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1.  Phase II study of tight glycaemic control in COPD patients with exacerbations admitted to the acute medical unit 
BMJ Open  2011;1(1):e000210.
Hyperglycaemia is associated with poor outcomes from exacerbations of chronic obstructive pulmonary disease (COPD). Glycaemic control could improve outcomes by reducing infection, inflammation and myopathy. Most patients with COPD are managed on the acute medical unit (AMU) outside intensive care (ICU).
To determine the feasibility, safety and efficacy of tight glycaemic control in patients on an AMU.
Prospective, non-randomised, phase II, single-arm study of tight glycaemic control in COPD patients with acute exacerbations and hyperglycaemia admitted to the AMU. Participants received intravenous, then subcutaneous, insulin to control blood glucose to 4.4–6.5 mmol/l. Tight glycaemic control was evaluated: feasibility, protocol adherence; acceptability, patient questionnaire; safety, frequency of hypoglycaemia (capillary blood glucose (CBG) <2.2 mmol/l and 2.2–3.3 mmol/l); efficacy, median CBG, fasting CBG, proportion of measurements/time in target range, glycaemic variability. Results were compared with 25 published ICU studies.
20 patients (10 females, age 71±9 years; forced expiratory volume in 1 s: 41±16% predicted) were recruited. Tight glycaemic control was feasible (78% CBG measurements and 89% of insulin-dose adjustments were adherent to protocol) and acceptable to patients. 0.2% CBG measurements were <2.2 mmol/l and 4.1% measurements 2.2–3.3 mmol/l. The study CBG and proportion of measurements/time in target range were similar to that of ICU studies, whereas the fasting CBG was lower, and the glycaemic variability was greater.
Tight glycaemic control is feasible and has similar safety and efficacy on AMU to ICU. However, as more recent ICU studies have shown no benefit and possible harm from tight glycaemic control, alternative strategies for blood glucose control in COPD exacerbations should now be explored.
Trial registration number
ISRCTN: 42412334. NCT00764556.
Article summary
Article focus
Hyperglycaemia is associated with poor outcomes from acute chronic obstructive pulmonary disease (COPD) exacerbations requiring hospital admission.
It is not known whether glycaemic control can improve COPD exacerbation outcomes.
The aim of this phase II study was to determine the feasibility, safety and efficacy of tight glycaemic control with insulin in COPD patients with exacerbations on acute medical wards, towards testing this intervention in a randomised controlled trial.
Key messages
Tight glycaemic control with insulin was feasible and acceptable to patients in a general ward setting.
The efficacy and safety of tight glycaemic control were similar in COPD patients on acute medical wards to that achieved in intensive care settings, with improved glycaemic control but increased hypoglycaemia and glycaemic variability.
Strengths and limitations of this study
This study was conducted when tight glycaemic control was standard practice in intensive care units (ICUs), following the publication of two single-centre studies demonstrating reduced morbidity and mortality compared with conventional glycaemic control.
More recent ICU studies have shown no benefit and possible harm from tight glycaemic control.
In this context, our finding that tight glycaemic control in the acute medical unit has a similar safety and efficacy to ICU protocols indicates that we should explore alternative strategies for blood glucose control in COPD exacerbations.
PMCID: PMC3191583  PMID: 22021788
Hyperglycaemia; insulin; hypoglycaemia; glycaemic variability; COPD; acute medical unit; Chronic airways disease; clinical pharmacology; medical education and training; respiratory infections; cystic fibrosis; other metabolic; iron; porphyria
2.  Glucose control in intensive care: usability, efficacy and safety of Space GlucoseControl in two medical European intensive care units 
The Space GlucoseControl system (SGC) is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm (B. Braun, Melsungen, Germany). We aimed to investigate the performance of the SGC in medical critically ill patients.
Two open clinical investigations in tertiary centers in Graz, Austria and Zurich, Switzerland were performed. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (≤2.2 mmol/L) and the percentage of time spent below this cutoff level. Usability was analyzed with a standardized questionnaire given to involved nursing staff after the trial.
Forty medical critically ill patients (age, 62 ± 15 years; body mass index, 30.0 ± 8.9 kg/m2; APACHE II score, 24.8 ± 5.4; 27 males; 8 with diabetes) were included for a period of 6.5 ± 3.7 days (n = 20 in each center). The primary endpoint (time in target range 4.4 to 8.3 mmol/l) was reached in 88.3% ± 9.3 of the time and mean arterial blood glucose was 6.7 ± 0.4 mmol/l. The sampling interval was 2.2 ± 0.4 hours. The mean daily insulin dose was 87.2 ± 64.6 IU. The adherence to the given insulin dose advice was high (98.2%). While the percentage of time spent in a moderately hypoglycemic range (2.2 to 3.3 mmol/L) was low (0.07 ± 0.26% of the time), one severe hypoglycemic episode (<2.2 mmol/L) occurred (2.5% of patients or 0.03% of glucose readings).
SGC is a safe and efficient method to control blood glucose in critically ill patients as assessed in two European medical intensive care units.
PMCID: PMC4118658  PMID: 25074071
Tight glycemic control; Critical illness; Critically ill patients; Protocol; Computer-assisted glycemic control; Insulin infusion protocol; Glucose control in intensive care
3.  Space GlucoseControl system for blood glucose control in intensive care patients - a European multicentre observational study 
BMC Anesthesiology  2016;16:8.
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.
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.
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 %).
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 NCT01523665
PMCID: PMC4722682  PMID: 26801983
Glycaemia control; Critically ill patients; Insulin protocol; Space Glucose Control; Enhanced Model Predictive Control (eMPC)
4.  Towards a feasible algorithm for tight glycaemic control in critically ill patients: a systematic review of the literature 
Critical Care  2006;10(1):R19.
Tight glycaemic control is an important issue in the management of intensive care unit (ICU) patients. The glycaemic goals described by Van Den Berghe and colleagues in their landmark study of intensive insulin therapy appear difficult to achieve in a real life ICU setting. Most clinicians and nurses are concerned about a potentially increased frequency of severe hypoglycaemic episodes with more stringent glycaemic control. One of the steps we took before we implemented a glucose regulation protocol was to review published trials employing insulin/glucose algorithms in critically ill patients.
We conducted a search of the PubMed, Embase and Cochrane databases using the following terms: 'glucose', 'insulin', 'protocol', 'algorithm', 'nomogram', 'scheme', 'critically ill' and 'intensive care'. Our search was limited to clinical trials conducted in humans. The aim of the papers selected was required to be glycaemic control in critically ill patients; the blood glucose target was required to be 10 mmol/l or under (or use of a protocol that resulted in a mean blood glucose = 10 mmol/l). The studies were categorized according to patient type, desired range of blood glucose values, method of insulin administration, frequency of blood glucose control, time taken to achieve the desired range for glucose, proportion of patients with glucose in the desired range, mean blood glucose and frequency of hypoglycaemic episodes.
A total of twenty-four reports satisfied our inclusion criteria. Most recent studies (nine) were conducted in an ICU; nine others were conducted in a perioperative setting and six were conducted in patients with acute myocardial infarction or stroke. Studies conducted before 2001 did not include normoglycaemia among their aims, which changed after publication of the study by Van Den Berghe and coworkers in 2001; glycaemic goals became tighter, with a target range between 4 and 8 mmol/l in most studies.
Studies using a dynamic scale protocol combining a tight glucose target and the last two blood glucose values to determine the insulin infusion rate yielded the best results in terms of glycaemic control and reported low frequencies of hypoglycaemic episodes.
PMCID: PMC1550808  PMID: 16469124
5.  Glycaemic control in Australia and New Zealand before and after the NICE-SUGAR trial: a translational study 
Critical Care  2013;17(5):R215.
There is no information on the uptake of Intensive Insulin Therapy (IIT) before the Normoglycemia in Intensive Care Evaluation and Surviving Using Glucose Algorithm Regulation (NICE-SUGAR) trial in Australia and New Zealand (ANZ) and on the bi-national response to the trial, yet such data would provide important information on the evolution of ANZ practice in this field. We aimed to study ANZ glycaemic control before and after the publication of the results of the NICE-SUGAR trial.
We analysed glucose control in critically ill patients across Australia and New Zealand during a two-year period before and after the publication of the NICE-SUGAR study. We used the mean first day glucose (Glu1) (a validated surrogate of ICU glucose control) to define practice. The implementation of an IIT protocol was presumed if the median of Glu1 measurements was <6.44 mmol/L for a given ICU. Hypoglycaemia was categorised as severe (glucose ≤2.2 mmol/L) or moderate (glucose ≤3.9 mmol/L).
We studied 49 ICUs and 176,505 patients. No ICU practiced IIT before or after NICE-SUGAR. Overall, Glu1 increased from 7.96 (2.95) mmol/L to 8.03 (2.92) mmol/L (P <0.0001) after NICE-SUGAR. Similar increases were noted in all patient subgroups studied (surgical, medical, insulin dependent diabetes mellitus, ICU stay >48/<48 hours). The rate of severe and moderate hypoglycaemia before and after NICE-SUGAR study were 0.59% vs. 0.55% (P =0.33) and 6.62% vs. 5.68% (P <0.0001), respectively. Both crude and adjusted mortalities declined over the study period.
IIT had not been adopted in ANZ before the NICE-SUGAR study and glycaemic control corresponded to that delivered in the control arm of NICE-SUGAR trial. There were only minor changes in practice after the trial toward looser glycaemic control. The rate of moderate hypoglycaemia and mortality decreased along with such changes.
PMCID: PMC4056083  PMID: 24088368
6.  Tight glycaemic control: a prospective observational study of a computerised decision-supported intensive insulin therapy protocol 
Critical Care  2007;11(4):R75.
A single centre has reported that implementation of an intensive insulin protocol, aiming for tight glycaemic control (blood glucose 4.4 to 6.1 mmol/l), resulted in significant reduction in mortality in longer stay medical and surgical critically ill patients. Our aim was to determine the degree to which tight glycaemic control can be maintained using an intensive insulin therapy protocol with computerized decision support and to identify factors that may be associated with the degree of control.
At a general adult 22-bed intensive care unit, we implemented an intensive insulin therapy protocol in mechanically ventilated patients, aiming for a target glucose range of 4.4 to 6.1 mmol/l. The protocol was integrated into the computerized information management system by way of a decision support program. The time spent in each predefined blood glucose band was estimated, assuming a linear trend between measurements.
Fifty consecutive patients were investigated, involving analysis of 7,209 blood glucose samples, over 9,214 hours. The target tight glycaemic control band (4.4 to 6.1 mmol/l) was achieved for a median of 23.1% of the time that patients were receiving intensive insulin therapy. Nearly half of the time (median 48.5%), blood glucose was within the band 6.2 to 7.99 mmol/l. Univariate analysis revealed that body mass index (BMI), Acute Physiology and Chronic Health Evaluation (APACHE) II score and previous diabetes each explained approximately 10% of the variability in tight glycaemic control. BMI and APACHE II score explained most (27%) of the variability in tight glycaemic control in the multivariate analysis, after adjusting for age and previous diabetes.
