturnes (phénomène de Somogyi) ainsi que l'utilité du CGMS pour réduire les hypoglycémies nocturnes. Méthode : quatre-vingt-huit patients diabétiques de type 1 qui avaient bénéficié d'un CGMS ont été inclus. Les indications au CGMS, les hypoglycémies nocturnes et diurnes ainsi que la corrélation entre les hypoglycémies nocturnes et les hyperglycémies matinales durant le CGMS ont été enregistrées. L'efficacité du CGMS pour réduire les hypoglycémies nocturnes a été évaluée six à neuf mois après. Résultats : la prévalence des hypoglycémies nocturnes était de 67% (32% non suspectées). La sensibilité d'une hypoglycémie à prédire une hypoglycémie nocturne était de 37% (OR = 2,37, P = 0,001) lorsqu'elle survient au coucher (22-24 h) et de 43% lorsqu'elle survient à 3 h (OR = 4,60, P < 0,001). Les hypoglycémies nocturnes n'étaient pas associées à des hyperglycémies matinales, mais à des hypoglycémies matinales (OR = 3.95, P < 0.001). Six à neuf mois après le CGMS, les suspicions cliniques d'hypoglycémies nocturnes ont diminué de 60% à 14% (P < 0.001). Abstract : Aim. - In type 1 diabetic patients (TIDM), nocturnal hypoglycaemias (Nlï) are a serious complication of T1DM treatment; self-monitoring of blood glucose (SMBG) is recommended to detect them. However, the majority of NH remains undetected on an occasional SMBG done during the night. An alternative strategy is the Continuous glucose monitoring (CGMS), which retrospectively shows the glycaemic profile. The aims of this retrospective study were to evaluate the true incidence of NH in TiDM, the bèst SMBG time to predict NH, the relationship between morning hyperglycaemia and N$ (Somogyi phenomenon) and the utility of CGMS to reduce NH. Methods. -Eighty-eight T1DM who underwent a CGMS exam were included. Indications for CGMS evaluarion, hypoglycaemias and correlation with morning hyperglycaemias were recorded. The efficiency of CGMS to reduce the suspected NH was evaluated after 6-9 months. Results. -The prevalence of NH was 67% (32% of them unsuspected). A measured hypoglycaemia at bedtime (22-24 h) had a sensitivity of 37% to detect NH (OR = 2.37, P = 0.001), while a single measure <_ 4 mmol/l at 3-hour had a sensitivity of 43% (OR = 4.60, P < 0.001). NH were not associated with morning hyperglycaemias but with morning hypoglycaemias (OR = 3.95, P < 0.001). After 6-9 months, suspicions of NH decreased from 60 to 14% (P < 0.001). Conclusion. - NH were highly prevalent and often undetected. SMBG at bedtime, which detected hypoglycaemia had sensitivity almost equal to that of 3-hour and should be preferred because it is easier to perform. Somogyi phenomenon was not observed. CGMS is useful to reduce the risk of NH in 75% of patients.

Glucose Control in Acute Myocardial Infarction: A Pilot Randomized Study Controlled by Continuous Glucose Monitoring System Comparing the Use of Insulin Glargine with Standard of Care

Background: This pilot study aimed to verify if glycemic control can be achieved in type 2 diabetes patients after acute myocardial infarction (AMI), using insulin glargine (iGlar) associated with regular insulin (iReg), compared with the standard intensive care unit protocol, which uses continuous insulin intravenous delivery followed by NPH insulin and iReg (St. Care). Patients and Methods: Patients (n = 20) within 24 h of AMI were randomized to iGlar or St. Care. Therapy was guided exclusively by capillary blood glucose (CBG), but glucometric parameters were also analyzed by blinded continuous glucose monitoring system (CGMS). Results: Mean glycemia was 141 +/- 39 mg/dL for St. Care and 132 +/- 42 mg/dL for iGlar by CBG or 138 +/- 35 mg/dL for St. Care and 129 +/- 34 mg/dL for iGlar by CGMS. Percentage of time in range (80-180 mg/dL) by CGMS was 73 +/- 18% for iGlar and 77 +/- 11% for St. Care. No severe hypoglycemia (<= 40 mg/dL) was detected by CBG, but CGMS indicated 11 (St. Care) and seven (iGlar) excursions in four subjects from each group, mostly in sulfonylurea users (six of eight patients). Conclusions: This pilot study suggests that equivalent glycemic control without increase in severe hyperglycemia may be achieved using iGlar with background iReg. Data outputs were controlled by both CBG and CGMS measurements in a real-life setting to ensure reliability. Based on CGMS measurements, there were significant numbers of glycemic excursions outside of the target range. However, this was not detected by CBG. In addition, the data indicate that previous use of sulfonylurea may be a potential major risk factor for severe hypoglycemia irrespective of the type of insulin treatment.

