Hyper-insulinemia euglycemia therapy (HIET) is a supra-physiological insulin dosing protocol used in acute cardiac failure to reduce dependency on inotropes to augment or generate cardiac output, and ...is based on the inotropic effects of insulin at high doses up to 45-250x normal daily dose. Such high insulin doses are managed using intravenous glucose infusion to control glycemia and prevent hypoglycemia. However, both insulin dosing and glycemic control in these patients is managed ad-hoc. This research examines a selection of clinical data to determine the effect of high insulin dosing on renal clearance and insulin sensitivity, to assess the feasibility of using model-based methods to control and guide these protocols. The results show that the model and, in particular, the modeled renal clearance constant are adequate and capture measured data well, although not perfectly. Equally, insulin sensitivity over time is similar to broader critical care cohorts in level and variability, and these results are the first time they have been presented for this cohort. While more data is needed to confirm and further specify these results, it is clear that the model used is adequate for controlling HIET in a model-based framework.
Tight glycemic control (TGC) remains controversial while successful, consistent, and effective protocols remain elusive. This research analyzes data from two TGC trials for root causes of the ...differences achieved in control and thus potentially in glycemic and other outcomes. The goal is to uncover aspects of successful TGC and delineate the impact of differences in cohorts.
A retrospective analysis was conducted using records from a 211-patient subset of the GluControl trial taken in Liege, Belgium, and 393 patients from Specialized Relative Insulin Nutrition Titration (SPRINT) in New Zealand. Specialized Relative Insulin Nutrition Titration targeted 4.0-6.0 mmol/liter, similar to the GluControl A (N = 142) target of 4.4-6.1 mmol/liter. The GluControl B (N = 69) target was 7.8-10.0 mmol/liter. Cohorts were matched by Acute Physiology and Chronic Health Evaluation II score and percentage males (p > .35); however, the GluControl cohort was slightly older (p = .011). Overall cohort and per-patient comparisons (median, interquartile range) are shown for (a) glycemic levels achieved, (b) nutrition from carbohydrate (all sources), and (c) insulin dosing for this analysis. Intra- and interpatient variability were examined using clinically validated model-based insulin sensitivity metric and its hour-to-hour variation.
Cohort blood glucose were as follows: SPRINT, 5.7 (5.0-6.6) mmol/liter; GluControl A, 6.3 (5.3-7.6) mmol/liter; and GluControl B, 8.2 (6.9-9.4) mmol/liter. Insulin dosing was 3.0 (1.0-3.0), 1.5 (0.5-3), and 0.7 (0.0-1.7) U/h, respectively. Nutrition from carbohydrate (all sources) was 435.5 (259.2-539.1), 311.0 (0.0-933.1), and 622.1 (103.7-1036.8) kcal/day, respectively. Median per-patient results for blood glucose were 5.8 (5.3-6.4), 6.4 (5.9-6.9), and 8.3 (7.6-8.8) mmol/liter. Insulin doses were 3.0 (2.0-3.0), 1.5 (0.8-2.0), and 0.5 (0.0-1.0) U/h. Carbohydrate administration was 383.6 (207.4-497.7), 103.7 (0.0-829.4), and 207.4 (0.0-725.8) kcal/day. Overall, SPRINT gave approximately 2x more insulin with a 3-4x narrower, but generally non-zero, range of nutritional input to achieve equally TGC with less hypoglycemia. Specialized Relative Insulin Nutrition Titration had much less hypoglycemia (<2.2 mmol/liter), with 2% of patients, compared to GluControl A (7.7%) and GluControl B (2.9%), indicating much lower variability, with similar results for glucose levels <3.0 mmol/liter. Specialized Relative Insulin Nutrition Titration also had less hyperglycemia (>8.0 mmol/liter) than groups A and B. GluControl patients (A+B) had a approximately 2x wider range of insulin sensitivity than SPRINT. Hour-to-hour variation was similar. Hence GluControl had greater interpatient variability but similar intrapatient variability.
Protocols that dose insulin blind to carbohydrate administration can suffer greater outcome glycemic variability, even if average cohort glycemic targets are met. While the cohorts varied significantly in model-assessed insulin resistance, their variability was similar. Such significant intra- and interpatient variability is a further significant cause and marker of glycemic variability in TGC. The results strongly recommended that TGC protocols be explicitly designed to account for significant intra- and interpatient variability in insulin resistance, as well as specifying or having knowledge of carbohydrate administration to minimize variability in glycemic outcomes across diverse cohorts and/or centers.