Use of the computerized decision supported intensive insulin therapy protocol did result in achievement of tight glycaemic control for a substantial percentage of each patient's stay, although it did deliver 'normoglycaemia' (4.4 to about 8 mmol/l) for nearly 75% of the time. Tight glycaemic control was difficult to achieve in critically ill patients using this protocol. More sophisticated methods such as continuous blood glucose monitoring with automated insulin and glucose infusion adjustment may be a more effective way to achieve tight glycaemic control. Glycaemia in patients with high BMI and APACHE II scores may be more difficult to control using intensive insulin therapy protocols. Trial registration number 05/Q0505/1.
PMCID: PMC2206495  PMID: 17623086
7.  The effects of baseline characteristics, glycaemia treatment approach, and glycated haemoglobin concentration on the risk of severe hypoglycaemia: post hoc epidemiological analysis of the ACCORD study 
Objectives To investigate potential determinants of severe hypoglycaemia, including baseline characteristics, in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial and the association of severe hypoglycaemia with levels of glycated haemoglobin (haemoglobin A1C) achieved during therapy.
Design Post hoc epidemiological analysis of a double 2×2 factorial, randomised, controlled trial.
Setting Diabetes clinics, research clinics, and primary care clinics.
Participants 10 209 of the 10 251 participants enrolled in the ACCORD study with type 2 diabetes, a haemoglobin A1C concentration of 7.5% or more during screening, and aged 40-79 years with established cardiovascular disease or 55-79 years with evidence of significant atherosclerosis, albuminuria, left ventricular hypertrophy, or two or more additional risk factors for cardiovascular disease (dyslipidaemia, hypertension, current smoker, or obese).
Interventions Intensive (haemoglobin A1C <6.0%) or standard (haemoglobin A1C 7.0-7.9%) glucose control.
Main outcome measures Severe hypoglycaemia was defined as episodes of “low blood glucose” requiring the assistance of another person and documentation of either a plasma glucose less than 2.8 mmol/l (<50 mg/dl) or symptoms that promptly resolved with oral carbohydrate, intravenous glucose, or glucagon.
Results The annual incidence of hypoglycaemia was 3.14% in the intensive treatment group and 1.03% in the standard glycaemia group. We found significantly increased risks for hypoglycaemia among women (P=0.0300), African-Americans (P<0.0001 compared with non-Hispanic whites), those with less than a high school education (P<0.0500 compared with college graduates), aged participants (P<0.0001 per 1 year increase), and those who used insulin at trial entry (P<0.0001). For every 1% unit decline in the haemoglobin A1C concentration from baseline to 4 month visit, there was a 28% (95% CI 19% to 37%) and 14% (4% to 23%) reduced risk of hypoglycaemia requiring medical assistance in the standard and intensive groups, respectively. In both treatment groups, the risk of hypoglycaemia requiring medical assistance increased with each 1% unit increment in the average updated haemoglobin A1C concentration (standard arm: hazard ratio 1.76, 95% CI 1.50 to 2.06; intensive arm: hazard ratio 1.15, 95% CI 1.02 to 1.21).
Conclusions A greater drop in haemoglobin A1C concentration from baseline to the 4 month visit was not associated with an increased risk for hypoglycaemia. Patients with poorer glycaemic control had a greater risk of hypoglycaemia, irrespective of treatment group. Identification of baseline subgroups with increased risk for severe hypoglycaemia can provide guidance to clinicians attempting to modify patient therapy on the basis of individual risk.
Trial registration number NCT00000620.
PMCID: PMC2803743  PMID: 20061360
8.  Diabetes: glycaemic control in type 2 
BMJ Clinical Evidence  2008;2008:0609.
Diabetes mellitus is now seen as a progressive disorder of glucose metabolism, affecting about 5% of the population worldwide, over 85% of whom have type 2 diabetes. Type 2 diabetes may occur with obesity, hypertension and dyslipidaemia (the metabolic syndrome), which are powerful predictors of CVD. Blood glucose levels rise progressively over time in people with type 2 diabetes regardless of treatment, causing microvascular and macrovascular complications.
Methods and outcomes
We conducted a systematic review and aimed to answer the following clinical question: What are the effects of interventions in adults with type 2 diabetes? We searched: Medline, Embase, The Cochrane Library and other important databases up to October 2006 (BMJ Clinical Evidence reviews are updated periodically, please check our website for the most up-to-date version of this review). We included harms alerts from relevant organisations such as the US Food and Drug Administration (FDA) and the UK Medicines and Healthcare products Regulatory Agency (MHRA).
We found 69 systematic reviews, RCTs, or observational studies that met our inclusion criteria. We performed a GRADE evaluation of the quality of evidence for interventions.
In this systematic review we present information relating to the effectiveness and safety of the following interventions: combined oral drug treatment, diet, education, insulin (continuous subcutaneous infusion), insulin, intensive treatment programmes, meglitinides (nateglinide, repaglinide), metformin, monotherapy, blood glucose self-monitoring (different frequencies), and sulphonylureas (newer or older).
Key Points
Diabetes mellitus is now seen as a progressive disorder of glucose metabolism; it affects about 5% of the population worldwide, over 85% of whom have type 2 diabetes. Type 2 diabetes is often associated with obesity, hypertension, and dyslipidaemia (the metabolic syndrome), which are powerful predictors of CVD.Type 2 diabetes is a disease in which glucose levels rise over time, with or without treatment and irrespective of the type of treatment given. This rise may lead to microvascular and macrovascular complications.
Most people with type 2 diabetes will eventually need treatment with oral hypoglycaemic agents. Metformin reduces glycated haemoglobin by 1−2% and reduces mortality compared with diet alone, without increasing weight, but it can cause hypoglycaemia compared with placebo. Sulphonylureas reduce HbA1c by 1−2% compared with diet alone. Older sulphonylureas can cause weight gain and hypoglycaemia, but the risk of these adverse effects may be lower with newer-generation sulphonylureas. Meglitinides (nateglinide, repaglinide) may reduce HbA1c by 0.4-0.9% compared with placebo, but may cause hypoglycaemia. Combined oral drug treatment may reduce HbA1c levels more than monotherapy, but increases the risk of hypoglycaemia. Insulin is no more effective than sulphonylureas in improving glucose control in people with newly diagnosed type 2 diabetes, and is associated with a higher rate of major hypoglycaemic episodes, and with weight gain.
Individual or group intensive educational programmes may reduce HbA1c compared with usual care, although studies have been of poor quality.
Insulin improves glycaemic control in people with inadequate control of HbA1c from oral drug treatment, but is associated with weight gain, and an increased risk of hypoglycaemia. Adding metformin to insulin improves glucose control compared with insulin alone, but increases gastrointestinal adverse effects. However, the combination may cause less weight gain than insulin alone.
Monitoring of blood glucose levels has not been shown to improve glycaemic control in people not being treated with insulin.
Diet may be less effective than metformin or sulphonylureas in improving glucose control, although sulphonylureas were associated with higher rates of hypoglycaemia. However, there is consensus that weight reduction in people with type 2 diabetes can improve glycaemic control, as well as conferring other health benefits.
PMCID: PMC2907982  PMID: 19450326
9.  Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change 
Critical Care  2008;12(2):R49.
Stress-induced hyperglycaemia is prevalent in critical care. Control of blood glucose levels to within a 4.4 to 6.1 mmol/L range or below 7.75 mmol/L can reduce mortality and improve clinical outcomes. The Specialised Relative Insulin Nutrition Tables (SPRINT) protocol is a simple wheel-based system that modulates insulin and nutritional inputs for tight glycaemic control.
SPRINT was implemented as a clinical practice change in a general intensive care unit (ICU). The objective of this study was to measure the effect of the SPRINT protocol on glycaemic control and mortality compared with previous ICU control methods. Glycaemic control and mortality outcomes for 371 SPRINT patients with a median Acute Physiology And Chronic Health Evaluation (APACHE) II score of 18 (interquartile range [IQR] 15 to 24) are compared with a 413-patient retrospective cohort with a median APACHE II score of 18 (IQR 15 to 23).
Overall, 53.9% of all measurements were in the 4.4 to 6.1 mmol/L band. Blood glucose concentrations were found to be log-normal and thus log-normal statistics are used throughout to describe the data. The average log-normal glycaemia was 6.0 mmol/L (standard deviation 1.5 mmol/L). Only 9.0% of all measurements were below 4.4 mmol/L, with 3.8% below 4 mmol/L and 0.1% of measurements below 2.2 mmol/L. On SPRINT, 80% more measurements were in the 4.4 to 6.1 mmol/L band and standard deviation of blood glucose was 38% lower compared with the retrospective control. The range and peak of blood glucose were not correlated with mortality for SPRINT patients (P >0.30). For ICU length of stay (LoS) of greater than or equal to 3 days, hospital mortality was reduced from 34.1% to 25.4% (-26%) (P = 0.05). For ICU LoS of greater than or equal to 4 days, hospital mortality was reduced from 34.3% to 23.5% (-32%) (P = 0.02). For ICU LoS of greater than or equal to 5 days, hospital mortality was reduced from 31.9% to 20.6% (-35%) (P = 0.02). ICU mortality was also reduced but the P value was less than 0.13 for ICU LoS of greater than or equal to 4 and 5 days.
SPRINT achieved a high level of glycaemic control on a severely ill critical cohort population. Reductions in mortality were observed compared with a retrospective hyperglycaemic cohort. Range and peak blood glucose metrics were no longer correlated with mortality outcome under SPRINT.
PMCID: PMC2447603  PMID: 18412978
10.  The effect of diabetes mellitus on the association between measures of glycaemic control and ICU mortality: a retrospective cohort study 
Critical Care  2013;17(2):R52.
In critical illness, four measures of glycaemic control are associated with ICU mortality: mean glucose concentration, glucose variability, the incidence of hypoglycaemia (≤ 2.2 mmol/l) or low glucose (2.3 to 4.7 mmol/l). Underlying diabetes mellitus (DM) might affect these associations. Our objective was to study whether the association between these measures of glycaemic control and ICU mortality differs between patients without and with DM and to explore the cutoff value for detrimental low glucose in both cohorts.
This retrospective database cohort study included patients admitted between January 2004 and June 2011 to a 24-bed medical/surgical ICU in a teaching hospital. We analysed glucose and outcome data from 10,320 patients: 8,682 without DM and 1,638 with DM. The cohorts were subdivided into quintiles of mean glucose and quartiles of glucose variability. Multivariable regression models were used to examine the independent association between the four measures of glycaemic control and ICU mortality, and for defining the cutoff value for detrimental low glucose.
Regarding mean glucose, a U-shaped relation was observed in the non-DM cohort with an increased ICU mortality in the lowest and highest glucose quintiles (odds ratio = 1.4 and 1.8, P < 0.001). No clear pattern was found in the DM cohort. Glucose variability was related to ICU mortality only in the non-DM cohort, with highest ICU mortality in the upper variability quartile (odds ratio = 1.7, P < 0.001). Hypoglycaemia was associated with ICU mortality in both cohorts (odds ratio non-DM = 2.5, P < 0.001; odds ratio DM = 4.2, P = 0.001), while low-glucose concentrations up to 4.9 mmol/l were associated with an increased risk of ICU mortality in the non-DM cohort and up to 3.5 mmol/l in the DM cohort.