Influence of time point of calibration on accuracy of continuous glucose monitoring in individuals with type 1 diabetes

Abstract Background and Aims: Data on the influence of calibration on accuracy of continuous glucose monitoring (CGM) are scarce. The aim of the present study was to investigate whether the time point of calibration has an influence on sensor accuracy and whether this effect differs according to glycemic level. Subjects and Methods: Two CGM sensors were inserted simultaneously in the abdomen on either side of 20 individuals with type 1 diabetes. One sensor was calibrated predominantly using preprandial glucose (calibration(PRE)). The other sensor was calibrated predominantly using postprandial glucose (calibration(POST)). At minimum three additional glucose values per day were obtained for analysis of accuracy. Sensor readings were divided into four categories according to the glycemic range of the reference values (low, ≤4 mmol/L; euglycemic, 4.1-7 mmol/L; hyperglycemic I, 7.1-14 mmol/L; and hyperglycemic II, >14 mmol/L). Results: The overall mean±SEM absolute relative difference (MARD) between capillary reference values and sensor readings was 18.3±0.8% for calibration(PRE) and 21.9±1.2% for calibration(POST) (P<0.001). MARD according to glycemic range was 47.4±6.5% (low), 17.4±1.3% (euglycemic), 15.0±0.8% (hyperglycemic I), and 17.7±1.9% (hyperglycemic II) for calibration(PRE) and 67.5±9.5% (low), 24.2±1.8% (euglycemic), 15.5±0.9% (hyperglycemic I), and 15.3±1.9% (hyperglycemic II) for calibration(POST). In the low and euglycemic ranges MARD was significantly lower in calibration(PRE) compared with calibration(POST) (P=0.007 and P<0.001, respectively). Conclusions: Sensor calibration predominantly based on preprandial glucose resulted in a significantly higher overall sensor accuracy compared with a predominantly postprandial calibration. The difference was most pronounced in the hypo- and euglycemic reference range, whereas both calibration patterns were comparable in the hyperglycemic range.

Accuracy of continuous glucose monitoring during differing exercise conditions.

AIM Depending on intensity, exercise may induce a strong hormonal and metabolic response, including acid-base imbalances and changes in microcirculation, potentially interfering with the accuracy of continuous glucose monitoring (CGM). The present study aimed at comparing the accuracy of the Dexcom G4 Platinum (DG4P) CGM during continuous moderate and intermittent high-intensity exercise (IHE) in adults with type 1 diabetes (T1DM). METHODS Ten male individuals with well-controlled T1DM (HbA1c 7.0±0.6% [54±6mmol/mol]) inserted the DG4P sensor 2 days prior to a 90min cycling session (50% VO2peak) either with (IHE) or without (CONT) a 10s all-out sprint every 10min. Venous blood samples for reference glucose measurement were drawn every 10min and euglycemia (target 7mmol/l) was maintained using an oral glucose solution. Additionally, lactate and venous blood gas variables were determined. RESULTS Mean reference blood glucose was 7.6±0.2mmol/l during IHE and 6.7±0.2mmol/l during CONT (p<0.001). IHE resulted in significantly higher levels of lactate (7.3±0.5mmol/l vs. 2.6±0.3mmol/l, p<0.001), while pH values were significantly lower in the IHE group (7.27 vs. 7.38, p=0.001). Mean absolute relative difference (MARD) was 13.3±2.2% for IHE and 13.6±2.8% for CONT suggesting comparable accuracy (p=0.90). Using Clarke Error Grid Analysis, 100% of CGM values during both IHE and CONT were in zones A and B (IHE: 77% and 23%; CONT: 78% and 22%). CONCLUSIONS The present study revealed good and comparable accuracy of the DG4P CGM system during intermittent high intensity and continuous moderate intensity exercise, despite marked differences in metabolic conditions. This corroborates the clinical robustness of CGM under differing exercise conditions. CLINICAL TRIAL REGISTRATION NUMBER ClinicalTrials.gov NCT02068638.

