Use of inert gas jets to measure the forces required for mechanical gene transfection

Background

Abnormal blood glucose (BG) concentrations have been associated with increased morbidity and mortality in both critically ill adults and infants. Furthermore, hypoglycaemia and glycaemic variability have both been independently linked to mortality in these patients. Continuous Glucose Monitoring (CGM) devices have the potential to improve detection and diagnosis of these glycaemic abnormalities. However, sensor noise is a trade-off of the high measurement rate and must be managed effectively if CGMs are going to be used to monitor, diagnose and potentially help treat glycaemic abnormalities.

Aim

To develop a tool that will aid clinicians in identifying unusual CGM behaviour and highlight CGM data that potentially need to be interpreted with care.

Methods

CGM data and BG measurements from 50 infants at risk of hypoglycaemia were used. Unusual CGM measurements were classified using a stochastic model based on the kernel density method and historical CGM measurements from the cohort. CGM traces were colour coded with very unusual measurements coloured red, highlighting areas to be interpreted with care. A 5-fold validation of the model was Monte Carlo simulated 25 times to ensure an adequate model fit.

Results

The stochastic model was generated using ~67,000 CGM measurements, spread across the glycaemic range ~2-10?mmol/L. A 5-fold validation showed a good model fit: the model 80% confidence interval (CI) captured 83% of clinical CGM data, the model 90% CI captured 91% of clinical CGM data, and the model 99% CI captured 99% of clinical CGM data. Three patient examples show the stochastic classification method in use with 1) A stable, low variability patient which shows no unusual CGM measurements, 2) A patient with a very sudden, short hypoglycaemic event (classified as unusual), and, 3) A patient with very high, potentially un-physiological, glycaemic variability after day 3 of monitoring (classified as very unusual).

Conclusions

This study has produced a stochastic model and classification method capable of highlighting unusual CGM behaviour. This method has the potential to classify important glycaemic events (e.g. hypoglycaemia) as true clinical events or sensor noise, and to help identify possible sensor degradation. Colour coded CGM traces convey the information quickly and efficiently, while remaining computationally light enough to be used retrospectively or in real-time.     

Development and optimisation of stochastic targeted (STAR) glycaemic control for pre-term infants in neonatal intensive care

Hyperglycaemia is a common complication of prematurity and stress in neonatal intensive care units (NICUs). It has been linked to worsened outcomes and mortality. There is currently no universally accepted best practice glycaemic control method, with many protocols lacking patient specificity or relying heavily on ad hoc clinical judgment from clinical staff who may be caring or overseeing care for several patients at once. The result is persistent hypoglycaemia and poor control. This research presents the virtual trial design and optimisation of a stochastic targeted (STAR) approach to improve performance and reduce hypoglycaemia. Clinically validated virtual trials based on NICU patient data (N = 61 patients, 7006 h) are used to develop and optimise a STAR protocol that improves on current STAR-NICU performance and reduce hypoglycaemia. Five approaches are used to maximise the stochastic range of BG outcomes within 4.0–8.0 mmol/L, and are designed based on an overall cohort risk to provide clinically specified risk (5%) of BG above or below a clinically specified level. The best protocol placed the 5th percentile BG outcome for an intervention on 4.0 mmol/L band. The optimised protocol increased %BG in the 4.0–8.0 mmol/L band by 3.5% and the incidence of BG < 2.6 mmol/L by 1 patient (50%). Significant intra- and inter-patient variability limited possible performance gains so that they are unlikely to be clinically substantial, indicating a need for a further increase patient-specific or sub-cohort specific approaches to manage variability. This result sets the direction for future research.     