Mean glucose and high glucose variability are related to ICU mortality in the non-DM cohort but not in the DM cohort. Hypoglycaemia (≤ 2.2 mmol/l) was associated with ICU mortality in both. The cutoff value for detrimental low glucose is higher in the non-DM cohort (4.9 mmol/l) than in the DM cohort (3.5 mmol/l). While hypoglycaemia (≤ 2.2 mmol/l) should be avoided in both groups, DM patients seem to tolerate a wider glucose range than non-DM patients.
PMCID: PMC3733428  PMID: 23510051
11.  Reducing the impact of insulin sensitivity variability on glycaemic outcomes using separate stochastic models within the STAR glycaemic protocol 
The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12–48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol.
The value of SI was identified hourly for each patient using a validated physiological model. Variability of SI was then calculated as the hour-to-hour percentage change in SI. SI was examined using 6 hour blocks of SI to display trends while mitigating the effects of noise. To reduce the impact of SI variability on achieving glycaemic control a new stochastic model for the most variable period, 0–18 hours, was generated. Virtual simulations were conducted using an existing glycaemic control protocol (STAR) to investigate the clinical impact of using this separate stochastic model during this period of increased metabolic variability.
For the first 18 hours, over 80% of all SI values were less than 0.5 × 10-3 L/mU.min, compared to 65% for >18 hours. Using the new stochastic model for the first 18 hours of ICU stay reduced the number of hypoglycaemic measurements during virtual trials. For time spent below 4.4, 4.0, and 3.0 mmol/L absolute reductions of 1.1%, 0.8% and 0.1% were achieved, respectively. No severe hypoglycaemic events (BG < 2.2 mmol/L) occurred for either case.
SI levels increase significantly, while variability decreases during the first 18 hours of a patients stay in ICU. Virtual trials, using a separate stochastic model for this period, demonstrated a reduction in variability and hypoglycaemia during the first 18 hours without adversely affecting the overall level of control. Thus, use of multiple models can reduce the impact of SI variability during model-based glycaemic control.
PMCID: PMC4013832  PMID: 24739335
Insulin sensitivity; Intensive care; Glycaemia; Model-based control
12.  Optimal blood glucose control in severely burned patients: a long way to go, but one step closer 
Critical Care  2013;17(5):1005.
Over the past years there has been a significant decrease in mortality and morbidity in patients suffering from severe burns due to improved burn wound management and approaches in critical care. Survival is no longer the exception, but unfortunately death still occurs. One of the key elements concerning state-of-the-art burn care is blood glucose control and insulin therapy; it is well known that burn-induced hyperglycaemia is associated with adverse clinical outcomes. However, controversy for insulin therapy and tight glycaemic control in critically ill and burn patients exists. The increased incidence of hypoglycaemia is the dominant argument against this treatment, because hypoglycaemia is also associated with an increased risk for death in critically ill patients. Taking all current data together, insulin therapy appears both a friend and a foe in the treatment of ICU patients. In order to overcome the limits of tight glycaemic control resulting from hypoglycaemic episodes, current efforts have been directed towards the development of protocols allowing for implementation of clinically feasible and safe guidelines. Among the strategies addressing this problem are closed loop techniques, which are supported by studies demonstrating their capability of exerting tight glycaemic control without the risk of developing hypoglycaemic episodes. Although closed loop techniques have become readily available, we require further evidence to ensure their safety in various ICU environments, notably in ICUs dealing with burn patients. Nonetheless, it is important to emphasise that glycaemic control and adequate insulin therapy are crucial factors for the final outcome (survival) and require our attention.
PMCID: PMC4056980  PMID: 24107553
13.  Twice daily versus four times daily insulin dose regimens for diabetes in pregnancy: randomised controlled trial 
BMJ : British Medical Journal  1999;319(7219):1223-1227.
To compare perinatal outcome and glycaemic control in two groups of pregnant diabetic patients receiving two insulin regimens.
Randomised controlled open label study.
University affiliated hospital, Israel.
138 patients with gestational diabetes mellitus and 58 patients with pregestational diabetes mellitus received insulin four times daily, and 136 patients with gestational diabetes and 60 patients with pregestational diabetes received insulin twice daily.
Three doses of regular insulin before meals and an intermediate insulin dose before bedtime (four times daily regimen), and a combination of regular and intermediate insulin in the morning and evening (twice daily regimen).
Main outcome measures
Maternal glycaemic control and perinatal outcome.
Mean daily insulin concentration before birth was higher in the women receiving insulin four times daily compared with twice daily: by 22 units (95% confidence interval 12 to 32) in patients with gestational diabetes and by 28 units (15 to 41) in patients with pregestational diabetes. Glycaemic control was better with the four times daily regimen than with the twice daily regimen: in patients with gestational diabetes mean blood glucose concentrations decreased by 0.19 mmol/l (0.13 to 0.25), HbA1c by 0.3% (0.2% to 0.4%), and fructosamine by 41 μmol/l (37 to 45), and adequate glycaemic control (mean blood glucose concentration <5.8 mmol/l) was achieved in 17% (8% to 26%) more women; in patients with pregestational diabetes mean blood glucose concentration decreased by 0.44 mmol/l (0.28 to 0.60), HbA1c by 0.5% (0.2% to 0.8%), and fructosamine by 51 μmol/l (45 to 57), and adequate glycaemic control was achieved in 31% (15% to 47%) more women. Maternal severe hypoglycaemic events, caesarean section, preterm birth, macrosomia, and low Apgar scores were similar in both dose groups. In women with gestational diabetes the four times daily regimen resulted in a lower rate of overall neonatal morbidity than the twice daily regimen (relative risk 0.59, 0.38 to 0.92), and the relative risk for hyperbilirubinaemia and hypoglycaemia was lower (0.51, 0.29 to 0.91 and 0.12, 0.02 to 0.97 respectively). The relative risk of hypoglycaemia in newborn infants to mothers with pregestational diabetes was 0.17 (0.04 to 0.74).
Giving insulin four times rather than twice daily in pregnancy improved glycaemic control and perinatal outcome without further risking the mother.
Key messagesImproving maternal glycaemic control during pregnancy is the key to better perinatal outcomeIn pregnant diabetic women insulin four times daily achieved better glycaemic control and lower rate of perinatal complications (hypoglycaemia, hyperbilirubinaemia) than insulin twice dailyBetter glycaemic control resulted from a larger total daily insulin doseThe intensified regimen did not lead to higher rate of severe maternal hypoglycaemia
PMCID: PMC28269  PMID: 10550081
14.  Clinical review: Strict or loose glycemic control in critically ill patients - implementing best available evidence from randomized controlled trials 
Critical Care  2010;14(3):223.
Glycemic control aiming at normoglycemia, frequently referred to as 'strict glycemic control' (SGC), decreased mortality and morbidity of adult critically ill patients in two randomized controlled trials (RCTs). Five successive RCTs, however, failed to show benefit of SGC with one trial even reporting an unexpected higher mortality. Consequently, enthusiasm for the implementation of SGC has declined, hampering translation of SGC into daily ICU practice. In this manuscript we attempt to explain the variances in outcomes of the RCTs of SGC, and point out other limitations of the current literature on glycemic control in ICU patients. There are several alternative explanations for why the five negative RCTs showed no beneficial effects of SGC, apart from the possibility that SGC may indeed not benefit ICU patients. These include, but are not restricted to, variability in the performance of SGC, differences among trial designs, changes in standard of care, differences in timing (that is, initiation) of SGC, and the convergence between the intervention groups and control groups with respect to achieved blood glucose levels in the successive RCTs. Additional factors that may hamper translation of SGC into daily ICU practice include the feared risk of severe hypoglycemia, additional labor associated with SGC, and uncertainties about who the primarily responsible caregiver should be for the implementation of SGC.
PMCID: PMC2911685  PMID: 20550725
15.  Influence of human insulin on symptoms and awareness of hypoglycaemia: a randomised double blind crossover trial. 
BMJ : British Medical Journal  1991;303(6803):622-626.
OBJECTIVE--To investigate the apparent increased risk of severe hypoglycaemia associated with use of human insulin by comparing the pattern of symptoms of hypoglycaemia with human insulin and porcine insulin. DESIGN--Randomised controlled double blind crossover trial of treatment with human insulin and porcine insulin, with two treatment periods of six weeks. SETTING--Diabetes outpatient department of a university teaching hospital in Berne, Switzerland. PATIENTS--44 patients (25 men, 19 women) aged 14 to 60 years, with insulin dependent diabetes mellitus. All patients met the following criteria: receiving treatment with fast acting soluble insulin and long acting protamine insulin; performing multiple daily fingerstick blood glucose self measurements; and had stable glycaemic control with about one mild hypoglycaemic episode a week during the preceding two months. INTERVENTION--Patients were randomised to receive either human or porcine insulin for six weeks and were then changed over to the other type of insulin for a further six weeks. MAIN OUTCOME MEASURE--Questionnaire recording "autonomic" and "neuroglycopenic" symptoms that occurred during hypoglycaemic episodes confirmed by a blood glucose concentration less than or equal to 2.8 mmol/l. RESULTS--Insulin doses and blood glucose, glycated haemoglobin A1c, and fructosamine concentrations were similar during the two treatment periods. 493 questionnaires on hypoglycaemia (234 during treatment with human insulin and 259 during treatment with porcine insulin) were analysed. With human insulin patients were more likely to report lack of concentration (52% v 35%, p = 0.0003) and restlessness (53% v 45%, p = 0.004) and less likely to report hunger (33% v 42%, p = 0.016) than during treatment with porcine insulin. The difference in the pattern of symptoms during the two treatments was similar to that between the 12 patients with a history of recurrent hypoglycaemic coma and the 32 patients without such a history. CONCLUSIONS--The pattern of symptoms associated with human insulin could impair patients' ability to take appropriate steps to avoid severe hypoglycaemia. Caution should be exercised when transferring patients from animal insulin to human insulin, and a large scale randomised trial of the two types of insulin may be justified.
PMCID: PMC1671054  PMID: 1932903
16.  Prevalence of dysglycaemic events among inpatients with diabetes mellitus: a Singaporean perspective 
Singapore Medical Journal  2015;56(7):393-400.
As the effectiveness of intensive glycaemic control is unclear and recommended glycaemic targets are inconsistent, this study aimed to ascertain the prevalence of dysglycaemia among hospitalised patients with diabetes mellitus in an Asian population and evaluate the current standards of inpatient glycaemic control.
A retrospective observational study was conducted at a secondary hospital. Point-of-care blood glucose (BG) values, demographic data, medical history, glycaemic therapy and clinical characteristics were recorded. Dysglycaemia prevalence was calculated as proportions of BG-monitored days with at least one reading exceeding the cut points of 8, 10 and 15 mmol/L for hyperglycaemia, and below the cut point of 4 mmol/L for hypoglycaemia.
