• Glycaemic control
• Hyperglycaemia
• Insulin therapy
• Intensive care
• STAR
• Virtual trial
• Workload

• Humans
• Glycemic Control/methods
• Computer Simulation
• Blood Glucose/analysis
• Retrospective Studies
• Insulin
• Intensive Care Units
• Male
• Female
• Time Factors
• Middle Aged

• Confidence interval
• Insulin sensitivity
• Multi-mode distribution
• Network configuration
• Quantile regression

• Glucose control
• Hyperglycemia
• Hyperglycemia tolerance
• Insulin therapy

• Humans
• Critical Illness/mortality
• Retrospective Studies
• Male
• Female
• Blood Glucose/analysis
• Middle Aged
• Aged
• Intensive Care Units
• Hypoglycemia/mortality
• Time Factors
• Hyperglycemia/mortality
• Hyperglycemia/blood
• Diabetes Mellitus/blood
• Diabetes Mellitus/mortality
• Glycemic Control

• Acute pancreatitis
• Blood glucose time in range
• Diabetes
• Glycosylated hemoglobin
• Severity

• Blood glucose
• Continuous glucose monitoring
• Critical care
• Glycaemic indices
• Hypoglycaemia
• Intensive care unit

• EGP
• STAR
• blood glucose
• endogenous glucose production
• glycemic control
• insulin sensitivity
• model-based

• Blood Glucose/analysis
• Critical Care/methods
• Critical Illness
• Glucose
• Humans
• Hyperglycemia
• Insulin
• Insulin Resistance
• Intensive Care Units

• Critical care
• Critical illness
• Hyperglycemia
• Hypoglycemia
• Insulin
• Nutrition

• Adult
• Blood Glucose
• Critical Illness/therapy
• Humans
• Hyperglycemia/diagnosis
• Hyperglycemia/drug therapy
• Hyperglycemia/prevention & control
• Hypoglycemia/chemically induced
• Hypoglycemic Agents/therapeutic use
• Infusions, Intravenous
• Insulin/adverse effects
• Intensive Care Units
• Retrospective Studies

• EGP
• critical illness
• endogenous glucose production
• glycemic control
• hyperglycemia
• insulin resistance
• insulin sensitivity
• physiological stress

• Blood Glucose
• Critical Illness
• Glucose
• Humans
• Hyperglycemia
• Insulin
• Insulin Resistance

• MedTech Core
• New Zealand National Science for Technology & Innovation Challenge

While time in targeted blood glucose range (TIR) 70–140 mg/dL is a known factor associated with mortality in critically ill patients, it remains unclear whether TIR is associated with 28-day mortality under the glycemic control with a less tight target glucose range of 70–180 mg/dL. We aimed to examine whether TIR 70–180 mg/dL was associated with 28-day mortality.

This is a retrospective cohort study using data from a tertiary care center in Japan collected from January 2016 through October 2019. We included adult patients (aged ≥20 years) admitted to the ICU. We excluded patients 1) with diabetic ketoacidosis patients, 2) discharged within 48 hours, 3) with repeated ICU admissions. We calculated TIR 70–180 mg/dL using the measured blood glucose values (≥3 times per day). The primary outcome was 28-day mortality. We examined the association between TIR and 28-day mortality using a logistic regression and Cox proportional hazard models with a stratification by glycosylated hemoglobin (HbA1c) level of 6.5%. Additionally, we repeated the analyses using the TIR category to assess the optimal TIR. For the sensitivity analysis, we repeated the primary analysis using TIR during the first three days from ICU admission.

Of 1,230 patients, the median age was 72 years, 65% were male, and 250 patients (20%) had HbA1c ≥6.5% on admission. In patients with HbA1c <6.5%, TIR <80% was associated with an increased risk of 28-day mortality, with an adjusted odds ratio (OR) of 1.88 (95%CI: 1.36–2.61). Likewise, when using 10% incremental TIR as a categorical variable, lower TIR was associated with a worse 28-day mortality compared with TIR ≥90% (e.g., adjusted OR of TIR <60%, 3.62 [95%CI 2.36–5.53]). Similar associations were found in the analyses using Cox proportional hazards model and using TIR during the first three days. By contrast, in patients with HbA1c ≥6.5%, there was no consistent association of TIR with 28-day mortality.

We found that lower TIR 70–180 mg/dL was associated with a higher 28-day mortality in critically ill patients with HbA1c <6.5%, whereas there was no consistent association in patients with HbA1c ≥6.5%.