Abstract A model-based insulin sensitivity parameter ( SI ) is often used in glucose–insulin system models to define the glycaemic response to insulin. As a parameter identified from clinical data, ...insulin sensitivity can be affected by blood glucose (BG) sensor error and measurement timing error, which can subsequently impact analyses or glycaemic variability during control. This study assessed the impact of both measurement timing and BG sensor errors on identified values of SI and its hour-to-hour variability within a common type of glucose–insulin system model. Retrospective clinical data were used from 270 patients admitted to the Christchurch Hospital ICU between 2005 and 2007 to identify insulin sensitivity profiles. We developed error models for the Abbott Optium Xceed glucometer and measurement timing from clinical data. The effect of these errors on the re-identified insulin sensitivity was investigated by Monte–Carlo analysis. The results of the study show that timing errors in isolation have little clinically significant impact on identified SI level or variability. The clinical impact of changes to SI level induced by combined sensor and timing errors is likely to be significant during glycaemic control. Identified values of SI were mostly (90th percentile) within 29% of the true value when influenced by both sources of error. However, these effects may be overshadowed by physiological factors arising from the critical condition of the patients or other under-modelled or un-modelled dynamics. Thus, glycaemic control protocols that are designed to work with data from glucometers need to be robust to these errors and not be too aggressive in dosing insulin.
To evaluate the costs of using dextrose gel as a primary treatment for neonatal hypoglycemia in the first 48 hours after birth compared with standard care.
We used a decision tree to model overall ...costs, including those specific to hypoglycemia monitoring and treatment and those related to the infant's length of stay in the postnatal ward or neonatal intensive care unit, comparing the use of dextrose gel for treatment of neonatal hypoglycemia with placebo, using data from the Sugar Babies randomized trial. Sensitivity analyses assessed the impact of dextrose gel cost, neonatal intensive care cost, cesarean delivery rate, and costs of glucose monitoring.
In the primary analysis, treating neonatal hypoglycemia using dextrose gel had an overall cost of NZ$6863.81 and standard care (placebo) cost NZ$8178.25; a saving of NZ$1314.44 per infant treated. Sensitivity analyses showed that dextrose gel remained cost saving with wide variations in dextrose gel costs, neonatal intensive care unit costs, cesarean delivery rates, and costs of monitoring.
Use of buccal dextrose gel reduces hospital costs for management of neonatal hypoglycemia. Because it is also noninvasive, well tolerated, safe, and associated with improved breastfeeding, buccal dextrose gel should be routinely used for initial treatment of neonatal hypoglycemia.
Australian New Zealand Clinical Trials Registry: ACTRN12608000623392.
Glargine and Glycemia: Pitfalls and Perils Fisk, Liam M.; Willis, Jonathan G.; Le Compte, Aaron J. ...
IFAC Proceedings Volumes,
2012, 2012-00-00, Letnik:
45, Številka:
18
Journal Article
Odprti dostop
Type 1 diabetics exhibit an unfulfilled basal insulin requirement, currently treated with long-acting subcutaneous insulins such as glargine. Due to glagine's unique flat peak the drug is an ideal ...basal insulin replacement. Use of the drug has extended beyond patients with diabetes, seeing use in critical care when patients are deemed stable but still require exogenous insulin. Data from four patients in the pilot trial of STAR in Christchurch hospital was gathered to outline serious considerations when using glargine in an ICU setting.
The patients were fitted with the ICING and Glargine Compartment models to identify time-varying insulin sensitivity (SI), which was plotted alongside the blood glucose (BG) trace, interstitial insulin compartment and insulin/nutrition inputs. Features of these profiles were then identified to elaborate on the risks associated with the use of a long-acting insulin analogue.
Importance of nutrition on patient safety, uncertainty in inter- and intra- patient variability in response to glargine doses, and time-scales of changes in patient condition were all highlighted from the four cases. The extended time-scale of physiological responses to glargine can put patients at risk of severe hypoglycemia if: A) metabolic condition changes dramatically within this period; or B) clinical limitations on nutrition are imposed after a dose is administered.