Among the 288 patients recruited, hyperglycaemia was highly prevalent (90.3%, 81.3% and 47.6% for the respective cut points), while hypoglycaemia was the least prevalent (18.8%). Dysglycaemic patients were more likely than normoglycaemic patients to have poorer glycated haemoglobin (HbA1c) levels (8.4% ± 2.6% vs. 7.3% ± 1.9%; p = 0.002 for BG > 10 mmol/L) and longer lengths of stay (10.1 ± 8.2 days vs. 6.8 ± 4.7 days; p = 0.007 for BG < 4 mmol/L). Hyperglycaemia was more prevalent in patients on more intensive treatment regimens, such as basal-bolus combination therapy and the use of both insulin and oral hypoglycaemic agents (100.0% and 96.0%, respectively; p < 0.001 for BG > 10 mmol/L).
Inpatient glycaemic control is suboptimal. Factors (e.g. type of treatment regimen, discipline and baseline HbA1c) associated with greater prevalence of dysglycaemia should be given due consideration in patient management.
PMCID: PMC4520918  PMID: 26243976
diabetes mellitus; dysglycaemia; hospital; hyperglycaemia; inpatients
17.  Double blind clinical and laboratory study of hypoglycaemia with human and porcine insulin in diabetic patients reporting hypoglycaemia unawareness after transferring to human insulin. 
BMJ : British Medical Journal  1993;306(6871):167-171.
OBJECTIVES--To compare awareness of hypoglycaemia and physiological responses to hypoglycaemia with human and porcine insulin in diabetic patients who reported loss of hypoglycaemia awareness after transferring to human insulin. DESIGN--Double blind randomised crossover study of clinical experience and physiological responses during slow fall hypoglycaemic clamping with porcine and human insulin. SETTING--Clinical investigation unit of teaching hospital recruiting from diabetes clinics of five teaching hospitals and one district general hospital. SUBJECTS--17 patients with insulin dependent diabetes mellitus of more than five years' duration who had reported altered hypoglycaemia awareness within three months of transferring to human insulin. MAIN OUTCOME MEASURES--Glycaemic control and frequency of hypoglycaemic episodes during two months' treatment with each insulin. Glucose thresholds for physiological and symptomatic responses during clamping. RESULTS--Glycaemic control did not change with either insulin. 136 hypoglycaemic episodes (eight severe) were reported with human insulin and 149 (nine severe) with porcine insulin (95% confidence interval -4 to 2.5, p = 0.63). 20 episodes of biochemical hypoglycaemia occurred with human insulin versus 18 with porcine insulin (-0.8 to 1, p = 0.78). During controlled hypoglycaemia the mean adrenaline response was 138 nmol/l/240 min for both insulins; neurohormonal responses were triggered at 3.0 (SE 0.2) versus 3.1 (0.2) mmol/l of glucose for adrenaline and 2.5 (0.1) versus 2.5 (0.1) mmol/l for subjective awareness. CONCLUSIONS--These data suggest that human insulin per se does not affect the presentation of hypoglycaemia or the neurohumoral, symptomatic, and cognitive function responses to hypoglycaemia in insulin dependent diabetic patients with a history of hypoglycaemia unawareness.
PMCID: PMC1676615  PMID: 8443479
18.  Continuous Subcutaneous Insulin Infusion (CSII) Pumps for Type 1 and Type 2 Adult Diabetic Populations 
Executive Summary
In June 2008, the Medical Advisory Secretariat began work on the Diabetes Strategy Evidence Project, an evidence-based review of the literature surrounding strategies for successful management and treatment of diabetes. This project came about when the Health System Strategy Division at the Ministry of Health and Long-Term Care subsequently asked the secretariat to provide an evidentiary platform for the Ministry’s newly released Diabetes Strategy.
After an initial review of the strategy and consultation with experts, the secretariat identified five key areas in which evidence was needed. Evidence-based analyses have been prepared for each of these five areas: insulin pumps, behavioural interventions, bariatric surgery, home telemonitoring, and community based care. For each area, an economic analysis was completed where appropriate and is described in a separate report.
To review these titles within the Diabetes Strategy Evidence series, please visit the Medical Advisory Secretariat Web site,,
Diabetes Strategy Evidence Platform: Summary of Evidence-Based Analyses
Continuous Subcutaneous Insulin Infusion Pumps for Type 1 and Type 2 Adult Diabetics: An Evidence-Based Analysis
Behavioural Interventions for Type 2 Diabetes: An Evidence-Based Analysis
Bariatric Surgery for People with Diabetes and Morbid Obesity: An Evidence-Based Summary
Community-Based Care for the Management of Type 2 Diabetes: An Evidence-Based Analysis
Home Telemonitoring for Type 2 Diabetes: An Evidence-Based Analysis
Application of the Ontario Diabetes Economic Model (ODEM) to Determine the Cost-effectiveness and Budget Impact of Selected Type 2 Diabetes Interventions in Ontario
The objective of this analysis is to review the efficacy of continuous subcutaneous insulin infusion (CSII) pumps as compared to multiple daily injections (MDI) for the type 1 and type 2 adult diabetics.
Clinical Need and Target Population
Insulin therapy is an integral component of the treatment of many individuals with diabetes. Type 1, or juvenile-onset diabetes, is a life-long disorder that commonly manifests in children and adolescents, but onset can occur at any age. It represents about 10% of the total diabetes population and involves immune-mediated destruction of insulin producing cells in the pancreas. The loss of these cells results in a decrease in insulin production, which in turn necessitates exogenous insulin therapy.
Type 2, or ‘maturity-onset’ diabetes represents about 90% of the total diabetes population and is marked by a resistance to insulin or insufficient insulin secretion. The risk of developing type 2 diabetes increases with age, obesity, and lack of physical activity. The condition tends to develop gradually and may remain undiagnosed for many years. Approximately 30% of patients with type 2 diabetes eventually require insulin therapy.
CSII Pumps
In conventional therapy programs for diabetes, insulin is injected once or twice a day in some combination of short- and long-acting insulin preparations. Some patients require intensive therapy regimes known as multiple daily injection (MDI) programs, in which insulin is injected three or more times a day. It’s a time consuming process and usually requires an injection of slow acting basal insulin in the morning or evening and frequent doses of short-acting insulin prior to eating. The most common form of slower acting insulin used is neutral protamine gagedorn (NPH), which reaches peak activity 3 to 5 hours after injection. There are some concerns surrounding the use of NPH at night-time as, if injected immediately before bed, nocturnal hypoglycemia may occur. To combat nocturnal hypoglycemia and other issues related to absorption, alternative insulins have been developed, such as the slow-acting insulin glargine. Glargine has no peak action time and instead acts consistently over a twenty-four hour period, helping reduce the frequency of hypoglycemic episodes.
Alternatively, intensive therapy regimes can be administered by continuous insulin infusion (CSII) pumps. These devices attempt to closely mimic the behaviour of the pancreas, continuously providing a basal level insulin to the body with additional boluses at meal times. Modern CSII pumps are comprised of a small battery-driven pump that is designed to administer insulin subcutaneously through the abdominal wall via butterfly needle. The insulin dose is adjusted in response to measured capillary glucose values in a fashion similar to MDI and is thus often seen as a preferred method to multiple injection therapy. There are, however, still risks associated with the use of CSII pumps. Despite the increased use of CSII pumps, there is uncertainty around their effectiveness as compared to MDI for improving glycemic control.
Part A: Type 1 Diabetic Adults (≥19 years)
An evidence-based analysis on the efficacy of CSII pumps compared to MDI was carried out on both type 1 and type 2 adult diabetic populations.
Research Questions
Are CSII pumps more effective than MDI for improving glycemic control in adults (≥19 years) with type 1 diabetes?
Are CSII pumps more effective than MDI for improving additional outcomes related to diabetes such as quality of life (QoL)?
Literature Search
Inclusion Criteria
Randomized controlled trials, systematic reviews, meta-analysis and/or health technology assessments from MEDLINE, EMBASE, CINAHL
Adults (≥ 19 years)
Type 1 diabetes
Study evaluates CSII vs. MDI
Published between January 1, 2002 – March 24, 2009
Patient currently on intensive insulin therapy
Exclusion Criteria
Studies with <20 patients
Studies <5 weeks in duration
CSII applied only at night time and not 24 hours/day
Mixed group of diabetes patients (children, adults, type 1, type 2)
Pregnancy studies
Outcomes of Interest
The primary outcomes of interest were glycosylated hemoglobin (HbA1c) levels, mean daily blood glucose, glucose variability, and frequency of hypoglycaemic events. Other outcomes of interest were insulin requirements, adverse events, and quality of life.
Search Strategy
The literature search strategy employed keywords and subject headings to capture the concepts of:
1) insulin pumps, and
2) type 1 diabetes.
The search was run on July 6, 2008 in the following databases: Ovid MEDLINE (1996 to June Week 4 2008), OVID MEDLINE In-Process and Other Non-Indexed Citations, EMBASE (1980 to 2008 Week 26), OVID CINAHL (1982 to June Week 4 2008) the Cochrane Library, and the Centre for Reviews and Dissemination/International Agency for Health Technology Assessment. A search update was run on March 24, 2009 and studies published prior to 2002 were also examined for inclusion into the review. Parallel search strategies were developed for the remaining databases. Search results were limited to human and English-language published between January 2002 and March 24, 2009. Abstracts were reviewed, and studies meeting the inclusion criteria outlined above were obtained. Reference lists were also checked for relevant studies.
Summary of Findings
The database search identified 519 relevant citations published between 1996 and March 24, 2009. Of the 519 abstracts reviewed, four RCTs and one abstract met the inclusion criteria outlined above. While efficacy outcomes were reported in each of the trials, a meta-analysis was not possible due to missing data around standard deviations of change values as well as missing data for the first period of the crossover arm of the trial. Meta-analysis was not possible on other outcomes (quality of life, insulin requirements, frequency of hypoglycemia) due to differences in reporting.
In studies where no baseline data was reported, the final values were used. Two studies (Hanaire-Broutin et al. 2000, Hoogma et al. 2005) reported a slight reduction in HbA1c of 0.35% and 0.22% respectively for CSII pumps in comparison to MDI. A slightly larger reduction in HbA1c of 0.84% was reported by DeVries et al.; however, this study was the only study to include patients with poor glycemic control marked by higher baseline HbA1c levels. One study (Bruttomesso et al. 2008) showed no difference between CSII pumps and MDI on Hba1c levels and was the only study using insulin glargine (consistent with results of parallel RCT in abstract by Bolli 2004). While there is statistically significant reduction in HbA1c in three of four trials, there is no evidence to suggest these results are clinically significant.
Mean Blood Glucose
Three of four studies reported a statistically significant reduction in the mean daily blood glucose for patients using CSII pump, though these results were not clinically significant. One study (DeVries et al. 2002) did not report study data on mean blood glucose but noted that the differences were not statistically significant. There is difficulty with interpreting study findings as blood glucose was measured differently across studies. Three of four studies used a glucose diary, while one study used a memory meter. In addition, frequency of self monitoring of blood glucose (SMBG) varied from four to nine times per day. Measurements used to determine differences in mean daily blood glucose between the CSII pump group and MDI group at clinic visits were collected at varying time points. Two studies use measurements from the last day prior to the final visit (Hoogma et al. 2005, DeVries et al. 2002), while one study used measurements taken during the last 30 days and another study used measurements taken during the 14 days prior to the final visit of each treatment period.