External validation and sub-cohort analysis of stochastic forecasting models in NICU cohorts

Hyperglycaemia is a prevalent complication in the neonatal intensive care unit (NICU) and is associated with worsened outcomes. It occurs as a result of prematurity, under-developed endogenous glucose regulatory systems, and clinical stress. The stochastic targeting (STAR) framework provides patient-specific, model-based glycaemic control with a clinically proven level of confidence on the outcome of treatment interventions, thus directly managing the risk of hypo- and hyper-glycaemia. However, stochastic models that are over conservative can limit control performance. Retrospective clinical data from 61 episodes (25 retrospective to STAR, and 36 from a prospective-STAR blood glucose control study) of insulin therapy in very-low birth weight (VLBW) and extremely-low birth weight (ELBW) neonates are used to create a new stochastic model of model-based insulin sensitivity (S_I [L/mU/min]). Sub-cohort models based on gestational age (GA) and birth weight (BW) are also created. Performance is assessed by the percentage of patients who have 90% of actual intra-patient variability in S_I captured by the 90% confidence bands of the cohort based (inter-patient) stochastic variability model created. This assessment measures per-patient accuracy for any given cohort model.Per-patient coverage trends were very similar between prospective and retrospective cohorts, providing a measure of external validation of cohort similarity. Per-patient coverage was improved through the use of BW and GA dependent stochastic models, which ensures that the stochastic models more accurately capture both inter- and intra-patient variability. Stochastic models based on insulin sensitivities during insulin treatment periods are tighter, and give better and safer glycaemic control. Overall it seems that inter-patient variation is more significant than intra-patient variation as a limiting factor in this stochastic forecasting model, and a small number of patients are essentially different in behaviour. More patient specific methods, particularly in the modelling of endogenous insulin and glucose production, will be required to further improve forecasting and glycaemic control.     

Accuracy of Continuous Glucose Monitoring Measurements in Normo-Glycemic Individuals

BackgroundThe validity of continuous glucose monitoring (CGM) is well established in diabetic patients. CGM is also increasingly used for research purposes in normo-glycemic individuals, but the CGM validity in such individuals is unknown. We studied the accuracy of CGM measurements in normo-glycemic individuals by comparing CGM-derived versus venous blood-derived glucose levels and measures of glycemia and glycemic variability.MethodsIn 34 healthy participants (mean age 65.7 years), glucose was simultaneously measured every 10 minutes, via both an Enlite^® CGM sensor, and in venous blood sampled over a 24-hour period. Validity of CGM-derived individual glucose measurements, calculated measures of glycemia over daytime (09:00h-23:00h) and nighttime (23:00h-09:00h), and calculated measures of glycemic variability (e.g. 24h standard deviation [SD]) were assessed by Pearson correlation coefficients, mean absolute relative difference (MARD) and paired t-tests.ResultsThe median correlation coefficient between CGM and venous glucose measurements per participant was 0.68 (interquartile range: 0.40–0.78), and the MARD was 17.6% (SD = 17%). Compared with venous sampling, the calculated measure of glycemia during daytime was 0.22 mmol/L higher when derived from CGM, but no difference was observed during nighttime. Most measures of glycemic variability were lower with CGM than with venous blood sampling (e.g., 24h SD: 1.07 with CGM and 1.26 with venous blood; p-value = 0.004).ConclusionIn normo-glycemic individuals, CGM-derived glucose measurements had good agreement with venous glucose levels. However, the measure of glycemia was higher during the day and most measures of glycemic variability were lower when derived from CGM.     

New Indices for Predicting Glycaemic Variability

Blood glucose variability is known to be associated with increased risk of long-term complications. Reliable indices for predicting hyperglycaemic and hypoglycaemic fluctuations are therefore needed. Glycaemic standard deviation (SD) obtained by continuous glucose monitoring correlates closely with nine previously described glycaemic variability formulas. Here, new indices predictive of glycaemic variability were developed, which can be calculated from laboratory measures based on a single blood draw. The indices included the glycated albumin (GA) to HbA1c ratio (GA/A1c ratio) and the fasting C-peptide immunoreactivity (FCPR) to fasting plasma glucose (FPG) ratio (FCPR index). Predictive values of these indices were assessed in 100 adults with diabetes. GA/A1c ratio and FCPR index showed close associations with glycaemic SD in addition to the nine existing glucose variability formulas. Subjects with a GA/A1c ratio ≥2.8 and FCPR index <3.0 showed the greatest SD and longest durations of hypoglycaemia, while those with a GA/A1c ratio <2.8 and FCPR index ≥3.0 had smaller SDs and little sign of hypoglycaemia. In adults with diabetes, a high GA/A1c ratio and low FCPR index value reflect higher glycaemic excursions, irrespective of diabetes type. Simultaneous measurements of GA, HbA1c, FPG and FCPR may help to identify a group of patients who warrant closer monitoring in relation to glycaemic variability and hypoglycaemia.     