Although use of glargine has the potential to cater for patients with a basal insulin requirement and who have less requirement for intensive supervision, more research should be done into action of the drug in an ICU cohort before use becomes widespread.
Nutrition is an important factor in the treatment of patients in critical care. Potential hyper-rmetabolism means underfeeding may cause malnourishment, while overfeeding increases risk of ...hyperglycemia and the associated physiological impact. Hyperglycemia can be treated through accurate glycemic control (AGC), and this paper examines the link between nutrition and achievement of AGC. Clinically validated virtual trials were carried out on the 371 patients in the SPRINT cohort using STAR, an adaptive AGC protocol targeting 80-145mg/dL. Nutrition results were compared to the rates given clinically to investigate the effect modulating nutrition has on the final level of nutrition administered. The effect of clinical nutrition stoppages on this level of nutrition was also isolated. The link between nutrition and the ability to achieve AGC was investigated by targeting STAR to both 80-145mg/dL and 140-180mg/dL, allowing STAR to modulate nutrition as well as delivering constant rates of 60%, 80%, 100%, 120% and 140% ACCP goal. Performance was assessed as %BG within the target range, hyperglycemia as %BG above the range and clinical workload as the number of BG measurements. Relative tightness was estimated using BG IQR. As expected, modulating nutrition led to a range of total nutrition delivered to patients. Importantly, low nutrition administration corresponded to low insulin sensitivity, and clinical nutrition stoppages were shown to drop median nutrition rates by 10% over the first 4 days in ICU, suggesting a significant effect if a nutrition target is desired. Variable nutrition in STAR was shown to lead to reduced BG variability and clinical workload, and different nutrition rates showed significant differences in BG outcomes despite the adaptive STAR framework. Combined, these results show that AGC could be better achieved with less effort if variable nutrition was permitted. In part, this effect is due to constant nutrition restricting the ability of a protocol to respond to low insulin sensitivity. Constant nutrition will also have a strong effect on the ability to target a specific range.
Critically ill patients often experience high levels of insulin resistance and stress-induced hyperglycemia, which may negatively impact outcomes. In 2001, Van den Berghe and coauthors used intensive ...insulin therapy (IIT) to control blood glucose (BG) to normal levels and reported a reduction in intensive care unit (ICU) mortality from 8% to 4.6%. Many studies tried to replicate these results, with some showing reduced mortality, others failing to match these results, and many seeing no clinically significant difference. The interpretation of results is important when drawing conclusions about the benefits and risks of IIT. There is the potential for negative results to be falsely negative due to unintended patient crossover or cohort overlap.
The aim of this study was to investigate the association between the amount of time each critically ill patient experiences good glucose control and hospital mortality.
This study uses BG data from 784 patients admitted to the Christchurch Hospital ICU between January 2003 and May 2007. For each of the 5 days of analysis, all patients with BG data were pooled together in a single cohort before being stratified into two subcohorts based on glycemic performance, determined by cumulative time in band (cTIB). The cTIB metric is calculated per patient/per day and defined here as the percentage of time the patient's BG levels have been cumulatively in a specific band (72-126 mg/dl) up to and including the considered day. Subcohort A had patients with cTIB ≥ threshold and subcohort B had patients with cTIB < threshold. Three cTIB thresholds were tested: 0.3 (30%), 0.5 (50%), and 0.7 (70%). The odds of living (OL) were then calculated for each subcohort and day, forming the basis of comparison between the subcohorts. A second analysis was run using only the 310 patients with BG data for 5 days or more to assess the impact of patient dropout.
Results show that, across all three cTIB threshold levels (0.3, 0.5, and 0.7) and all 5 days of analysis, patients with a cTIB ≥ threshold have a higher OL than patients with a cTIB < threshold. A cTIB threshold of 0.7 showed the strongest separation between the subcohorts, and on day 5, the OL for subcohort A was 4.4 versus 1.6 for subcohort B. The second analysis showed that patient dropout had little effect on the overall trends. Using a cTIB threshold of 0.7, the OL for subcohort A was 0.8 higher than the OL for subcohort B on day 1, which steadily increased over the 5 days of analysis.
Results show that OL are higher for patients with cTIB ≥ 0.3-0.7 than patients with cTIB < 0.3-0.7, irrespective of how cTIB was achieved. A cTIB threshold of 0.5 was found to be a minimum acceptable threshold based on outcome. If cTIB is used in similar BG studies in the future, cTIB ≥ 0.7 may be a good target for glycemic control to ensure outcomes and to separate patients with good BG control from patients with poor control.
Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is assessed to evaluate its efficacy as a diagnostic ...indicator. Statistical analyses investigate the SOFA score distributions in the days leading up to patient mortality and patient discharge. It is found that the SOFA score is not an effective predictor of patient mortality, but it is a useful tool for prediction of patient discharge from the Intensive Care Unit (ICU). The distribution of overall SOFA score was observed not to change notably in the days leading up to patient death. However, the SOFA score distribution was observed to have a trend towards lower SOFA scores in the days leading up to patient discharge. Finally, assessment of the individual components of the overall SOFA score indicated that the coagulation and cardiovascular scores showed the highest correlation to mortality and are therefore the most useful individual groups to be used as diagnostic indicators.
Premature neonates often experience hyperglycemia, which has been linked to worsened outcomes. Insulin therapy can assist in controlling blood glucose (BG) levels. However, a reliable, robust control ...protocol is required to avoid hypoglycemia and to ensure that clinically important nutrition goals are met.
This study presents an adaptive, model-based predictive controller designed to incorporate the unique metabolic state of the neonate. Controller performance was tested and refined in virtual trials on a 25-patient retrospective cohort. The effects of measurement frequency and BG sensor error were evaluated. A stochastic model of insulin sensitivity was used in control to provide a guaranteed maximum 4% risk of BG < 72 mg/dl to protect against hypoglycemia as well as account for patient variability over 1-3 h intervals when determining the intervention. The resulting controller is demonstrated in two 24 h clinical neonatal pilot trials at Christchurch Women's Hospital.
Time in the 72-126 mg/dl BG band was increased by 103-161% compared to retrospective clinical control for virtual trials of the controller, with fewer hypoglycemic measurements. Controllers were robust to BG sensor errors. The model-based controller maintained glycemia to a tight target control range and accounted for interpatient variability in patient glycemic response despite using more insulin than the retrospective case, illustrating a further measure of controller robustness. Pilot clinical trials demonstrated initial safety and efficacy of the control method.
A controller was developed that made optimum use of the very limited available BG measurements in the neonatal intensive care unit and provided robustness against BG sensor error and longer BG measurement intervals. It used more insulin than typical sliding scale approaches or retrospective hospital control. The potential advantages of a model-based approach demonstrated in simulation were applied to initial clinical trials.
Accurate glycemic control (AGC) is difficult due to excessive hypoglycemia risk. Stochastic TARgeted (STAR) glycemic control forecasts changes in insulin sensitivity to calculate a range of glycemic ...outcomes for an insulin intervention, creating a risk framework to improve safety and performance. An improved, simplified STAR framework was developed to reduce light hypoglycemia and clinical effort, while improving nutrition rates and performance. Blood glucose (BG) levels are targeted to 80 – 145mg/dL, using insulin and nutrition control for 1-3 hour interventions. Insulin changes are limited to +3U/hour and nutrition to ±30% of goal rate (minimum 30%). All targets and rate change limits are clinically specified and generalizable. Clinically validated virtual trials were run using clinical data from 371 patients (39,841hours) from the SPRINT cohort. Cohort and per-patient results are compared to clinical SPRINT data. Performance was measured as time within glycemic bands, and safety by patients with severe (BG<40mg/dL) and mild (%BG<72mg/dL) hypoglycemia. Pilot trial results from the first 10 patients (1,458 hours) are included to support the in-silico findings. In both virtual and clinical trials, mild hypoglycemia was below 2% versus 4% for SPRINT. Severe hypoglycemia was reduced from 14 (SPRINT) to 6 (STAR), and 0 in the pilot trial. BG results tighter than SPRINT clinical data, with 91.6% BG within the specified target (80–145mg/dL) in virtual trials and 89.4% in pilot trials. Clinical effort (measurements) was reduced from 16.1/day to 11.8/day (13.5/day in pilot trials). This STAR framework provides safe, accurate glycemic control with significant reductions in hypoglycemia and clinical effort due to stochastic forecasting of patient variation – a unique risk-based approach. Initial pilot trials validate the in silico design methods and resulting protocol, all of which can be generalized to suit any given clinical environment.
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