Glucose Variability
All four studies showed a statistically significant reduction in glucose variability for patients using CSII pumps compared to those using MDI, though one, Bruttomesso et al. 2008, only showed a significant reduction at the morning time point. Brutomesso et al. also used alternate measures of glucose variability and found that both the Lability index and mean amplitude of glycemic excursions (MAGE) were in concordance with the findings using the standard deviation (SD) values of mean blood glucose, but the average daily risk range (ADRR) showed no difference between the CSII pump and MDI groups.
Hypoglycemic Events
There is conflicting evidence concerning the efficacy of CSII pumps in decreasing both mild and severe hypoglycemic events. For mild hypoglycemic events, DeVries et al. observed a higher number of events per patient week in the CSII pump group than the MDI group, while Hoogma et al. observed a higher number of events per patient year in the MDI group. The remaining two studies found no differences between the two groups in the frequency of mild hypoglycemic events. For severe hypoglycemic events, Hoogma et al. found an increase in events per patient year among MDI patients, however, all of the other RCTs showed no difference between the patient groups in this aspect.
Insulin Requirements and Adverse Events
In all four studies, insulin requirements were significantly lower in patients receiving CSII pump treatment in comparison to MDI. This difference was statistically significant in all studies. Adverse events were reported in three studies. Devries et al. found no difference in ketoacidotic episodes between CSII pump and MDI users. Bruttomesso et al. reported no adverse events during the study. Hanaire-Broutin et al. found that 30 patients experienced 58 serious adverse events (SAEs) during MDI and 23 patients had 33 SAEs during treatment out of a total of 256 patients. Most events were related to severe hypoglycemia and diabetic ketoacidosis.
Quality of Life and Patient Preference
QoL was measured in three studies and patient preference was measured in one. All three studies found an improvement in QoL for CSII users compared to those using MDI, although various instruments were used among the studies and possible reporting bias was evident as non-positive outcomes were not consistently reported. Moreover, there was also conflicting results in two of the studies using the Diabetes Treatment Satisfaction Questionnaire (DTSQ). DeVries et al. reported no difference in treatment satisfaction between CSII pump users and MDI users while Brutomesso et al. reported that treatment satisfaction improved among CSII pump users.
Patient preference for CSII pumps was demonstrated in just one study (Hanaire-Broutin et al. 2000) and there are considerable limitations with interpreting this data as it was gathered through interview and 72% of patients that preferred CSII pumps were previously on CSII pump therapy prior to the study. As all studies were industry sponsored, findings on QoL and patient preference must be interpreted with caution.
Quality of Evidence
Overall, the body of evidence was downgraded from high to low due to study quality and issues with directness as identified using the GRADE quality assessment tool (see Table 1) While blinding of patient to intervention/control was not feasible in these studies, blinding of study personnel during outcome assessment and allocation concealment were generally lacking. Trials reported consistent results for the outcomes HbA1c, mean blood glucose and glucose variability, but the directness or generalizability of studies, particularly with respect to the generalizability of the diabetic population, was questionable as most trials used highly motivated populations with fairly good glycemic control. In addition, the populations in each of the studies varied with respect to prior treatment regimens, which may not be generalizable to the population eligible for pumps in Ontario. For the outcome of hypoglycaemic events the evidence was further downgraded to very low since there was conflicting evidence between studies with respect to the frequency of mild and severe hypoglycaemic events in patients using CSII pumps as compared to CSII (see Table 2). The GRADE quality of evidence for the use of CSII in adults with type 1 diabetes is therefore low to very low and any estimate of effect is, therefore, uncertain.
GRADE Quality Assessment for CSII pumps vs. MDI on HbA1c, Mean Blood Glucose, and Glucose Variability for Adults with Type 1 Diabetes
Inadequate or unknown allocation concealment (3/4 studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; No ITT analysis (2/4 studies); possible bias SMBG (all studies)
HbA1c: 3/4 studies show consistency however magnitude of effect varies greatly; Single study uses insulin glargine instead of NPH; Mean Blood Glucose: 3/4 studies show consistency however magnitude of effect varies between studies; Glucose Variability: All studies show consistency but 1 study only showed a significant effect in the morning
Generalizability in question due to varying populations: highly motivated populations, educational component of interventions/ run-in phases, insulin pen use in 2/4 studies and varying levels of baseline glycemic control and experience with intensified insulin therapy, pumps and MDI.
GRADE Quality Assessment for CSII pumps vs. MDI on Frequency of Hypoglycemic
Inadequate or unknown allocation concealment (3/4 studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; No ITT analysis (2/4 studies); possible bias SMBG (all studies)
Conflicting evidence with respect to mild and severe hypoglycemic events reported in studies
Generalizability in question due to varying populations: highly motivated populations, educational component of interventions/ run-in phases, insulin pen use in 2/4 studies and varying levels of baseline glycemic control and experience with intensified insulin therapy, pumps and MDI.
Economic Analysis
One article was included in the analysis from the economic literature scan. Four other economic evaluations were identified but did not meet our inclusion criteria. Two of these articles did not compare CSII with MDI and the other two articles used summary estimates from a mixed population with Type 1 and 2 diabetes in their economic microsimulation to estimate costs and effects over time. Included were English articles that conducted comparisons between CSII and MDI with the outcome of Quality Adjusted Life Years (QALY) in an adult population with type 1 diabetes.
From one study, a subset of the population with type 1 diabetes was identified that may be suitable and benefit from using insulin pumps. There is, however, limited data in the literature addressing the cost-effectiveness of insulin pumps versus MDI in type 1 diabetes. Longer term models are required to estimate the long term costs and effects of pumps compared to MDI in this population.
CSII pumps for the treatment of adults with type 1 diabetes
Based on low-quality evidence, CSII pumps confer a statistically significant but not clinically significant reduction in HbA1c and mean daily blood glucose as compared to MDI in adults with type 1 diabetes (>19 years).
CSII pumps also confer a statistically significant reduction in glucose variability as compared to MDI in adults with type 1 diabetes (>19 years) however the clinical significance is unknown.
There is indirect evidence that the use of newer long-acting insulins (e.g. insulin glargine) in MDI regimens result in less of a difference between MDI and CSII compared to differences between MDI and CSII in which older insulins are used.
There is conflicting evidence regarding both mild and severe hypoglycemic events in this population when using CSII pumps as compared to MDI. These findings are based on very low-quality evidence.
There is an improved quality of life for patients using CSII pumps as compared to MDI however, limitations exist with this evidence.
Significant limitations of the literature exist specifically:
All studies sponsored by insulin pump manufacturers
All studies used crossover design
Prior treatment regimens varied
Types of insulins used in study varied (NPH vs. glargine)
Generalizability of studies in question as populations were highly motivated and half of studies used insulin pens as the mode of delivery for MDI
One short-term study concluded that pumps are cost-effective, although this was based on limited data and longer term models are required to estimate the long-term costs and effects of pumps compared to MDI in adults with type 1 diabetes.
Part B: Type 2 Diabetic Adults
Research Questions
Are CSII pumps more effective than MDI for improving glycemic control in adults (≥19 years) with type 2 diabetes?
Are CSII pumps more effective than MDI for improving other outcomes related to diabetes such as quality of life?
Literature Search
Inclusion Criteria
Randomized controlled trials, systematic reviews, meta-analysis and/or health technology assessments from MEDLINE, Excerpta Medica Database (EMBASE), Cumulative Index to Nursing & Allied Health Literature (CINAHL)
Any person with type 2 diabetes requiring insulin treatment intensive
Published between January 1, 2000 – August 2008
Exclusion Criteria
Studies with <10 patients
Studies <5 weeks in duration
CSII applied only at night time and not 24 hours/day
Mixed group of diabetes patients (children, adults, type 1, type 2)
Pregnancy studies
Outcomes of Interest
The primary outcome of interest was a reduction in glycosylated hemoglobin (HbA1c) levels. Other outcomes of interest were mean blood glucose level, glucose variability, insulin requirements, frequency of hypoglycemic events, adverse events, and quality of life.
Search Strategy
A comprehensive literature search was performed in OVID MEDLINE, MEDLINE In-Process and Other Non-Indexed Citations, EMBASE, CINAHL, The Cochrane Library, and the International Agency for Health Technology Assessment (INAHTA) for studies published between January 1, 2000 and August 15, 2008. Studies meeting the inclusion criteria were selected from the search results. Data on the study characteristics, patient characteristics, primary and secondary treatment outcomes, and adverse events were abstracted. Reference lists of selected articles were also checked for relevant studies. The quality of the evidence was assessed as high, moderate, low, or very low according to the GRADE methodology.
Summary of Findings
The database search identified 286 relevant citations published between 1996 and August 2008. Of the 286 abstracts reviewed, four RCTs met the inclusion criteria outlined above. Upon examination, two studies were subsequently excluded from the meta-analysis due to small sample size and missing data (Berthe et al.), as well as outlier status and high drop out rate (Wainstein et al) which is consistent with previously reported meta-analyses on this topic (Jeitler et al 2008, and Fatourechi M et al. 2009).
The primary outcome in this analysis was reduction in HbA1c. Both studies demonstrated that both CSII pumps and MDI reduce HbA1c, but neither treatment modality was found to be superior to the other. The results of a random effects model meta-analysis showed a mean difference in HbA1c of -0.14 (-0.40, 0.13) between the two groups, which was found not to be statistically or clinically significant. There was no statistical heterogeneity observed between the two studies (I2=0%).
Forrest plot of two parallel, RCTs comparing CSII to MDI in type 2 diabetes
Secondary Outcomes
Mean Blood Glucose and Glucose Variability
Mean blood glucose was only used as an efficacy outcome in one study (Raskin et al. 2003). The authors found that the only time point in which there were consistently lower blood glucose values for the CSII group compared to the MDI group was 90 minutes after breakfast. Glucose variability was not examined in either study and the authors reported no difference in weight gain between the CSII pump group and MDI groups at the end of study. Conflicting results were reported regarding injection site reactions between the two studies. Herman et al. reported no difference in the number of subjects experiencing site problems between the two groups, while Raskin et al. reported that there were no injection site reactions in the MDI group but 15 such episodes among 8 participants in the CSII pump group.
Frequency of Hypoglycemic Events and Insulin Requirements
All studies reported that there were no differences in the number of mild hypoglycemic events in patients on CSII pumps versus MDI. Herman et al. also reported no differences in the number of severe hypoglycemic events in patients using CSII pumps compared to those on MDI. Raskin et al. reported that there were no severe hypoglycemic events in either group throughout the study duration. Insulin requirements were only examined in Herman et al., who found that daily insulin requirements were equal between the CSII pump and MDI treatment groups.
Quality of Life
QoL was measured by Herman et al. using the Diabetes Quality of Life Clinical Trial Questionnaire (DQOLCTQ). There were no differences reported between CSII users and MDI users for treatment satisfaction, diabetes impact, and worry-related scores. Patient satisfaction was measured in Raskin et al. using a patient satisfaction questionnaire, whose results indicated that patients in the CSII pump group had significantly greater improvement in overall treatment satisfaction at the end of the study compared to the MDI group. Although patient preference was also reported, it was only examined in the CSII pump group, thus results indicating a greater preference for CSII pumps in this groups (as compared to prior injectable insulin regimens) are biased and must be interpreted with caution.