Frequency of Continuous Glucose Monitoring use and Change in Hemoglobin A1C for Adults With Type 1 Diabetes in a Clinical Practice Setting

ObjectiveTo estimate the frequency of continuous glucose monitoring (CGM) use and change in hemoglobin A1c (HbA1c) compared to self-monitoring of blood glucose (SMBG) alone in adults with type 1 diabetes in a clinical practice setting.MethodsWe retrospectively identified 66 adult type 1 diabetes patients at the Barbara Davis Center for Diabetes (BDC) who first initiated CGM between 2006 and 2011 and 67 controls using SMBG. The frequency of CGM use was estimated from survey recall and defined as the mean number of days/month of CGM use during a maximum follow-up of 10 months. Change in HbA1c was calculated as the difference between the baseline value and the lowest follow-up value.ResultsThe mean change in HbA1c for CGM users was − 0.48% (95% confidence interval [CI]: − 0.67, − 0.28) and for SMBG users was − 0.37% (95% CI: − 0.56, − 0.18). The between-group mean difference in change in HbA1c, adjusted for patient characteristics, was − 0.11% (95% CI: − 0.38, 0.16), whereas the subgroup with a baseline HbA1c ≥ 7.0% and users of CGM ≥ 21 days/month was − 0.36% (95% CI, − 0.78, 0.05). Nearly half (n = 32, 48%) used CGM < 21 days/month. The reasons for low frequency of CGM use or discontinuation included sensor costs, frequency of alarms, inaccuracy, and discomfort.ConclusionsThese CGM data from clinical practice suggest a trend toward decreasing HbA1c for adults with type 1 diabetes, especially in patients with higher baseline HbA1c and higher frequency of CGM use. Future studies are needed to assess the use of CGM in larger populations of clinical practice adult type 1 diabetes patients. (Endocr Pract. 2014;20:1007-1015)     

Superiority of insulin analogues versus human insulin in the treatment of diabetes mellitus

The modern goals of insulin replacement in Type 1 and Type 2 diabetes mellitus (T1, T2DM) are A1C <6.5% long-term, and prevention of hypoglycaemia (blood glucose, BG <70 mg/dl). In addition to appropriate education and motivation of diabetic subjects, the use of rapid- and long-acting insulin analogues, is critical to achieve these goals. The benefits of rapid-acting analogues (lispro, aspart and glulisine have similar pharmacodynamic effects) compared with non-modified human regular insulin, are: (a) lower 1- and 2-h post-prandial blood glucose; (b) lower risk of late post-prandial hypoglycaemia (and therefore lower BG variability); (c) better quality of life (greater flexibility in timing and dosing of insulin). In T1DM, rapid-acting analogues improve A1C only by the extent to which replacement of basal insulin is optimized at the same time, either by multiple daily NPH administrations, or continuous subcutaneous insulin infusion (CSII), or use of the long-acting insulin analogues glargine or detemir. In T2DM, rapid-acting analogues reduce post-prandial hyperglycaemia more than human regular insulin, but systematic studies are needed to examine the effects on A1C. The benefits of long-acting insulin analogues glargine and detemir vs. NPH, are: (1) lower fasting BG combined with lower risk of hypoglycaemia in the interprandial state (night); (2) lower variability of BG. Glargine and detemir differ in terms of potency and duration of action. Detemir should be given twice daily in the large majority of people with T1DM, and in a large percentage of subjects with T2DM as well, usually at doses greater vs those of the once daily glargine. However, when used appropriately for individual pharmacokinetics and pharmacodynamics, glargine and detemir result into similar effects on BG, risk of hypoglycaemia and A1C. Rapid- and long-acting insulin analogues should always be combined in the treatment of T1 and T2DM.     

