Insulin sensitivity in critically ill patients: are women more insulin resistant?

Glucose management in critically ill adults: A qualitative study from the experiences of health care providers

Aims and objective

To explain the components and elements of glucose management in critically ill adult patients from the healthcare providers' experiences.

Background

Critically ill adults are highly susceptible to stress-induced hyperglycaemia due to glucose metabolic disorders. Healthcare workers play a key role in the glycaemic management of critically ill patients. However, there is a lack of qualitative studies on the content and elements of glycaemic management and healthcare workers' perceptions about glycaemic management in China.

Design

Qualitative study that followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines.

Methods

Individual semi-structured interviews were conducted from January to April 2022. Fifteen physicians and nurses were recruited from ten hospitals in mainland China. Data were analysed using inductive thematic analysis.

Results

Glucose management in critically ill adult patients from their experiences included two parts: the inner ring (practice behaviours) and the external space (methods and drivers). The practice behaviours of glucose management include five elements, while the methods and drivers of glucose management focus on three elements. The content covered under each element was identified.

Conclusion

This study developed a glycaemic management model for critically ill adult patients, clarified its elements based on the perceptions of healthcare providers and elaborated on the methods and drivers covered under each element to provide a reference for physicians and nurses to develop a comprehensive glycaemic management guideline for critically ill adult patients.

Relevance to clinical practice

Our study proposed a glucose management practice model for critically ill adult patients, and the elements and components included in this model can provide a reference for physicians and nurses when performing glucose management in critically ill patients.

Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance

BACKGROUND

Critically ill ICU patients frequently experience acute insulin resistance and increased endogenous glucose production, manifesting as stress-induced hyperglycemia and hyperinsulinemia. STAR (Stochastic TARgeted) is a glycemic control protocol, which directly manages inter- and intra- patient variability using model-based insulin sensitivity (SI). The model behind STAR assumes a population constant for endogenous glucose production (EGP), which is not otherwise identifiable.

OBJECTIVE

This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.

METHODS

Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.

RESULTS

Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.

CONCLUSIONS

Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.

Variability in Estimated Modelled Insulin Secretion

BACKGROUND

The ability to measure insulin secretion from pancreatic beta cells and monitor glucose-insulin physiology is vital to current health needs. C-peptide has been used successfully as a surrogate for plasma insulin concentration. Quantifying the expected variability of modelled insulin secretion will improve confidence in model estimates.

METHODS

Forty-three healthy adult males of Māori or Pacific peoples ancestry living in New Zealand participated in an frequently sampled, intravenous glucose tolerance test (FS-IVGTT) with an average age of 29 years and a BMI of 33 kg/m². A 2-compartment model framework and standardized kinetic parameters were used to estimate endogenous pancreatic insulin secretion from plasma C-peptide measurements. Monte Carlo analysis (N = 10 000) was then used to independently vary parameters within ±2 standard deviations of the mean of each variable and the 5th and 95th percentiles determined the bounds of the expected range of insulin secretion. Cumulative distribution functions (CDFs) were calculated for each subject for area under the curve (AUC) total, AUC Phase 1, and AUC Phase 2. Normalizing each AUC by the participant's median value over all N = 10 000 iterations quantifies the expected model-based variability in AUC.

RESULTS

Larger variation is found in subjects with a BMI > 30 kg/m², where the interquartile range is 34.3% compared to subjects with a BMI ≤ 30 kg/m² where the interquartile range is 24.7%.

CONCLUSIONS

Use of C-peptide measurements using a 2-compartment model and standardized kinetic parameters, one can expect ~±15% variation in modelled insulin secretion estimates. The variation should be considered when applying this insulin secretion estimation method to clinical diagnostic thresholds and interpretation of model-based analyses such as insulin sensitivity.

Nutritional therapy in critically ill patients with diabetes

PURPOSE OF REVIEW

There has been a significant increase in nutrition therapy related studies within the critical care cohort in recent years. Management of patients with both diabetes and stress hyperglycaemia through targeted nutrition interventions is no exception. The aim of this review is to outline current available diabetes specific nutrition formula, its impact on gastric emptying and subsequently glycaemic control as well as explore recent literature on the efficacy of utilizing nutrition support to optimize glycaemic control in critically ill patients.