Quality of Evidence
Overall, the body of evidence was downgraded from high to low according to study quality and issues with directness as identified using the GRADE quality assessment tool (see Table 3). While blinding of patient to intervention/control is not feasible in these studies, blinding of study personnel during outcome assessment and allocation concealment were generally lacking. ITT was not clearly explained in one study and heterogeneity between study populations was evident from participants’ treatment regimens prior to study initiation. Although trials reported consistent results for HbA1c outcomes, the directness or generalizability of studies, particularly with respect to the generalizability of the diabetic population, was questionable as trials required patients to adhere to an intense SMBG regimen. This suggests that patients were highly motivated. In addition, since prior treatment regimens varied between participants (no requirement for patients to be on MDI), study findings may not be generalizable to the population eligible for a pump in Ontario. The GRADE quality of evidence for the use of CSII in adults with type 2 diabetes is, therefore, low and any estimate of effect is uncertain.
GRADE Quality Assessment for CSII pumps vs. MDI on HbA1c Adults with Type 2 Diabetes
Inadequate or unknown allocation concealment (all studies); Unblinded assessment (all studies) however lack of blinding due to the nature of the study; ITT not well explained in 1 of 2 studies
Indirect due to lack of generalizability of findings since participants varied with respect to prior treatment regimens and intensive SMBG suggests highly motivated populations used in trials.
Economic Analysis
An economic analysis of CSII pumps was carried out using the Ontario Diabetes Economic Model (ODEM) and has been previously described in the report entitled “Application of the Ontario Diabetes Economic Model (ODEM) to Determine the Cost-effectiveness and Budget Impact of Selected Type 2 Diabetes Interventions in Ontario”, part of the diabetes strategy evidence series. Based on the analysis, CSII pumps are not cost-effective for adults with type 2 diabetes, either for the age 65+ sub-group or for all patients in general. Details of the analysis can be found in the full report.
CSII pumps for the treatment of adults with type 2 diabetes
There is low quality evidence demonstrating that the efficacy of CSII pumps is not superior to MDI for adult type 2 diabetics.
There were no differences in the number of mild and severe hypoglycemic events in patients on CSII pumps versus MDI.
There are conflicting findings with respect to an improved quality of life for patients using CSII pumps as compared to MDI.
Significant limitations of the literature exist specifically:
All studies sponsored by insulin pump manufacturers
Prior treatment regimens varied
Types of insulins used in study varied (NPH vs. glargine)
Generalizability of studies in question as populations may not reflect eligible patient population in Ontario (participants not necessarily on MDI prior to study initiation, pen used in one study and frequency of SMBG required during study was high suggesting highly motivated participants)
Based on ODEM, insulin pumps are not cost-effective for adults with type 2 diabetes either for the age 65+ sub-group or for all patients in general.
PMCID: PMC3377523  PMID: 23074525
19.  Routine use of continuous glucose monitoring in 10 501 people with diabetes mellitus 
Diabetic Medicine  2015;32(12):1568-1574.
To analyse blood glucose control according to continuous glucose monitoring use in data from the CareLink™ database, and to identify factors associated with continuation of sensor use during sensor‐augmented pump therapy.
The analysis used data from 10 501 people with Type 1 and 2 diabetes mellitus, of whom 7916 (61.7%) had used glucose sensors for ≥ 15 days during any 6‐month period over a 2‐year observation period. Data were analysed according to the extent of sensor use ( < 25%, 25–49%, 50–74% and ≥ 75% of the time). Time to discontinuation of sensor use was also analysed in new users of glucose sensors.
Compared with patients in the lowest sensor usage group and non‐users, the highest glucose sensor usage group had significantly (P < 0.0001) lower mean blood glucose and blood glucose sd, were more likely to achieve a mean blood glucose concentration < 8.6 mmol/l, (odds ratio 1.5, 95% CI 1.3–1.7; P < 0.0001), and had 50% fewer hypoglycaemic (blood glucose concentration < 2.8 mmol/l) episodes. Among new users, sensor use during the first month of therapy was an important predictor of subsequent discontinuation. Lack of full reimbursement was also significantly associated with early discontinuation, whereas measures of glycaemic control were predictive of discontinuation during long‐term treatment.
The use of continuous glucose monitoring was significantly associated with reductions in hypoglycaemia and improved metabolic control during insulin pump therapy. Sensor use during the first month was strongly associated with long‐term adherence; patient education and training may be helpful in achieving this.
What's new?
The generalizability to routine clinical use of results obtained on continuous glucose monitoring under the controlled conditions pertaining in clinical trials remains to be established.This study, which involved data from > 10 000 people with diabetes, is the largest study to have analysed objective data derived from a self‐uploaded patient electronic database.The analysis showed that the routine home use of continuous glucose monitoring was significantly associated with a reduction in hypoglycaemia and improved metabolic control in people with diabetes receiving insulin pump therapy.
PMCID: PMC4744771  PMID: 26042926
20.  Developing an efficient scheduling template of a chemotherapy treatment unit 
The Australasian Medical Journal  2011;4(10):575-588.
This study was undertaken to improve the performance of a Chemotherapy Treatment Unit by increasing the throughput and reducing the average patient’s waiting time. In order to achieve this objective, a scheduling template has been built. The scheduling template is a simple tool that can be used to schedule patients' arrival to the clinic. A simulation model of this system was built and several scenarios, that target match the arrival pattern of the patients and resources availability, were designed and evaluated. After performing detailed analysis, one scenario provide the best system’s performance. A scheduling template has been developed based on this scenario. After implementing the new scheduling template, 22.5% more patients can be served.
CancerCare Manitoba is a provincially mandated cancer care agency. It is dedicated to provide quality care to those who have been diagnosed and are living with cancer. MacCharles Chemotherapy unit is specially built to provide chemotherapy treatment to the cancer patients of Winnipeg. In order to maintain an excellent service, it tries to ensure that patients get their treatment in a timely manner. It is challenging to maintain that goal because of the lack of a proper roster, the workload distribution and inefficient resource allotment. In order to maintain the satisfaction of the patients and the healthcare providers, by serving the maximum number of patients in a timely manner, it is necessary to develop an efficient scheduling template that matches the required demand with the availability of resources. This goal can be reached using simulation modelling. Simulation has proven to be an excellent modelling tool. It can be defined as building computer models that represent real world or hypothetical systems, and hence experimenting with these models to study system behaviour under different scenarios.1, 2
A study was undertaken at the Children's Hospital of Eastern Ontario to identify the issues behind the long waiting time of a emergency room.3 A 20-­‐day field observation revealed that the availability of the staff physician and interaction affects the patient wait time. Jyväskylä et al.4 used simulation to test different process scenarios, allocate resources and perform activity-­‐based cost analysis in the Emergency Department (ED) at the Central Hospital. The simulation also supported the study of a new operational method, named "triage-team" method without interrupting the main system. The proposed triage team method categorises the entire patient according to the urgency to see the doctor and allows the patient to complete the necessary test before being seen by the doctor for the first time. The simulation study showed that it will decrease the throughput time of the patient and reduce the utilisation of the specialist and enable the ordering all the tests the patient needs right after arrival, thus quickening the referral to treatment.
Santibáñez et al.5 developed a discrete event simulation model of British Columbia Cancer Agency"s ambulatory care unit which was used to study the impact of scenarios considering different operational factors (delay in starting clinic), appointment schedule (appointment order, appointment adjustment, add-­‐ons to the schedule) and resource allocation. It was found that the best outcomes were obtained when not one but multiple changes were implemented simultaneously. Sepúlveda et al.6 studied the M. D. Anderson Cancer Centre Orlando, which is a cancer treatment facility and built a simulation model to analyse and improve flow process and increase capacity in the main facility. Different scenarios were considered like, transferring laboratory and pharmacy areas, adding an extra blood draw room and applying different scheduling techniques of patients. The study shows that by increasing the number of short-­‐term (four hours or less) patients in the morning could increase chair utilisation.
Discrete event simulation also helps improve a service where staff are ignorant about the behaviour of the system as a whole; which can also be described as a real professional system. Niranjon et al.7 used simulation successfully where they had to face such constraints and lack of accessible data. Carlos et al. 8 used Total quality management and simulation – animation to improve the quality of the emergency room. Simulation was used to cover the key point of the emergency room and animation was used to indicate the areas of opportunity required. This study revealed that a long waiting time, overload personnel and increasing withdrawal rate of patients are caused by the lack of capacity in the emergency room.
Baesler et al.9 developed a methodology for a cancer treatment facility to find stochastically a global optimum point for the control variables. A simulation model generated the output using a goal programming framework for all the objectives involved in the analysis. Later a genetic algorithm was responsible for performing the search for an improved solution. The control variables that were considered in this research are number of treatment chairs, number of drawing blood nurses, laboratory personnel, and pharmacy personnel. Guo et al. 10 presented a simulation framework considering demand for appointment, patient flow logic, distribution of resources, scheduling rules followed by the scheduler. The objective of the study was to develop a scheduling rule which will ensure that 95% of all the appointment requests should be seen within one week after the request is made to increase the level of patient satisfaction and balance the schedule of each doctor to maintain a fine harmony between "busy clinic" and "quiet clinic".
Huschka et al.11 studied a healthcare system which was about to change their facility layout. In this case a simulation model study helped them to design a new healthcare practice by evaluating the change in layout before implementation. Historical data like the arrival rate of the patients, number of patients visited each day, patient flow logic, was used to build the current system model. Later, different scenarios were designed which measured the changes in the current layout and performance.
Wijewickrama et al.12 developed a simulation model to evaluate appointment schedule (AS) for second time consultations and patient appointment sequence (PSEQ) in a multi-­‐facility system. Five different appointment rule (ARULE) were considered: i) Baily; ii) 3Baily; iii) Individual (Ind); iv) two patients at a time (2AtaTime); v) Variable Interval and (V-­‐I) rule. PSEQ is based on type of patients: Appointment patients (APs) and new patients (NPs). The different PSEQ that were studied in this study were: i) first-­‐ come first-­‐serve; ii) appointment patient at the beginning of the clinic (APBEG); iii) new patient at the beginning of the clinic (NPBEG); iv) assigning appointed and new patients in an alternating manner (ALTER); v) assigning a new patient after every five-­‐appointment patients. Also patient no show (0% and 5%) and patient punctuality (PUNCT) (on-­‐time and 10 minutes early) were also considered. The study found that ALTER-­‐Ind. and ALTER5-­‐Ind. performed best on 0% NOSHOW, on-­‐time PUNCT and 5% NOSHOW, on-­‐time PUNCT situation to reduce WT and IT per patient. As NOSHOW created slack time for waiting patients, their WT tends to reduce while IT increases due to unexpected cancellation. Earliness increases congestion whichin turn increases waiting time.