• Glucose tolerance
• Glycemic control
• Insulin therapy
• Intensive care
• Nutrition management

• Cardiac surgical procedures
• Critical care nursing
• Glycaemia control
• Healthcare technology
• SpaceGlucose control

• cost-benefit analysis
• decision-making
• risk
• risk-based assessment
• risk-based protocol

Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.

Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.

Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.

Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.

• Glycaemic control
• Hyperglycaemia
• Hypoglycaemia
• Insulin resistance
• Insulin sensitivity
• Intensive care
• Metabolic variability

• Glycaemic control
• Hyperglycaemia
• Hypoglycaemia
• Insulin resistance
• Insulin sensitivity
• Intensive care
• Metabolic variability

• blood glucose
• clinical protocols
• intensive care units
• nurses

This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage.

STAR proposes 1–3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed.

The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance.

The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.

• compliance
• diabetes
• glycemic control
• model-based control
• stochastic targeted prediction

STAR is a model-based, personalised, risk-based dosing approach for glycaemic control (GC) in critically ill patients. STAR provides safe, effective control to nearly all patients, using 1–3 hourly measurement and intervention intervals. However, the average 11–12 measurements per day required can be a clinical burden in many intensive care units. This study aims to significantly reduce workload by extending STAR 1–3 hourly intervals to 1 to 4-, 5-, and 6-hourly intervals, and evaluate the impact of these longer intervals on GC safety and efficacy, using validated in silico virtual patients and trials methods. A Standard STAR approach was used which allowed more hyperglycaemia over extended intervals, and a STAR Upper Limit Controlled approach limited nutrition to mitigate hyperglycaemia over longer intervention intervals.

Extending STAR from 1–3 hourly to 1–6 hourly provided high safety and efficacy for nearly all patients in both approaches. For STAR Standard, virtual trial results showed lower % blood glucose (BG) in the safe 4.4–8.0 mmol/L target band (from 83 to 80%) as treatment intervals increased. Longer intervals resulted in increased risks of hyper- (15% to 18% BG > 8.0 mmol/L) and hypo- (2.1% to 2.8% of patients with min. BG < 2.2 mmol/L) glycaemia. These results were achieved with slightly reduced insulin (3.2 [2.0 5.0] to 2.5 [1.5 3.0] U/h) and nutrition (100 [85 100] to 90 [75 100] % goal feed) rates, but most importantly, with significantly reduced workload (12 to 8 measurements per day). The STAR Upper Limit Controlled approach mitigated hyperglycaemia and had lower insulin and significantly lower nutrition administration rates.

The modest increased risk of hyper- and hypo-glycaemia, and the reduction in nutrition delivery associated with longer treatment intervals represent a significant risk and reward trade-off in GC. However, STAR still provided highly safe, effective control for nearly all patients regardless of treatment intervals and approach, showing this unique risk-based dosing approach, modulating both insulin and nutrition, to be robust in its design. Clinical pilot trials using STAR with different measurement timeframes should be undertaken to confirm these results clinically.

• Blood glucose
• Glycaemic control
• Hyperglycaemia
• Insulin resistance
• Insulin sensitivity
• Insulin therapy
• Trade-off
• Workload

• Blood Glucose
• Critical Illness
• Humans
• Hyperglycemia
• Hypoglycemia
• Hypoglycemic Agents
• Insulin
• Retrospective Studies

The challenges of glycaemic control in critically ill patients have been debated for 20 years. While glycaemic control shows benefits inter- and intra-patient metabolic variability results in increased hypoglycaemia and glycaemic variability, both increasing morbidity and mortality. Hence, current recommendations for glycaemic control target higher glycaemic ranges, guided by the fear of harm. Lately, studies have proven the ability to provide safe, effective control for lower, normoglycaemic, ranges, using model-based computerised methods. Such methods usually identify patient-specific physiological parameters to personalize titration of insulin and/or nutrition. The Stochastic-Targeted (STAR) glycaemic control framework uses patient-specific insulin sensitivity and a stochastic model of its future variability to directly account for both inter- and intra-patient variability in a risk-based insulin-dosing approach.