Continuous glucose monitoring in diabetic long distance runners

Marathon running is growing in popularity, and many diabetic patients are participating in various marathon races all over the world each year. This study aimed to investigate the prevalence and extent of glycemic excursions (hypo- and hyperglycemic) during a marathon run in patients with well-controlled diabetes mellitus using a continuous glucose monitoring system (CGMS). Five subjects with type 1 and one patient with type 2 diabetes mellitus were monitored with the Medtronic MiniMed CGMS during the 2002 Vienna City Marathon (n = 3) or the Fernwarme run (n = 3) long distance runs of 42.19/15.8 km. All six patients finished their course. The CGSM system was well tolerated in all patients over an average duration of 34 +/- 4.0 hours and it did not limit the patients' activities. The mean running time for the Vienna city marathon was 257 +/- 8 min (247 to 274 min) and for the Fernwarme run 134 +/- 118 min (113 to 150 min). A total of 1470 blood glucose measurements (mean 245 readings per subject) were performed. During and after the marathons frequent hypo and hyperglycemic episodes with and without clinical symptoms were measured. Our data confirm that the CGMS may help to identify asymptomatic hypoglycemia or hyperglycemia during and after a long distance run. The system may also be helpful to improve our understanding about the individual changes of glucose during and after a marathon and may protect hypoglycemic or hyperglycemic periods in future races.

Strength and endurance training lead to different post exercise glucose profiles in diabetic participants using a continuous subcutaneous glucose monitoring system

Background Although both strength training (ST) and endurance training (ET) seem to be beneficial in type 2 diabetes mellitus (T2D), little is known about post-exercise glucose profiles. The objective of the study was to report changes in blood glucose (BG) values after a 4-month ET and ST programme now that a device for continuous glucose monitoring has become available. Materials and methods Fifteen participants, comprising four men age 56.5 +/- 0.9 years and 11 women age 57.4 +/- 0.9 years with T2D, were monitored with the MiniMed (Northridge, CA, USA) continuous glucose monitoring system (CGMS) for 48 h before and after 4 months of ET or ST. The ST consisted of three sets at the beginning, increasing to six sets per week at the end of the training period, including all major muscle groups and ET performed with an intensity of maximal oxygen uptake of 60% and a volume beginning at 15 min and advancing to a maximum of 30 min three times a week. Results A total of 17 549 single BG measurements pretraining (619.7 +/- 39.8) and post-training (550.3 +/- 30.1) were recorded, correlating to an average of 585 +/- 25.3 potential measurements per participant at the beginning and at the end of the study. The change in BG-value between the beginning (132 mg dL(-1)) and the end (118 mg dL(-1)) for all participants was significant (P = 0.028). The improvement in BG-value for the ST programme was significant (P = 0.02) but for the ET no significant change was measured (P = 0.48). Glycaemic control improved in the ST group and the mean BG was reduced by 15.6% (Cl 3-25%). Conclusion In conclusion, the CGMS may be a useful tool in monitoring improvements in glycaemic control after different exercise programmes. Additionally, the CGMS may help to identify asymptomatic hypoglycaemia or hyperglycaemia after training programmes.

Continuous amperometric monitoring of glucose in a brittle diabetic chimpanzee with a miniature subcutaneous electrode

The performance of an amperometric biosensor, consisting of a subcutaneously implanted miniature (0.29 mm diameter, 5 × 10−4 cm2 mass transporting area), 90 s 10–90% rise/decay time glucose electrode, and an on-the-skin electrocardiogram Ag/AgCl electrode was tested in an unconstrained, naturally diabetic, brittle, type I, insulin-dependent chimpanzee. The chimpanzee was trained to wear on her wrist a small electronic package and to present her heel for capillary blood samples. In five sets of measurements, averaging 5 h each, 82 capillary blood samples were assayed, their concentrations ranging from 35 to 400 mg/dl. The current readings were translated to blood glucose concentration by assaying, at t = 1 h, one blood sample for each implanted sensor. The rms error in the correlation between the sensor-measured glucose concentration and that in capillary blood was 17.2%, 4.9% above the intrinsic 12.3% rms error of the Accu-Chek II reference, through which the illness of the chimpanzee was routinely managed. Linear regression analysis of the data points taken at t>1 h yielded the relationship (Accu-Chek) = 0.98 × (implanted sensor) + 4.2 mg/dl, r2 = 0.94. The capillary blood and the subcutaneous glucose concentrations were statistically indistinguishable when the rate of change was less than 1 mg/(dl⋅min). However, when the rate of decline exceeded 1.8 mg/(dl⋅min) after insulin injection, the subcutaneous glucose concentration was transiently higher.

Impact of perinatal factors on continuous early monitoring of brain electrocortical activity in very preterm newborns by amplitude-integrated EEG.