Ramis et al.13 conducted a study of a Medical Imaging Center (MIC) to build a simulation model which was used to improve the patient journey through an imaging centre by reducing the wait time and making better use of the resources. The simulation model also used a Graphic User Interface (GUI) to provide the parameters of the centre, such as arrival rates, distances, processing times, resources and schedule. The simulation was used to measure the waiting time of the patients in different case scenarios. The study found that assigning a common function to the resource personnel could improve the waiting time of the patients.
The objective of this study is to develop an efficient scheduling template that maximises the number of served patients and minimises the average patient's waiting time at the given resources availability. To accomplish this objective, we will build a simulation model which mimics the working conditions of the clinic. Then we will suggest different scenarios of matching the arrival pattern of the patients with the availability of the resources. Full experiments will be performed to evaluate these scenarios. Hence, a simple and practical scheduling template will be built based on the indentified best scenario. The developed simulation model is described in section 2, which consists of a description of the treatment room, and a description of the types of patients and treatment durations. In section 3, different improvement scenarios are described and their analysis is presented in section 4. Section 5 illustrates a scheduling template based on one of the improvement scenarios. Finally, the conclusion and future direction of our work is exhibited in section 6.
Simulation Model
A simulation model represents the actual system and assists in visualising and evaluating the performance of the system under different scenarios without interrupting the actual system. Building a proper simulation model of a system consists of the following steps.
Observing the system to understand the flow of the entities, key players, availability of resources and overall generic framework.
Collecting the data on the number and type of entities, time consumed by the entities at each step of their journey, and availability of resources.
After building the simulation model it is necessary to confirm that the model is valid. This can be done by confirming that each entity flows as it is supposed to and the statistical data generated by the simulation model is similar to the collected data.
Figure 1 shows the patient flow process in the treatment room. On the patient's first appointment, the oncologist comes up with the treatment plan. The treatment time varies according to the patient’s condition, which may be 1 hour to 10 hours. Based on the type of the treatment, the physician or the clinical clerk books an available treatment chair for that time period.
On the day of the appointment, the patient will wait until the booked chair is free. When the chair is free a nurse from that station comes to the patient, verifies the name and date of birth and takes the patient to a treatment chair. Afterwards, the nurse flushes the chemotherapy drug line to the patient's body which takes about five minutes and sets up the treatment. Then the nurse leaves to serve another patient. Chemotherapy treatment lengths vary from less than an hour to 10 hour infusions. At the end of the treatment, the nurse returns, removes the line and notifies the patient about the next appointment date and time which also takes about five minutes. Most of the patients visit the clinic to take care of their PICC line (a peripherally inserted central catheter). A PICC is a line that is used to inject the patient with the chemical. This PICC line should be regularly cleaned, flushed to maintain patency and the insertion site checked for signs of infection. It takes approximately 10–15 minutes to take care of a PICC line by a nurse.
Cancer Care Manitoba provided access to the electronic scheduling system, also known as "ARIA" which is comprehensive information and image management system that aggregates patient data into a fully-­‐electronic medical chart, provided by VARIAN Medical System. This system was used to find out how many patients are booked in every clinic day. It also reveals which chair is used for how many hours. It was necessary to search a patient's history to find out how long the patient spends on which chair. Collecting the snapshot of each patient gives the complete picture of a one day clinic schedule.
The treatment room consists of the following two main limited resources:
Treatment Chairs: Chairs that are used to seat the patients during the treatment.
Nurses: Nurses are required to inject the treatment line into the patient and remove it at the end of the treatment. They also take care of the patients when they feel uncomfortable.
Mc Charles Chemotherapy unit consists of 11 nurses, and 5 stations with the following description:
Station 1: Station 1 has six chairs (numbered 1 to 6) and two nurses. The two nurses work from 8:00 to 16:00.
Station 2: Station 2 has six chairs (7 to 12) and three nurses. Two nurses work from 8:00 to 16:00 and one nurse works from 12:00 to 20:00.
Station 3: Station 4 has six chairs (13 to 18) and two nurses. The two nurses work from 8:00 to 16:00.
Station 4: Station 4 has six chairs (19 to 24) and three nurses. One nurse works from 8:00 to 16:00. Another nurse works from 10:00 to 18:00.
Solarium Station: Solarium Station has six chairs (Solarium Stretcher 1, Solarium Stretcher 2, Isolation, Isolation emergency, Fire Place 1, Fire Place 2). There is only one nurse assigned to this station that works from 12:00 to 20:00. The nurses from other stations can help when need arises.
There is one more nurse known as the "float nurse" who works from 11:00 to 19:00. This nurse can work at any station. Table 1 summarises the working hours of chairs and nurses. All treatment stations start at 8:00 and continue until the assigned nurse for that station completes her shift.
Currently, the clinic uses a scheduling template to assign the patients' appointments. But due to high demand of patient appointment it is not followed any more. We believe that this template can be improved based on the availability of nurses and chairs. Clinic workload was collected from 21 days of field observation. The current scheduling template has 10 types of appointment time slot: 15-­‐minute, 1-­‐hour, 1.5-­‐hour, 2-­‐hour, 3-­‐hour, 4-­‐hour, 5-­‐hour, 6-­‐hour, 8-­‐hour and 10-­‐hour and it is designed to serve 95 patients. But when the scheduling template was compared with the 21 days observations, it was found that the clinic is serving more patients than it is designed for. Therefore, the providers do not usually follow the scheduling template. Indeed they very often break the time slots to accommodate slots that do not exist in the template. Hence, we find that some of the stations are very busy (mostly station 2) and others are underused. If the scheduling template can be improved, it will be possible to bring more patients to the clinic and reduce their waiting time without adding more resources.
In order to build or develop a simulation model of the existing system, it is necessary to collect the following data:
Types of treatment durations.
Numbers of patients in each treatment type.
Arrival pattern of the patients.
Steps that the patients have to go through in their treatment journey and required time of each step.
Using the observations of 2,155 patients over 21 days of historical data, the types of treatment durations and the number of patients in each type were estimated. This data also assisted in determining the arrival rate and the frequency distribution of the patients. The patients were categorised into six types. The percentage of these types and their associated service times distributions are determined too.
ARENA Rockwell Simulation Software (v13) was used to build the simulation model. Entities of the model were tracked to verify that the patients move as intended. The model was run for 30 replications and statistical data was collected to validate the model. The total number of patients that go though the model was compared with the actual number of served patients during the 21 days of observations.
Improvement Scenarios
After verifying and validating the simulation model, different scenarios were designed and analysed to identify the best scenario that can handle more patients and reduces the average patient's waiting time. Based on the clinic observation and discussion with the healthcare providers, the following constraints have been stated:
The stations are filled up with treatment chairs. Therefore, it is literally impossible to fit any more chairs in the clinic. Moreover, the stakeholders are not interested in adding extra chairs.
The stakeholders and the caregivers are not interested in changing the layout of the treatment room.
Given these constraints the options that can be considered to design alternative scenarios are:
Changing the arrival pattern of the patients: that will fit over the nurses' availability.
Changing the nurses' schedule.
Adding one full time nurse at different starting times of the day.
Figure 2 compares the available number of nurses and the number of patients' arrival during different hours of a day. It can be noticed that there is a rapid growth in the arrival of patients (from 13 to 17) between 8:00 to 10:00 even though the clinic has the equal number of nurses during this time period. At 12:00 there is a sudden drop of patient arrival even though there are more available nurses. It is clear that there is an imbalance in the number of available nurses and the number of patient arrivals over different hours of the day. Consequently, balancing the demand (arrival rate of patients) and resources (available number of nurses) will reduce the patients' waiting time and increases the number of served patients. The alternative scenarios that satisfy the above three constraints are listed in Table 2. These scenarios respect the following rules:
Long treatments (between 4hr to 11hr) have to be scheduled early in the morning to avoid working overtime.
Patients of type 1 (15 minutes to 1hr treatment) are the most common. They can be fitted in at any time of the day because they take short treatment time. Hence, it is recommended to bring these patients in at the middle of the day when there are more nurses.
Nurses get tired at the end of the clinic day. Therefore, fewer patients should be scheduled at the late hours of the day.
In Scenario 1, the arrival pattern of the patient was changed so that it can fit with the nurse schedule. This arrival pattern is shown Table 3. Figure 3 shows the new patients' arrival pattern compared with the current arrival pattern. Similar patterns can be developed for the remaining scenarios too.
Analysis of Results
ARENA Rockwell Simulation software (v13) was used to develop the simulation model. There is no warm-­‐up period because the model simulates day-­‐to-­‐day scenarios. The patients of any day are supposed to be served in the same day. The model was run for 30 days (replications) and statistical data was collected to evaluate each scenario. Tables 4 and 5 show the detailed comparison of the system performance between the current scenario and Scenario 1. The results are quite interesting. The average throughput rate of the system has increased from 103 to 125 patients per day. The maximum throughput rate can reach 135 patients. Although the average waiting time has increased, the utilisation of the treatment station has increased by 15.6%. Similar analysis has been performed for the rest of the other scenarios. Due to the space limitation the detailed results are not given. However, Table 6 exhibits a summary of the results and comparison between the different scenarios. Scenario 1 was able to significantly increase the throughput of the system (by 21%) while it still results in an acceptable low average waiting time (13.4 minutes). In addition, it is worth noting that adding a nurse (Scenarios 3, 4, and 5) does not significantly reduce the average wait time or increase the system's throughput. The reason behind this is that when all the chairs are busy, the nurses have to wait until some patients finish the treatment. As a consequence, the other patients have to wait for the commencement of their treatment too. Therefore, hiring a nurse, without adding more chairs, will not reduce the waiting time or increase the throughput of the system. In this case, the only way to increase the throughput of the system is by adjusting the arrival pattern of patients over the nurses' schedule.
Developing a Scheduling Template based on Scenario 1
Scenario 1 provides the best performance. However a scheduling template is necessary for the care provider to book the patients. Therefore, a brief description is provided below on how scheduling the template is developed based on this scenario.
Table 3 gives the number of patients that arrive hourly, following Scenario 1. The distribution of each type of patient is shown in Table 7. This distribution is based on the percentage of each type of patient from the collected data. For example, in between 8:00-­‐9:00, 12 patients will come where 54.85% are of Type 1, 34.55% are of Type 2, 15.163% are of Type 3, 4.32% are of Type 4, 2.58% are of Type 5 and the rest are of Type 6. It is worth noting that, we assume that the patients of each type arrive as a group at the beginning of the hourly time slot. For example, all of the six patients of Type 1 from 8:00 to 9:00 time slot arrive at 8:00.
The numbers of patients from each type is distributed in such a way that it respects all the constraints described in Section 1.3. Most of the patients of the clinic are from type 1, 2 and 3 and they take less amount of treatment time compared with the patients of other types. Therefore, they are distributed all over the day. Patients of type 4, 5 and 6 take a longer treatment time. Hence, they are scheduled at the beginning of the day to avoid overtime. Because patients of type 4, 5 and 6 come at the beginning of the day, most of type 1 and 2 patients come at mid-­‐day (12:00 to 16:00). Another reason to make the treatment room more crowded in between 12:00 to 16:00 is because the clinic has the maximum number of nurses during this time period. Nurses become tired at the end of the clinic which is a reason not to schedule any patient after 19:00.