RECENT FINDINGS

Studies explored within this review were similar in terms of outcomes measures, focusing primarily on insulin use and glycaemic control. Although there were promising results in terms of the impact of diabetes-specific nutrition formula on these outcome measures, there were no significant associations with clinical outcomes.

SUMMARY

The use of diabetes-specific formulae in critically ill patients with pre-existing diabetes and stress hyperglycaemia can be considered a logical approach to minimize the risks associated with high doses of insulin. Additional research is required to address the effects of these formulae on the dysglycaemia, nursing workload, safety of glycaemic control and cost-effectiveness.

Sex-Specific Aspects of Skeletal Muscle Metabolism in the Clinical Context of Intensive Care Unit-Acquired Weakness

(1) Background: Female sex is considered a risk factor for Intensive Care Unit-Acquired Weakness (ICUAW). The aim is to investigate sex-specific aspects of skeletal muscle metabolism in the context of ICUAW. (2) Methods: This is a sex-specific sub-analysis from two prospectively conducted trials examining skeletal muscle metabolism and advanced muscle activating measures in critical illness. Muscle strength was assessed by Medical Research Council Score. The insulin sensitivity index was analyzed by hyperinsulinemic-euglycemic (HE) clamp. Muscular metabolites were studied by microdialysis. M. vastus lateralis biopsies were taken. The molecular analysis included protein degradation pathways. Morphology was assessed by myocyte cross-sectional area (MCSA). Multivariable linear regression models for the effect of sex on outcome parameters were performed. (3) Results: n = 83 (♂n = 57, 68.7%; ♀n = 26, 31.3%) ICU patients were included. ICUAW was present in 81.1%♂ and in 82.4%♀ at first awakening (p = 0.911) and in 59.5%♂ and in 70.6%♀ at ICU discharge (p = 0.432). Insulin sensitivity index was reduced more in women than in men (p = 0.026). Sex was significantly associated with insulin sensitivity index and MCSA of Type IIa fibers in the adjusted regression models. (4) Conclusion: This hypothesis-generating analysis suggests that more pronounced impairments in insulin sensitivity and lower MCSA of Type IIa fibers in critically ill women may be relevant for sex differences in ICUAW.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

Insulin Resistance and Homeostatic Model Assessment in Critically Ill: Where do We Stand?

How to cite this article: Shah JN. Insulin Resistance and Homeostatic Model Assessment in Critically Ill: Where do We Stand? Indian J Crit Care Med 2021;25(12):1335-1336.

The goldilocks problem: Nutrition and its impact on glycaemic control

BACKGROUND

Glucose intolerance and insulin resistance manifest as hyperglycaemia in intensive care, which is associated with mortality and morbidities. Glycaemic control (GC) may improve outcomes, though safe and effective control has proven elusive. Nutritional glucose intake affects blood glucose (BG) outcomes, but few protocols actively control it. This study aims to examine BG outcomes in the context of nutritional management during GC.

METHODS

Retrospective cohort analysis of 5 glycaemic control cohorts spanning 4 years (n = 273) from Christchurch Hospital Intensive Care Unit (ICU). GC is delivered using a single model-based protocol (STAR), with default 4.4-8.0 mmol/L target range via. modulation of insulin and nutrition. Clinical adaptations/cohorts include variations on upper target (UL-9 with 9.0 mmol/L, reducing workload and nutrition responsiveness), and insulin only (IO) with clinically set nutrition at 3 glucose concentrations (71 g/L vs. 120 and 180 g/L in the TARGET study).

RESULTS

Percent of BG hours in the 4.4-8.0 mmol/L range highest under standard STAR conditions (78%), and was lower at 64% under UL-9, likely due to reduced time-responsiveness of nutrition-insulin changes. By comparison, IO only resulted in 64-69% BG in range across different nutrition types. A subset of patients receiving high glucose nutrition under IO were persistently hyperglycaemic, indicating patient-specific glucose tolerance.

CONCLUSION

STAR GC is most effective when nutrition and insulin are modulated together with timely responsiveness to persistent hyperglycaemia. Results imply modulation of nutrition alongside insulin improves GC, particularly in patients with persistent hyperglycaemia/low glucose tolerance.