Second pilot trials of the STAR-Liege protocol for tight glycemic control in critically ill patients

ABSTRACT: BACKGROUND: Critically ill patients often present increased insulin resistance and stress-induced hyperglycemia. Tight glycemic control aims to reduce blood glucose (BG) levels and variability while ensuring safety from hypoglycemia. This paper presents the results of the second Belgian clinical trial using the customizable STAR framework in a target-to-range control approach. The main objective is reducing measurement frequency while maintaining performance and safety of the glycemic control. METHODS: The STAR-Liege 2 (SL2) protocol targeted the 100--140 mg/dL glycemic band and offered 2-hourly and 3-hourly interventions. Only insulin rates were adjusted, and nutrition inputs were left to the attending clinicians. This protocol restricted the forecasted risk of BG < 90 mg/dL to a 5% level using a stochastic model of insulin sensitivity to assess patient-specific responses to insulin and its future likely variability to optimize insulin interventions. The clinical trial was performed at the Centre Hospitalier Universitaire de Liege and included 9 patients. Results are compared to 24-hour pre-trial and 24-hour post-trial, but also to the results of the first pilot trial performed in Liege, STAR-Liege 1 (SL1). This trial was approved by the Ethics Committee of the Medical Faculty of the University of Liege (Liege, Belgium). RESULTS: During the SL2 trial, 91 measurements were taken over 194 hours. BG levels were tightly distributed: 54.9% of BG within 100--140 mg/dL, 40.7% were [GREATER-THAN OR EQUAL TO] 140 mg/dL and 4.4% were < 100 mg/dL with no BG < 70 mg/dL. Comparing these results with 24-hour pre-trial and post-trial shows that SL2 reduced high and low BG levels and reduced glycemic variability. Nurses selected 3-hourly measurement only 5 of 16 times and overrode 12% of 91 recommended interventions (35% increased insulin rates and 65% decreased insulin rates). SL1 and SL2 present similar BG levels distribution (p > 0.05) with significantly reduced measurement frequency for SL2 (p < 0.05). CONCLUSIONS: The SL2 protocol succeeded in reducing clinical workload while maintaining safety and effectiveness of the glycemic control. SL2 was also shown to be safer and tighter than hospital control. Overall results validate the efficacy of significantly customizing the STAR framework.     

Effect of chronic hypoglycaemia on glucose concentration and glycogen content in rat brain: A localized 13C NMR study

While chronic hypoglycaemia has been reported to increase unidirectional glucose transport across the blood-brain barrier (BBB) and to increase GLUT1 expression at the endothelium, the effect on steady-state brain d-glucose and brain glycogen content is currently unknown. Brain glucose and glycogen concentrations were directly measured in vivo using localized 13C magnetic resonance spectroscopy (MRS) following 12-14 days of hypoglycaemia. Brain glucose content was significantly increased by 48%, which is consistent with an increase in the maximal glucose transport rate, Tmax, by 58% compared with the sham-treated animals. The localized 13C NMR measurements of brain glucose were directly validated by comparison with biochemically determined brain glucose content after rapid focused microwave fixation (1.4 s at 4 kW). Both in vivo MRS and biochemical measurements implied that brain glycogen content was not affected by chronic hypoglycaemia, consistent with brain glucose being a major factor controlling brain glycogen content. We conclude that the increased glucose transporter expression in chronic hypoglycaemia leads to increased brain glucose content at a given level of glycaemia. Such increased brain glucose concentrations can result in a lowered glycaemic threshold of counter-regulation observed in chronic hypoglycaemia.     

Continuous Glucose Monitoring for Patients with COVID-19 Pneumonia: Initial Experience at a Tertiary Care Center

