Glucose control positively influences patient outcome: a retrospective study

The goal of this research is to demonstrate that well-regulated glycemia is beneficial to patient outcome, regardless of how it is achieved

Impact of glucocorticoids on insulin resistance in the critically ill

Glucocorticoids (GCs) have been shown to reduce insulin sensitivity in healthy individuals. Widely used in critical care to treat a variety of inflammatory and allergic disorders, they may inadvertently exacerbate stress-hyperglycaemia. This research uses model-based methods to quantify the reduction of insulin sensitivity from GCs in critically ill patients, and thus their impact on glycaemic control. A clinically validated model-based measure of insulin sensitivity (SI) was used to quantify changes between two matched cohorts of 40 intensive care unit (ICU) patients who received GCs and a control cohort who did not. All patients were admitted to the Christchurch hospital ICU between 2005 and 2007 and spent at least 24 hours on the SPRINT glycaemic control protocol. A 31% reduction in whole-cohort median insulin sensitivity was seen between the control cohort and patients receiving glucocorticoids with a median dose equivalent to 200mg/day of hydrocortisone per patient. Comparing percentile-patients as a surrogate for matched patients, reductions in median insulin sensitivity of 20, 25, and 21% were observed for the 25th, 50th and 75th-percentile patients. All these cohort and per-patient reductions are less than or equivalent to the 30-62% reductions reported in healthy subjects especially when considering the fact that the GC doses in this study are 1.3-4 times larger than those in studies of healthy subjects. This reduced suppression of insulin sensitivity in critically ill patients could be a result of saturation due to already increased levels of catecholamines and cortisol common in critically illness. Virtual trial simulation showed that reductions in insulin sensitivity of 20-30% associated with glucocorticoid treatment in the ICU have limited impact on glycaemic control levels within the context of the SPRINT protocol

Glargine as a Basal Insulin Supplement in Recovering Critically Ill Patients - An In Silico Study

Tight glycaemic control is now benefiting medical and surgical intensive care patients by reducing complications associated with hyperglycaemia. Once patients leave this intensive care environment, less acute wards do not continue to provide the same level of glycaemic control. Main reason is that these less acute wards do not have the high levels of nursing resources to provide the same level of glycaemic control. Therefore developments in protocols that are less labour intensive are necessary. This study examines the use of insulin glargine for basal supplement in recovering critically ill patients. These patients represent a group who may benefit from such basal support therapy. In silico study results showed the potential in reducing nursing effort with the use of glargine. However, a protocol using only glargine for glucose control did not show to be effective in the simulated patients. This may be an indication that a protocol using only glargine is more suitable after discharge from critical care

Predicting fluid-response, the heart of hemodynamic management: A model-based solution

peer reviewedBackground: Intravenous fluid infusions are an important therapy for patients with circulatory shock. However, it is challenging to predict how patients’ cardiac stroke volume (SV) will respond, and thus identify how much fluids should be delivered, if any. Model-predicted SV time-profiles of response to fluid infusions could potentially be used to guide fluid therapy. Method: A clinically applicable model-based method predicts SV changes in response to fluid-infusions for a pig trial (N = 6). Validation/calibration SV, SVmea, is from an aortic flow probe. Model parameters are identified in 3 ways: fitting to SVmea from the entire infusion, SVflfit, from the first 200 ml, SVfl200, or from the first 100 ml, SVfl100. RMSE compares error of model-based SV time-profiles for each parameter identification method, and polar plot analysis assesses trending ability. Receiver-operating characteristic (ROC) analysis evaluates ability of model-predicted SVs, SVfl200 and SVfl100, to distinguish non-responsive and responsive infusions, using area-under the curve (AUC), and balanced accuracy as a measure of performance. Results: RMSE for SVflFit, SVfl200, and SVfl100 was 1.8, 3.2, and 6.5 ml, respectively, and polar plot angular limit of agreement from was 11.6, 28.0, and 68.8°, respectively. For predicting responsive and non-responsive interventions SVfl200, and SVfl100 had ROC AUC of 0.64 and 0.69, respectively, and balanced accuracy was 0.75 in both cases. Conclusions: The model-predicted SV time-profiles matched measured SV trends well for SVflFit, SVfl200, but not SVfl100. Thus, the model can fit the observed SV dynamics, and can deliver good SV prediction given a sufficient parameter identification period. This trial is limited by small numbers and provides proof-of-method, with further experimental and clinical investigation needed. Potentially, this method could deliver model-predicted SV time-profiles to guide fluid therapy decisions, or as part of a closed-loop fluid control system. © 2021 Elsevier Lt

Modeled Insulin Sensitivity and Interstitial Insulin Action from a Pilot Study of Dynamic Insulin Sensitivity Tests