In this study, a more personalized and specific 3D version of the stochastic model used in STAR is compared to the current 2D stochastic model, both built using kernel-density estimation methods. Fivefold cross validation on 681 retrospective patient glycaemic control episodes, totalling over 65,000 h of control, is used to determine whether the 3D model better captures metabolic variability, and the potential gain in glycaemic outcome is assessed using validated virtual trials. Results show that the 3D stochastic model has similar forward predictive power, but provides significantly tighter, more patient-specific, prediction ranges, showing the 2D model over-conservative > 70% of the time. Virtual trial results show that overall glycaemic safety and performance are similar, but the 3D stochastic model reduced median blood glucose levels (6.3 [5.7, 7.0] vs. 6.2 [5.6, 6.9]) with a higher 61% vs. 56% of blood glucose within the 4.4–6.5 mmol/L range.

This improved performance is achieved with higher insulin rates and higher carbohydrate intake, but no loss in safety from hypoglycaemia. Thus, the 3D stochastic model developed better characterises patient-specific future insulin sensitivity dynamics, resulting in improved simulated glycaemic outcomes and a greater level of personalization in control. The results justify inclusion into ongoing clinical use of STAR.

• Blood glucose
• Glycaemic control
• Hyperglycaemia
• Insulin
• Insulin sensitivity
• Kernel density

• critical care
• healthcare survey
• hyperglycaemia
• hypoglycaemia

• Clinical workload
• Critical care
• Glycaemic control
• Hyperglycaemia
• Hypoglycaemia
• Intensive care unit
• Model-based
• Nutrition delivery
• Stochastic
• Targeted

• Adult
• Aged
• Aged, 80 and over
• Blood Glucose/analysis
• Computer Simulation
• Critical Care/methods
• Critical Illness/therapy
• Female
• Humans
• Hypoglycemia/prevention & control
• Hypoglycemia/therapy
• Hypoglycemic Agents/administration & dosage
• Insulin/chemistry
• Intensive Care Units
• Male
• Middle Aged
• Nutritional Sciences/methods
• Software
• Workload

• critical care
• critical illness
• enteral nutrition
• feeding protocols
• glycemic control

There is considerable physiological and clinical evidence of harm and increased risk of death associated with dysglycemia in critical care. However, glycemic control (GC) currently leads to increased hypoglycemia, independently associated with a greater risk of death. Indeed, recent evidence suggests GC is difficult to safely and effectively achieve for all patients. In this review, leading experts in the field discuss this evidence and relevant data in diabetology, including the artificial pancreas, and suggest how safe, effective GC can be achieved in critically ill patients in ways seeking to mimic normal islet cell function. The review is structured around the specific clinical hurdles of: understanding the patient’s metabolic state; designing GC to fit clinical practice, safety, efficacy, and workload; and the need for standardized metrics. These aspects are addressed by reviewing relevant recent advances in science and technology. Finally, we provide a set of concise recommendations to advance the safety, quality, consistency, and clinical uptake of GC in critical care. This review thus presents a roadmap toward better, more personalized metabolic care and improved patient outcomes.

• Artificial pancreas
• Endocrine function
• Glycemic control
• In silico
• Model based
• Modeling
• Review
• Validation
• Virtual patient

Dysglycemia in critically ill patients (hyperglycemia, hypoglycemia, glycemic variability and time in range) is a biomarker of disease severity and is associated with higher mortality. However, this impact appears to be weakened in patients with previous diabetes mellitus, particularly in those with poor premorbid glycemic control; this phenomenon has been called "diabetes paradox". This phenomenon determines that glycated hemoglobin (HbA1c) values should be considered in choosing glycemic control protocols on admission to an intensive care unit and that patients' target blood glucose ranges should be adjusted according to their HbA1c values. Therefore, HbA1c emerges as a simple tool that allows information that has therapeutic utility and prognostic value to be obtained in the intensive care unit.

• Cohort Studies
• Computer Simulation
• Critical Care/methods
• Humans
• Models, Biological
• Physiological Phenomena
• Precision Medicine/methods

• JCF-15-UOC-013 Royal Society of New Zealand
• CRS-S3-2016 New Zealand National Science Challenge

• blood glucose
• continuous glucose monitoring
• glycemic control
• modeling
• sensor
• simulation

• Blood Glucose/analysis
• Computer Simulation
• Humans
• Hypoglycemia/blood
• Hypoglycemia/diagnosis
• Intensive Care Units
• Monitoring, Physiologic

• Continuous glucose monitoring
• Hyperinsulinaemia
• Interstitial glucose
• Neonatal hyperglycaemia
• Neonatal hypoglycaemia

• glycaemic control
• insulin sensitivity
• model-based control
• physiological modelling
• premature infant
• stochastic forecasting

Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided.

Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant.

SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed.

Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition.

The online version of this article (doi:10.1186/s13054-017-1725-y) contains supplementary material, which is available to authorized users.

• Blood glucose
• Critical care
• Glycaemic control
• Hyperglycaemia
• Insulin
• Insulin sensitivity
• Variability

• Aged
• Blood Glucose/analysis
• Blood Glucose/physiology
• Computer Simulation
• Critical Illness
• Databases, Factual
• Female
• Humans
• Hyperglycemia/drug therapy
• Hyperglycemia/physiopathology
• Hyperglycemia/prevention & control
• Insulin/administration & dosage
• Insulin/pharmacokinetics
• Insulin/therapeutic use
• Insulin Resistance/physiology
• Male
• Middle Aged
• Models, Biological
• Retrospective Studies

Hyperglycemia associated with critical illness, also called “stress hyperglycemia” or “stress diabetes”, is a consequence of many pathophysiologic hormonal responses including increased catecholamines, cortisol, glucagon, and growth hormone. Alterations in multiple biochemical pathways result in increased hepatic and peripheral insulin resistance with an uncontrolled activation of gluconeogenesis and glycogenolysis. Hyperglycemia has a negative impact on the function of the immune system, on the host response to illness or injury, and on infectious and overall outcomes. The degree of glucose elevation is associated with increased disease severity. Large randomized controlled trials including the Van den Berghe study, the NICE-SUGAR trial, VISEP and GLUCONTROL have shown that the control of glucose levels in critically ill patients has implications on outcome and that both hyperglycemia and hypoglycemia are detrimental and should be avoided. Glucose variability has also been shown to be detrimental. Aggressive glucose control strategies have changed due to the concerns of hypoglycemia and therefore intermediate target glucose control strategies are most often adopted. Different patient populations may vary with regards to optimal glucose targets, timing and approach for glucose control, and with regards to the prognostic significance of glucose excursions and variability. Medical, surgical, and trauma patients may benefit at different rates from glucose control and the approach may need to be adapted to various medical settings and to correspond to the workflow of health providers. Effect modifiers for the success of insulin therapy for hyperglycemia include the methods of nutritional supplementation and exogenous glucose administration. Further research is required to improve insulin protocols for glucose control, to further define glucose targets, and to enhance the accuracy of glucose measuring technologies.

• Hyperglycemia
• Hypoglycemia
• Critically ill
• Intensive care unit
• Glucose control

• Neonatal
• glucose
• hypothermia
• hypoxic ischemic encephalopathy
• treatment outcomes

• blood glucose
• critically ill
• insulin
• intravenous insulin
• subcutaneous insulin
• transition

• Adult
• Aged
• Blood Glucose/analysis
• Critical Care/methods
• Critical Illness/therapy
• Female
• Humans
• Hyperglycemia/blood
• Hyperglycemia/prevention & control
• Hypoglycemia/blood
• Hypoglycemia/prevention & control
• Hypoglycemic Agents/administration & dosage
• Infusions, Intravenous
• Injections, Subcutaneous
• Insulin/administration & dosage
• Male
• Middle Aged
• Retrospective Studies

• CGM
• DFA
• ICU
• continuous glucose monitoring
• critical care
• detrended fluctuation
• diabetes
• fractal
• review
• sensor

• Biomarkers/blood
• Blood Glucose/drug effects
• Blood Glucose/metabolism
• Critical Care/methods
• Critical Illness
• Fractals
• Glucose Metabolism Disorders/blood
• Glucose Metabolism Disorders/diagnosis
• Glucose Metabolism Disorders/drug therapy
• Health Status
• Health Status Indicators
• Humans
• Hypoglycemic Agents/therapeutic use
• Intensive Care Units
• Models, Statistical
• Monitoring, Physiologic/instrumentation
• Monitoring, Physiologic/methods
• Predictive Value of Tests
• Signal Processing, Computer-Assisted
• Time Factors
• Treatment Outcome

Continuous Glucose Monitoring (CGM) has become an increasingly investigated tool, especially with regards to monitoring of diabetic and critical care patients. The continuous glucose data allows the calculation of several glucose variability parameters, however, without specific application the interpretation of the results is time-consuming, utilizing extreme efforts. Our aim was to create an open access software [Glycemic Variability Analyzer Program (GVAP)], readily available to calculate the most common parameters of the glucose variability and to test its usability.