ObjectivePatients hospitalized with COVID-19 and hyperglycemia require frequent glucose monitoring, usually performed with glucometers. Continuous glucose monitors (CGMs) are common in the outpatient setting but not yet approved for hospital use. We evaluated CGM accuracy, safety for insulin dosing, and CGM clinical reliability in 20 adult patients hospitalized with COVID-19 and hyperglycemia.MethodsStudy patients were fitted with a remotely monitored CGM. CGM values were evaluated against glucometer readings. The CGM sensor calibration was performed if necessary. CGM values were used to dose insulin, without glucometer confirmation.ResultsCGM accuracy against glucometer, expressed as mean absolute relative difference (MARD), was calculated using 812 paired glucometer-CGM values. The aggregate MARD was 10.4%. For time in range and grades 1 and 2 hyperglycemia, MARD was 11.4%, 9.4%, and 9.1%, respectively, with a small variation between medical floors and intensive care units. There was no MARD correlation with mean arterial blood pressure levels, oxygen saturation, daily hemoglobin levels, and glomerular filtration rates. CGM clinical reliability was high, with 99.7% of the CGM values falling within the “safe” zones of Clarke error grid. After CGM placement, the frequency of glucometer measurements decreased from 5 to 3 and then 2 per day, reducing nurse presence in patient rooms and limiting viral exposure.ConclusionWith twice daily, on-demand calibration, the inpatient CGM use was safe for insulin dosing, decreasing the frequency of glucometer fingersticks. For glucose levels >70 mg/dL, CGMs showed adequate accuracy, without interference from vital and laboratory values.     

Continuous Glucose Monitoring in Patients Undergoing Extracorporeal Ventricular Assist Therapy

Background

Dysregulations of blood glucose (BG) are associated with adverse outcome in critical illness; controlling BG to target appears to improve outcome. Since BG-control is challenging in daily intensive care practice BG-control remains poor especially in patients with rapidly fluctuating BG. To improve BG-control and to avoid deleterious hypoglycemia, automated online-measurement tools are advocated. We thus evaluated the point-accuracy of the subcutaneous Sentrino^® Continuous Glucose Monitoring System (CGM, Medtronic Diabetes, Northridge, California) in patients undergoing extracorporeal cardiac life support (ECLS) for cardiogenic shock.

Methods

Management of BG was performed according to institute’s standard aiming at BG-levels between 100–145 mg/dl. CGM-values were recorded without taking measures into therapeutic account. Point-accuracy in comparison to intermittent BG-measurement by the ABL-blood-gas analyzer was determined.

Results

CGM (n = 25 patients) correlated significantly with ABL-values (r = 0.733, p<0.001). Mean error from standard was 15.0 mg/dl (11.9%). 44.2% of the readings were outside a 15% range around ABL-values. In one of 635 paired data-points, ABL revealed hypoglycemia (BG 32 mg/dl) whereas CGM did not show hypoglycemic values (132mg/dl).

Conclusions

CGM reveals minimally invasive BG-values in critically ill adults with dynamically impaired tissue perfusion. Because of potential deviations from standard, CGM-readings must be interpreted with caution in specific ICU-populations.     

A cartilage growth mixture model with collagen remodeling: validation protocols

A cartilage growth mixture (CGM) model is proposed to address limitations of a model used in a previous study. New stress constitutive equations for the solid matrix are derived and collagen (COL) remodeling is incorporated into the CGM model by allowing the intrinsic COL material constants to evolve during growth. An analytical validation protocol based on experimental data from a recent in vitro growth study is developed. Available data included measurements of tissue volume, biochemical composition, and tensile modulus for bovine calf articular cartilage (AC) explants harvested at three depths and incubated for 13 days in 20% fetal borine serum (FBS) and 20% FBS+beta-aminopropionitrile. The proposed CGM model can match tissue biochemical content and volume exactly while predicting theoretical values of tensile moduli that do not significantly differ from experimental values. Also, theoretical values of a scalar COL remodeling factor are positively correlated with COL cross-link content, and mass growth functions are positively correlated with cell density. The results suggest that the CGM model may help us to guide in vitro growth protocols for AC tissue via the a priori prediction of geometric and biomechanical properties.     

Continuous monitoring of blood glucose

Continuous blood glucose monitoring aims to: better evaluate glycaemic variations; better detect hypoglycaemia; and, ultimately, automatize insulin delivery (artificial beta cell). The sensors can be fully implantable, with the challenge of constructing durable systems to avoid repeated implantations. In-dwelling needle-like electrodes and microdialysis fibres with a pump that brings the dialysate to the glucose sensor are inserted in the subcutaneous tissue through the skin. The GlucoWatch is an almost non-invasive technique that extracts the extracellular fluid by iontophoresis. In these systems, the glucose oxidase generates the electrical signal, proportional to the glucose concentration. Non-invasive techniques aim at measuring the glucose concentration without breaching the skin, using absorption of light in the infrared spectrum. These techniques have not reached the necessary reliability for use as glycaemic alarms, and even less as artificial beta cells. Currently, glucose sensors are mainly used as glycaemic holters to help in the management of insulin therapy.     