Based on the patient arrival schedule and nurse availability a scheduling template is built and shown in Figure 4. In order to build the template, if a nurse is available and there are patients waiting for service, a priority list of these patients will be developed. They are prioritised in a descending order based on their estimated slack time and secondarily based on the shortest service time. The secondary rule is used to break the tie if two patients have the same slack. The slack time is calculated using the following equation:
Slack time = Due time - (Arrival time + Treatment time)
Due time is the clinic closing time. To explain how the process works, assume at hour 8:00 (in between 8:00 to 8:15) two patients in station 1 (one 8-­‐hour and one 15-­‐ minute patient), two patients in station 2 (two 12-­‐hour patients), two patients in station 3 (one 2-­‐hour and one 15-­‐ minute patient) and one patient in station 4 (one 3-­‐hour patient) in total seven patients are scheduled. According to Figure 2, there are seven nurses who are available at 8:00 and it takes 15 minutes to set-­‐up a patient. Therefore, it is not possible to schedule more than seven patients in between 8:00 to 8:15 and the current scheduling is also serving seven patients by this time. The rest of the template can be justified similarly.
PMCID: PMC3562880  PMID: 23386870
21.  Impact of improved glycaemic control on rates of hypoglycaemia in insulin dependent diabetes mellitus 
Archives of Disease in Childhood  1998;78(2):111-115.
Increased emphasis on strict glycaemic control of insulin dependent diabetes mellitus (IDDM) in young patients may be expected to cause increases in rates of significant hypoglycaemia. To evaluate whether this is the case for a large population based sample of IDDM children and adolescents rates of severe (coma, convulsion) and moderate (requiring assistance for treatment) hypoglycaemia were studied prospectively over a four year period.
 A total of 709 patients were studied yielding 2027 patient years of data (mean (SD) age: 12.3 (4.4); range 0-18 years, duration IDDM: 4.9 (3.8) years). Details of hypoglycaemia were recorded at clinic visits every three months when glycated haemoglobin (HbA1c) was also measured.
Overall the incidence of severe hypoglycaemia was 7.8 and moderate was 15.4 episodes/100 patient years. Over the four years mean (SD) clinic HbA1c steadily fell from 10.2 (1.6)% in 1992to 8.8 (1.5)% in 1995. In parallel with this there was a dramatic increase in the rate of hypoglycaemia, especially in the fourth year of the study, when severe hypoglycaemia increased from 4.8to 15.6 episodes/100 patient years. This increase was particularly marked in younger children (<6 years) in whom severe hypoglycaemia increased from 14.9 to 42.1 episodes/100 patient years in 1995.
It is concluded that attempts to achieve improved metabolic control must be accompanied by efforts to minimise the effects of significant hypoglycaemia, particularly in the younger age group.

PMCID: PMC1717459  PMID: 9579150
22.  Strict glycaemic control in patients hospitalised in a mixed medical and surgical intensive care unit: a randomised clinical trial 
Critical Care  2008;12(5):R120.
Critically ill patients can develop hyperglycaemia even if they do not have diabetes. Intensive insulin therapy decreases morbidity and mortality rates in patients in a surgical intensive care unit (ICU) and decreases morbidity in patients in a medical ICU. The effect of this therapy on patients in a mixed medical/surgical ICU is unknown. Our goal was to assess whether the effect of intensive insulin therapy, compared with standard therapy, decreases morbidity and mortality in patients hospitalised in a mixed ICU.
This is a prospective, randomised, non-blinded, single-centre clinical trial in a medical/surgical ICU. Patients were randomly assigned to receive either intensive insulin therapy to maintain glucose levels between 80 and 110 mg/dl (4.4 to 6.1 mmol/l) or standard insulin therapy to maintain glucose levels between 180 and 200 mg/dl (10 and 11.1 mmol/l). The primary end point was mortality at 28 days.
Over a period of 30 months, 504 patients were enrolled. The 28-day mortality rate was 32.4% (81 of 250) in the standard insulin therapy group and 36.6% (93 of 254) in the intensive insulin therapy group (Relative Risk [RR]: 1.1; 95% confidence interval [CI]: 0.85 to 1.42). The ICU mortality in the standard insulin therapy group was 31.2% (78 of 250) and 33.1% (84 of 254) in the intensive insulin therapy group (RR: 1.06; 95%CI: 0.82 to 1.36). There was no statistically significant reduction in the rate of ICU-acquired infections: 33.2% in the standard insulin therapy group compared with 27.17% in the intensive insulin therapy group (RR: 0.82; 95%CI: 0.63 to 1.07). The rate of hypoglycaemia (≤ 40 mg/dl) was 1.7% in the standard insulin therapy group and 8.5% in the intensive insulin therapy group (RR: 5.04; 95% CI: 1.20 to 21.12).
IIT used to maintain glucose levels within normal limits did not reduce morbidity or mortality of patients admitted to a mixed medical/surgical ICU. Furthermore, this therapy increased the risk of hypoglycaemia.
Trial Registration Identifiers: 4374-04-13031; 094-2 in 000966421
PMCID: PMC2592751  PMID: 18799004
23.  Implementation and evaluation of a nurse-centered computerized potassium regulation protocol in the intensive care unit - a before and after analysis 
Potassium disorders can cause major complications and must be avoided in critically ill patients. Regulation of potassium in the intensive care unit (ICU) requires potassium administration with frequent blood potassium measurements and subsequent adjustments of the amount of potassium administrated. The use of a potassium replacement protocol can improve potassium regulation. For safety and efficiency, computerized protocols appear to be superior over paper protocols. The aim of this study was to evaluate if a computerized potassium regulation protocol in the ICU improved potassium regulation.
In our surgical ICU (12 beds) and cardiothoracic ICU (14 beds) at a tertiary academic center, we implemented a nurse-centered computerized potassium protocol integrated with the pre-existent glucose control program called GRIP (Glucose Regulation in Intensive Care patients). Before implementation of the computerized protocol, potassium replacement was physician-driven. Potassium was delivered continuously either by central venous catheter or by gastric, duodenal or jejunal tube. After every potassium measurement, nurses received a recommendation for the potassium administration rate and the time to the next measurement. In this before-after study we evaluated potassium regulation with GRIP. The attitude of the nursing staff towards potassium regulation with computer support was measured with questionnaires.
The patient cohort consisted of 775 patients before and 1435 after the implementation of computerized potassium control. The number of patients with hypokalemia (<3.5 mmol/L) and hyperkalemia (>5.0 mmol/L) were recorded, as well as the time course of potassium levels after ICU admission. The incidence of hypokalemia and hyperkalemia was calculated. Median potassium-levels were similar in both study periods, but the level of potassium control improved: the incidence of hypokalemia decreased from 2.4% to 1.7% (P < 0.001) and hyperkalemia from 7.4% to 4.8% (P < 0.001). Nurses indicated that they considered computerized potassium control an improvement over previous practice.
Computerized potassium control, integrated with the nurse-centered GRIP program for glucose regulation, is effective and reduces the prevalence of hypo- and hyperkalemia in the ICU compared with physician-driven potassium regulation.
PMCID: PMC2826292  PMID: 20100342
24.  The impact of the severity of sepsis on the risk of hypoglycaemia and glycaemic variability 
Critical Care  2008;12(5):R129.
The purpose of this study was to assess the relation between glycaemic control and the severity of sepsis in a cohort of patients treated with intensive insulin therapy (IIT).
In a prospective, observational study, all patients in the intensive care unit (ICU) (n = 191) with sepsis, severe sepsis or septic shock were treated with IIT (target blood glucose (BG) level 80 to 140 mg/dl instead of strict normoglycaemia). BG values were analysed by calculating mean values, rate of BG values within different ranges, rate of patients experiencing BG values within different levels and standard deviation (SD) of BG values as an index of glycaemic variability.
The number of patients with hypoglycaemia and hyperglycaemia was highly dependent on the severity of sepsis (critical hypoglycaemia ≤ 40 mg/dl: sepsis: 2.1%, severe sepsis: 6.0%, septic shock: 11.5%, p = 0.1497; hyperglycaemia: >140 mg/dl: sepsis: 76.6%, severe sepsis: 88.0%, septic shock: 100%, p = 0.0006; >179 mg/dl: sepsis: 55.3%, severe sepsis: 73.5%, septic shock: 88.5%, p = 0.0005; >240 mg/dl: sepsis: 17.0%, severe sepsis: 48.2%, septic shock: 45.9%, p = 0.0011). Multivariate analyses showed a significant association of SD levels with critical hypoglycaemia especially for patients in septic shock (p = 0.0197). In addition, SD levels above 20 mg/dl were associated with a significantly higher mortality rate relative to those with SD levels below 20 mg/dl (24% versus 2.5%, p = 0.0195).
Patients with severe sepsis and septic shock who were given IIT had a high risk of hypoglycaemia and hyperglycaemia. Among these patients even with a higher target BG level, IIT mandates an increased awareness of the occurrence of critical hypoglycaemia, which is related to the severity of the septic episode.
PMCID: PMC2592768  PMID: 18939991
25.  Conventional insulin vs insulin infusion therapy in acute coronary syndrome diabetic patients 
World Journal of Diabetes  2014;5(4):562-568.
AIM: To evaluate the impact on glucose variability (GLUCV) of an nurse-implemented insulin infusion protocol when compared with a conventional insulin treatment during the day-to-day clinical activity.
METHODS: We enrolled 44 type 2 diabetic patients (n = 32 males; n = 12 females) with acute coronary syndrome (ACS) and randomy assigned to standard a subcutaneous insulin treatment (n = 23) or a nurse-implemented continuous intravenous insulin infusion protocol (n = 21). We utilized some parameters of GLUCV representing well-known surrogate markers of prognosis, i.e., glucose standard deviation (SD), the mean daily δ glucose (mean of daily difference between maximum and minimum glucose), and the coefficient of variation (CV) of glucose, expressed as percent glucose (SD)/glucose (mean).
RESULTS: At the admission, first fasting blood glucose, pharmacological treatments (insulin and/or anti-diabetic drugs) prior to entering the study and basal glycated hemoglobin (HbA1c) were observed in the two groups treated with subcutaneous or intravenous insulin infusion, respectively. When compared with patients submitted to standard therapy, insulin-infused patients showed both increased first 24-h (median 6.9 mmol/L vs 5.7 mmol/L P < 0.045) and overall hospitalization δ glucose (median 10.9 mmol/L vs 9.3 mmol/L, P < 0.028), with a tendency to a significant increase in first 24-h glycaemic CV (23.1% vs 19.6%, P < 0.053). Severe hypoglycaemia was rare (14.3%), and it was observed only in 3 patients receiving insulin infusion therapy. HbA1c values measured during hospitalization and 3 mo after discharge did not differ in the two groups of treatment.
CONCLUSION: Our pilot data suggest that no real benefit in terms of GLUCV is observed when routinely managing blood glucose by insulin infusion therapy in type 2 diabetic ACS hospitalized patients in respect to conventional insulin treatment
PMCID: PMC4127591  PMID: 25126402
Glycaemic management; Intensive insulin therapy; Conventional insulin treatment; Acute coronay syndrome; Glucose variability

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