The GVAP was developed in MATLAB® 2010b environment. The calculated parameters were the following: average area above/below the target range (Avg. AUC-H/L); Percentage Spent Above/Below the Target Range (PATR/PBTR); Continuous Overall Net Glycemic Action (CONGA); Mean of Daily Differences (MODD); Mean Amplitude of Glycemic Excursions (MAGE). For verification purposes we selected 14 CGM curves of pediatric critical care patients. Medtronic® Guardian® Real-Time with Enlite® sensor was used. The reference values were obtained from Medtronic®^’s own software for Avg. AUC-H/L and PATR/PBTR, from GlyCulator for MODD and CONGA, and using manual calculation for MAGE.

The Pearson and Spearman correlation coefficients were above 0.99 for all parameters. The initial execution took 30 minutes, for further analysis with the Windows® Standalone Application approximately 1 minute was needed.

The GVAP is a reliable open access program for analyzing different glycemic variability parameters, hence it could be a useful tool for the study of glycemic control among critically ill patients.

The online version of this article (doi:10.1186/s12938-015-0035-3) contains supplementary material, which is available to authorized users.

Hyperglycemia, hypoglycemia and increased glucose variability are independently associated with increased risk of death in critically ill adults. The relationship between time in targeted blood glucose range (TIR) and mortality is not well described and may be a factor that has confounded the results of the major interventional trials of intensive insulin therapy.

We conducted a retrospective analysis of prospectively collected data involving 3,297 patients with intensive care unit (ICU) lengths of stay (LOS) of ≥1.0 day who were admitted between 1 January 2009 and 31 December 2013 to a single mixed medical-surgical ICU. We investigated the relationship between TIR 70 to 140 mg/dl with mortality and compared outcomes of non-diabetics (NON) and individuals with diabetes mellitus (DM), including stratifying by TIR above (TIR-hi) and below (TIR-lo) the median value for the NON and DM groups.

There were 85,799 blood glucose (BG) values for the NON group and 32,651 for the DM group, and we found that 75.5% and 54.8%, respectively, were between 70 and 140 (P <0.0001). The median (interquartile range) TIR (%) values for the NON and DM groups were 80.6% (61.4% to 94.0%) and 55.0% (35.5% to 71.1%), respectively (P <0.0001). For the NON group, mortality was 8.47% and 15.71% for TIR-hi and TIR-lo, respectively (P <0.0001). For the DM group, mortality was 16.09% and 14.44% for TIR-hi and TIR-lo, respectively (P = NS). We observed similar relationships for the NON group when we stratified by ICU LOS or severity of illness, especially in the most severely ill patients. There was a cumulative interaction of indices of hypoglycemia, hyperglycemia or glucose variability with TIR. Multivariable analysis demonstrated, for the NON group, that TIR-hi was independently associated with increased survival (P =0.0019). For the NON group, the observed-to-expected mortality ratios for TIR-hi and TIR-lo, based on Acute Physiology and Chronic Health Evaluation IV methodology, were 0.53 and 0.78, respectively. In contrast, among those in the DM group, there was no clear relationship between TIR 70 to 140 mg/dl and survival.

Independently of ICU LOS and severity of illness, TIR 70 to 140 mg/dl >80% is strongly associated with survival in critically ill patients without diabetes. These findings have implications for the design of clinical protocols for glycemic control in critically ill patients as well for the design of future interventional trials of intensive insulin therapy.

The online version of this article (doi:10.1186/s13054-015-0908-7) contains supplementary material, which is available to authorized users.

• Glycemic variability
• Hospitalization outcomes
• Hypoglycemia
• Length of stay
• Non-critically ill

• Acute Kidney Injury/etiology
• Blood Glucose/drug effects
• Blood Glucose/metabolism
• Critical Illness
• Cross Infection/etiology
• Diabetes Mellitus/drug therapy
• Female
• Hospitalization/statistics & numerical data
• Humans
• Hypoglycemia/chemically induced
• Hypoglycemia/complications
• Hypoglycemic Agents/adverse effects
• Insulin/adverse effects
• Intensive Care Units/statistics & numerical data
• Length of Stay/statistics & numerical data
• Male
• Middle Aged
• Retrospective Studies

• Decompensated liver cirrhosis
• Glucose disturbance
• Hypoglycemia
• Mortality