Clinical and Laboratory Aspects of Insulin Autoantibody-Mediated Glycaemic Dysregulation and Hyperinsulinaemic Hypoglycaemia: Insulin Autoimmune Syndrome and Exogenous Insulin Antibody Syndrome

Autoimmune glycaemic dysregulation and hyperinsulinaemic hypoglycaemia mediated by insulin autoantibodies is an increasingly recognised but controversial phenomenon described in both exogenous insulin naïve (insulin autoimmune syndrome) and exposed (exogenous insulin antibody syndrome) individuals. There has been a significant proliferation of case reports, clinical studies and reviews in the medical literature in recent years which have collectively highlighted the discrepancy between experts in the field with regard to the nomenclature, definition, proposed pathophysiology, as well as the clinical and biochemical diagnostic criteria associated with the condition. The essential characteristics of the condition are glycaemic dysregulation manifesting as episodes of hyperglycaemia and unpredictable hyperinsulinaemic hypoglycaemia associated with high titres of endogenous antibodies to insulin. Although the hypoglycaemia is often life-threatening and initiation of targeted therapies critical, the diagnosis is often delayed and attributable to various factors including: the fact that existence of the condition is not universally accepted; the need to exclude surreptitious causes of hypoglycaemia; the diverse and often complex nature of the glycaemic dysregulation; and the challenge of diagnostic confirmation. Once confirmed, the available therapeutic options are expansive and the reported responses to these therapies have been variable. This review will focus on our evolving understanding, and the associated diagnostic challenges – both clinical and laboratory – of this complex condition.     

Pragmatic Clinic-Based Investigation of Glycemic Variability in Patients With Type 1 Diabetes in Routine Clinical Practice and Its Association With Cardiovascular Autonomic Neuropathy: A Pilot Study

ObjectiveTo evaluate the relationship between markers of glycemic variability (GV), assessed by blinded continuous glucose monitoring (CGM), and cardiovascular autonomic neuropathy (CAN) in patients with type 1 diabetes (T1D).MethodsGV indices, such as SD and coefficient of variation were obtained by blinded CGM through an electrode inserted into the subcutaneous tissue for at least 3 consecutive days. CAN was assessed by cardiovascular reflex tests and HRV.ResultsFifteen T1D patients were included: 7 (46.7%) women, aged 47.1 ± 11.6 years, with a diabetes duration of 26 years (20 to 29.5 years). Five patients (25%) were excluded from our study. The majority of our patients presented glycated hemoglobin (60%), SD (86.3%), and coefficient of variation (60%) above the established goals. Patients with defined CAN had a longer diabetes duration, higher glycated hemoglobin levels, lower glomerular filtration rate, lower prevalence of indices related to hypoglycemic stress, and short-term GV indices compared with patients without CAN.ConclusionOur study showed an inverse association between GV and CAN. The most important risk factors associated with CAN were age, diabetes duration, and markers of chronic hyperglycemia. Furthermore, the difficulty in the interpretation of data extracted from the blinded CGM system, which also requires a minimum of 3 capillary blood glucose measurements for calibration, should be carefully analyzed to ensure the accuracy and usefulness of the blinded CGM system as a tool for diabetes management in developing countries. Further studies are necessary to establish the role of GV in the development of CAN in patients with T1D.     

A stochastic mathematical model of methicillin resistant Staphylococcus aureus transmission in an intensive care unit: predicting the impact of interventions