Background:Amplitude-integrated electroencephalogram (aEEG) is increasingly used for neuromonitoring in preterms. We aimed to quantify the effects of gestational age (GA), postnatal age (PNA), and other perinatal factors on the development of aEEG early after birth in very preterm newborns with normal cerebral ultrasounds.Methods:Continuous aEEG was prospectively performed in 96 newborns (mean GA: 29.5 (range: 24.4-31.9) wk, birth weight 1,260 (580-2,120) g) during the first 96 h of life. aEEG tracings were qualitatively (maturity scores) and quantitatively (amplitudes) evaluated using preestablished criteria.Results:A significant increase in all aEEG measures was observed between day 1 and day 4 and for increasing GA (P < 0.001). The effect of PNA on aEEG development was 6.4- to 11.3-fold higher than that of GA. In multivariate regression, GA and PNA were associated with increased qualitative and quantitative aEEG measures, whereas small-for-GA status was independently associated with increased maximum aEEG amplitude (P = 0.003). Morphine administration negatively affected all aEEG measures (P < .05), and caffeine administration negatively affected qualitative aEEG measures (P = 0.02).Conclusion:During the first few days after birth, aEEG activity in very preterm infants significantly develops and is strongly subjected to the effect of PNA. Perinatal factors may alter the early aEEG tracing and interfere with its interpretation.

Prognostic value of continuous eeg monitoring during therapeutic hypothermia in patients with coma after cardiac arrest

INTRODUCTION: Continuous EEG (cEEG) is increasingly used to monitor brain function in neuro-ICU patients. However, its value in patients with coma after cardiac arrest (CA), particularly in the setting of therapeutic hypothermia (TH), is only beginning to be elucidated. The aim of this study was to examine whether cEEG performed during TH may predict outcome. METHODS: From April 2009 to April 2010, we prospectively studied 34 consecutive comatose patients treated with TH after CA who were monitored with cEEG, initiated during hypothermia and maintained after rewarming. EEG background reactivity to painful stimulation was tested. We analyzed the association between cEEG findings and neurologic outcome, assessed at 2 months with the Glasgow-Pittsburgh Cerebral Performance Categories (CPC). RESULTS: Continuous EEG recording was started 12 ± 6 hours after CA and lasted 30 ± 11 hours. Nonreactive cEEG background (12 of 15 (75%) among nonsurvivors versus none of 19 (0) survivors; P < 0.001) and prolonged discontinuous "burst-suppression" activity (11 of 15 (73%) versus none of 19; P < 0.001) were significantly associated with mortality. EEG seizures with absent background reactivity also differed significantly (seven of 15 (47%) versus none of 12 (0); P = 0.001). In patients with nonreactive background or seizures/epileptiform discharges on cEEG, no improvement was seen after TH. Nonreactive cEEG background during TH had a positive predictive value of 100% (95% confidence interval (CI), 74 to 100%) and a false-positive rate of 0 (95% CI, 0 to 18%) for mortality. All survivors had cEEG background reactivity, and the majority of them (14 (74%) of 19) had a favorable outcome (CPC 1 or 2). CONCLUSIONS: Continuous EEG monitoring showing a nonreactive or discontinuous background during TH is strongly associated with unfavorable outcome in patients with coma after CA. These data warrant larger studies to confirm the value of continuous EEG monitoring in predicting prognosis after CA and TH.

Prognostic value of continuous EEG monitoring during therapeutic hypothermia after cardiac arrest.

Introduction: Continuous EEG (cEEG) is increasingly used to monitor brain function in neuro-ICU patients. However, its value in patients with coma after cardiac arrest (CA), particularly in the setting of therapeutic hypothermia (TH), is only beginning to be elucidated. The aim of this study was to examine whether cEEG performed during TH may predict outcome. Methods: From April 2009 to April 2010, we prospectively studied 34 consecutive comatose patients treated with TH after CA who were monitored with cEEG, initiated during hypothermia and maintained after rewarming. EEG background reactivity to painful stimulation was tested. We analyzed the association between cEEG findings and neurologic outcome, assessed at 2 months with the Glasgow-Pittsburgh Cerebral Performance Categories (CPC). Results: Continuous EEG recording was started 12 ± 6 hours after CA and lasted 30 ± 11 hours. Nonreactive cEEG background (12 of 15 (75%) among nonsurvivors versus none of 19 (0) survivors; P < 0.001) and prolonged discontinuous "burst-suppression" activity (11 of 15 (73%) versus none of 19; P < 0.001) were significantly associated with mortality. EEG seizures with absent background reactivity also differed significantly (seven of 15 (47%) versus none of 12 (0); P = 0.001). In patients with nonreactive background or seizures/epileptiform discharges on cEEG, no improvement was seen after TH. Nonreactive cEEG background during TH had a positive predictive value of 100% (95% confidence interval (CI), 74 to 100%) and a false-positive rate of 0 (95% CI, 0 to 18%) for mortality. All survivors had cEEG background reactivity, and the majority of them (14 (74%) of 19) had a favorable outcome (CPC 1 or 2). Conclusions: Continuous EEG monitoring showing a nonreactive or discontinuous background during TH is strongly associated with unfavorable outcome in patients with coma after CA. These data warrant larger studies to confirm the value of continuous EEG monitoring in predicting prognosis after CA and TH.