An accurate test for insulin resistance can delay or prevent the development of Type 2 diabetes and its complications. The current gold standard test, CLAMP, is too labor intensive to be used in general practice. A recently developed dynamic insulin sensitivity test, DIST, uses a glucose-insulin-C-peptide model to calculate model-based insulin sensitivity, SI. Preliminary results show good correlation to CLAMP. However both CLAMP and DIST ignore saturation in insulin-mediated glucose removal. This study uses the data from 17 patients who underwent multiple DISTs to investigate interstitial insulin action and its influence on modeled insulin sensitivity. The critical parameters influencing interstitial insulin action are saturation in insulin receptor binding, αG, and plasma-interstitial difiusion rate, nI . Very low values of αG and very low values of nI produced the most intra-patient variability in SI. Repeatability in SI is enhanced with modeled insulin receptor saturation. Future parameter study on subjects with varying degree of insulin resistance may provide a better understanding of different contributing factors of insulin resistance

Endogenous insulin secretion in critically ill patients

1-pageGlucose-insulin system models can be used for improved glycemic control of critically ill patients. A key component of glucose-insulin models is pancreatic insulin secretion. There is limited data in the literature quantifying insulin secretion in critically ill patients at physiologic levels. This study presents a model pancreatic insulin secretion in critically ill patients based on data from a critically ill population

Tube-load model: A clinically applicable pulse contour analysis method for estimation of cardiac stroke volume

peer reviewedBackground and Objectives: Accurate, reproducible, and reliable real-time clinical measurement of stroke volume (SV) is challenging. To accurately estimate arterial mechanics and SV by pulse contour analysis, accounting for wave reflection, such as by a tube-load model, is potentially important. This study tests for the first time whether a dynamically identified tube-load model, given a single peripheral arterial input signal and pulse transit time (PTT), provides accurate SV estimates during hemodynamic instability. Methods: The model is tested for 5 pigs during hemodynamic interventions, using either an aortic flow probe or admittance catheter for a validation SV measure. Performance is assessed using Bland-Altman and polar plot analysis for a series of long-term state-change and short-term dynamic events. Results:The overall median bias and limits of agreement (2.5th, 97.5th percentile) from Bland-Altman analysis were -10% [-49, 36], and -1% [-28,20] for state-change and dynamic events, respectively. The angular limit of agreement (maximum of 2.5th, 97.5th percentile) from polar-plot analysis for state-change and dynamic interventions was 35.6∘, and 35.2∘, respectively. Conclusion: SV estimation agreement and trending performance was reasonable given the severity of the interventions. This simple yet robust method has potential to track SV within acceptable limits during hemodynamic instability in critically ill patients, provided a sufficiently accurate PTT measure. © 2021 Elsevier B.V

Pilot Proof of Concept Clinical Trials of Stochastic Targeted (STAR) Glycemic Control

(open access)Introduction: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials. Methods: Seven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay ≥3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. Results: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. Conclusions: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT

Why Protocolised care works in my unit

peer reviewe

Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction

Monitoring patient-specific respiratory mechanics can be used to guide mechanical ventilation (MV) therapy in critically ill patients. However, many patients can exhibit spontaneous breathing (SB) efforts during ventilator supported breaths, altering airway pressure waveforms and hindering model-based (or other) identification of the true, underlying respiratory mechanics necessary to guide MV. This study aims to accurately assess respiratory mechanics for breathing cycles masked by SB efforts. A cumulative pressure reconstruction method is used to ameliorate SB by identifying SB affected waveforms and reconstructing unaffected pressure waveforms for respiratory mechanics identification using a single-compartment model. Performance is compared to conventional identification without reconstruction, where identified values from reconstructed waveforms should be less variable. Results are validated with 9485 breaths affected by SB, including periods of muscle paralysis that eliminates SB, as a validation test set where reconstruction should have no effect. In this analysis, the patients are their own control, with versus without reconstruction, as assessed by breath-to-breath variation using the non-parametric coefficient of variation (CV) of respiratory mechanics. Pressure reconstruction successfully estimates more consistent respiratory mechanics. CV of estimated respiratory elastance is reduced up to 78% compared to conventional identification (p < 0.05). Pressure reconstruction is comparable (p > 0.05) to conventional identification during paralysis, and generally performs better as paralysis weakens, validating the algorithm’s purpose. Pressure reconstruction provides less-affected pressure waveforms, ameliorating the effect of SB, resulting in more accurate respiratory mechanics identification. Thus providing the opportunity to use respiratory mechanics to guide mechanical ventilation without additional muscle relaxants, simplifying clinical care and reducing risk