OBJECTIVES: To estimate the transmission rate of MRSA in an intensive care unit (ICU) in an 800 bed Australian teaching hospital and predict the impact of infection control interventions. METHODS: A mathematical model was developed which consisted of four compartments: colonised and uncolonised patients and contaminated and uncontaminated health-care workers (HCWs). Patient movements, MRSA acquisition and daily prevalence data were collected from an ICU over 939 days. Hand hygiene compliance and the probability of MRSA transmission from patient to HCW per discordant contact were measured during the study. Attack rate and reproduction ratio were estimated using Bayesian methods. The impact of a number of interventions on attack rate was estimated using both stochastic and deterministic versions of the model. RESULTS: The mean number of secondary cases arising from the ICU admission of colonised patients, also called the ward reproduction ratio, R(w), was estimated to be 0.50 (95% CI 0.39-0.62). The attack rate was one MRSA transmission per 160 (95% CI 130-210) uncolonised-patient days. Results were not sensitive to uncertainty in measured model parameters (hand hygiene rate and transmission probability per contact). Hand hygiene was predicted to be the most effective intervention. Decolonisation was predicted to be relatively ineffective. Increasing HCW numbers was predicted to increase MRSA transmission, in the absence of patient cohorting. The predictions of the stochastic model differed from those of the deterministic model, with lower levels of colonisation predicted by the stochastic model. CONCLUSIONS: The number of secondary cases of MRSA colonisation within the ICU in this study was below unity. Transmission of MRSA was sustained through admission of colonised patients. Stochastic model simulations give more realistic predictions in hospital ward settings than deterministic models. Increasing staff does not necessarily lead to reduced transmission of nosocomial pathogens.     

Ethnic Differences in Glycaemic Control in People with Type 2 Diabetes Mellitus Living in Scotland

Background and Aims

Previous studies have investigated the association between ethnicity and processes of care and intermediate outcomes of diabetes, but there are limited population-based studies available. The aim of this study was to use population-based data to investigate the relationships between ethnicity and glycaemic control in men and women with diabetes mellitus living in Scotland

Methods

We used a 2008 extract from the population-based national electronic diabetes database of Scotland. The association between ethnicity with mean glycaemic control in type 2 diabetes mellitus was examined in a retrospective cohort study, including adjustment for a number of variables including age, sex, socioeconomic status, body mass index (BMI), prescribed treatment and duration of diabetes.

Results

Complete data for analyses were available for 56,333 White Scottish adults, 2,535 Pakistanis, 857 Indians, 427 Chinese and 223 African-Caribbeans. All other ethnic groups had significantly (p<0.05) greater proportions of people with suboptimal glycaemic control (HbA1c >58 mmol/mol, 7.5%) compared to the White Scottish group, despite generally younger mean age and lower BMI. Fully adjusted odds ratios for suboptimal glycaemic control were significantly higher among Pakistanis and Indians (1.85, 95% CI: 1.68–2.04, and 1.62,95% CI: 1.38–1.89) respectively.

Conclusions

Pakistanis and Indians with type 2 diabetes mellitus were more likely to have suboptimal glycaemic control than the white Scottish population. Further research on health services and self-management are needed to understand the association between ethnicity and glycaemic control to address ethnic disparities in glycaemic control.     

Risk of severe hypoglycaemia in insulin treated diabetic patients transferred to human insulin: a case control study.

OBJECTIVE--To examine whether transfer from animal insulin to human insulin is associated with an increased risk of severe hypoglycaemia. DESIGN--Matched case-control study of insulin treated diabetic patients admitted to hospital because of hypoglycaemia during 1984-7, the period when human insulin was introduced into treatment. SETTING--Case admissions and control admissions were obtained from eight public hospitals within the Swiss canton of Berne and a second control group comprised members of the Bernese section of the Swiss Diabetes Association. SUBJECTS--94 patients with insulin treated diabetes with a total of 112 admissions for hypoglycaemia during 1984-7 (case admissions), 182 patients with insulin treated diabetes seen in the same hospitals for reasons other than hypoglycaemia with a total of 225 admissions (control admissions), and 86 insulin treated diabetic patients who were members of the Bernese section of the Swiss Diabetes Association. MAIN OUTCOME MEASURES--Type of insulin used at time of admission, glycaemic control as measured by amount of glycated haemoglobin or glucose concentration; severity of hypoglycaemia. RESULTS--Treatment with human insulin at admission was more common in cases than controls (52/112 (46%) admissions v 77/225 (34%); p = 0.003). 116 out of 129 (90%) of admissions taking human insulin had been transferred from animal insulin, mainly because of non-availability of porcine insulins. The ratio of rate of hypoglycaemia in those taking human insulin to the rate in those taking animal insulin was 2.4 (95% confidence interval 1.3 to 4.4). Other risk factors for hypoglycaemia were a history of hypoglycaemic coma (rate ratio of history to no history 3.8, 2.3 to 6.4) and good glycaemic control (rate ratio of good to poor control 3.9, 1.4 to 7.5). With multivariate analysis the increase in rate ratio associated with use of human insulin rose to 3.0 (1.4 to 6.4). Comparison with the diabetes association controls also showed an increased risk associated with use of human insulin (2.2; 1.1 to 4.8). CONCLUSIONS--Transfer of treatment from animal insulin to human insulin was associated with an increased risk of severe hypoglycaemia. Caution should be exercised when transferring diabetic patients to human insulin. Further studies are required to elucidate why this effect occurs.     