Continuous electroencephalographic monitoring in critically ill patients with central nervous system infections.

OBJECTIVES: To determine the prevalence, predictors, and clinical significance of electrographic seizures (ESz) and other continuous electroencephalographic monitoring findings in critically ill patients with central nervous system infections. DESIGN: Retrospective cohort study. SETTING: Eighteen-bed neurocritical care unit. PATIENTS: We identified 42 consecutive patients with primary central nervous system infection (viral, 27 patients [64%]; bacterial, 8 patients [18%]; and fungal or parasitic, 7 patients [17%]) who underwent continuous electroencephalographic monitoring between January 1, 1996, and February 28, 2007. MAIN OUTCOME MEASURES: Presence of ESz or periodic epileptiform discharges (PEDs). RESULTS: Electrographic seizures were recorded in 14 patients (33%), and PEDs were recorded in 17 patients (40%). Twenty patients (48%) had either PEDs or ESz. Of the 14 patients with ESz, only 5 (36%) had a clinical correlate. Periodic epileptiform discharges (odds ratio=13.4; P=.001) and viral cause (odds ratio=13.0; P=.02) were independently associated with ESz. Both ESz (odds ratio=5.9; P=.02) and PEDs (odds ratio=6.1; P=.01) were independently associated with poor outcome at discharge (severe disability, vegetative state, or death). CONCLUSIONS: In patients with central nervous system infections undergoing continuous electroencephalographic monitoring, ESz and/or PEDs were frequent, occurring in 48% of our cohort. More than half of the ESz had no clinical correlate. Both ESz and PEDs were independently associated with poor outcome. Additional studies are needed to determine whether prevention or treatment of these electrographic findings improves outcome.

Évaluation d’un prototype de détecteur de glucose dans le tissu interstitiel sans aiguille, le PGS (Photonic Glucose Sensor)

Objectif : Déterminer la fiabilité et la précision d’un prototype d’appareil non invasif de mesure de glucose dans le tissu interstitiel, le PGS (Photonic Glucose Sensor), en utilisant des clamps glycémiques multi-étagés. Méthodes : Le PGS a été évalué chez 13 sujets avec diabète de type 1. Deux PGS étaient testés par sujet, un sur chacun des triceps, pour évaluer la sensibilité, la spécificité, la reproductibilité et la précision comparativement à la technique de référence (le Beckman®). Chaque sujet était soumis à un clamp de glucose multi-étagé de 8 heures aux concentrations de 3, 5, 8 et 12 mmol/L, de 2 heures chacun. Résultats : La corrélation entre le PGS et le Beckman® était de 0,70. Pour la détection des hypoglycémies, la sensibilité était de 63,4%, la spécificité de 91,6%, la valeur prédictive positive (VPP) 71,8% et la valeur prédictive négative (VPN) 88,2%. Pour la détection de l’hyperglycémie, la sensibilité était de 64,7% et la spécificité de 92%, la VPP 70,8% et la VPN : 89,7%. La courbe ROC (Receiver Operating Characteristics) démontrait une précision de 0,86 pour l’hypoglycémie et de 0,87 pour l’hyperglycémie. La reproductibilité selon la « Clark Error Grid » était de 88% (A+B). Conclusion : La performance du PGS était comparable, sinon meilleure que les autres appareils sur le marché(Freestyle® Navigator, Medtronic Guardian® RT, Dexcom® STS-7) avec l’avantage qu’il n’y a pas d’aiguille. Il s’agit donc d’un appareil avec beaucoup de potentiel comme outil pour faciliter le monitoring au cours du traitement intensif du diabète. Mot clés : Diabète, diabète de type 1, PGS (Photonic Glucose Sensor), mesure continue de glucose, courbe ROC, « Clark Error Grid».