Occurence of adverse events due to continuous glucose monitoring

Aims: Continuous glucose monitoring (CGM) using transcutaneous sensors is becoming a sophisticated method to control and regulate glucose metabolism. The transcutaneous sensor of the CGM system (CGMStrade mark Medtronic Minimed, Northridge, CA, USA) is chosen to measure glucose concentration in interstitial fluid up to three days after insertion even though its function remains stable for a longer period. The question arises, which factors really limit the period of sensor insertion without unnecessary risk. The aim of this study was to assess any adverse events occurring in the course of 9 days after the sensor insertion. Methods: In a group of 22 healthy volunteers aged 21.8+/-1.30 y (mean +/- SE) a total of 26 sensors was inserted subcutaneously in gluteal or lumbar region for 9 days. Before insertion the site was sprayed with an antiseptic (Cutasept F, Bode Chemie, Hamburg, Germany). Local adverse reactions and disturbances in general condition were examined. Results: In the course of 184 sensor-days, there were only minor local adverse events: hypersensitivity, itching, pain, redness, burning, subcutaneous hemorrhage. Additionally, sleep disturbances, attention deficits, problems related to the CGMS monitor, to adhesive tape and/or sensor were found. None of these resulted in sensor withdrawal. In 12 volunteers (55 %) no complications were observed. The sensor function measured according to electrical signals (ISIG) failed (always on day 1-2) in 4 cases (16 %). Conclusions: The present FDA approved 3-day insertion period for Medtronic transcutaneous sensor does not seem to limit its use and appears to be worth a careful revision.     

Use of a Continuous Glucose Monitoring System in High-Risk Hospitalized Noncritically Ill Patients With Diabetes After Cardiac Surgery and During Their Transition of Care From the Intensive Care Unit During COVID-19: A Pilot Study

ObjectiveContinuous glucose monitoring (CGM) has demonstrated benefits in managing inpatient diabetes. We initiated this single-arm pilot feasibility study during the COVID-19 pandemic in 11 patients with diabetes to determine the feasibility and accuracy of real-time CGM in patients who underwent cardiac surgery and whose care was being transitioned from the intensive care unit.MethodsA Clarke error grid analysis was used to compare CGM and point-of-care measurements. The mean absolute relative difference (MARD) of the paired measurements was calculated to assess the accuracy of CGM for glucose measurements during the first 24 hours on CGM, the remaining time on CGM, and for different chronic kidney disease (CKD) strata.ResultsOverall MARD between point-of-care and CGM measurements was 14.80%. MARD for patients without CKD IV and V with an estimated glomerular filtration rate (eGFR) of ≥20 mL/min/1.73 m² was 12.13%. Overall, 97% of the CGM values were within the no-risk zone of the Clarke error grid analysis. For the first 24 hours, a sensitivity analysis of the overall MARD for all patients and those with an eGFR of ≥20 mL/min/1.73 m² was 15.42% ± 14.44% and 12.80% ± 7.85%, respectively. Beyond the first 24 hours, overall MARD for all patients and those with an eGFR of ≥20 mL/min/1.73 m² was 14.54% ± 13.21% and 11.86% ± 7.64%, respectively.ConclusionCGM has shown great promise in optimizing inpatient diabetes management in the noncritical care setting and after the transition of care from the intensive care unit with high clinical reliability and accuracy. More studies are needed to further assess CGM in patients with advanced CKD.