Acute methanol poisoning signifies a global health issue. This study was designed to explore the role of the leukocyte glucose index (LGI) in predicting clinical outcomes; in-hospital mortality and visual impairment, and length of hospital stay, in acute methanol toxicity and to evaluate the association between LGI and all initial patient characteristics.

This was a retrospective analysis that involved 82 acutely methanol-intoxicated patients, starting from January 2021 to December 2023. Patients were categorized by on-admission LGI tertiles into low, intermediate, and high groups.

Approximately 27 % (22 out of 82) of patients died during hospitalization, with most of them belonging to the high LGI group. No significant differences existed in the proportions of patients with total vision loss, or the length of hospital stay. The majority of the undesirable findings were apparent in patients in either the intermediate or high LGI groups. LGI can distinguish exceptionally between survivors and non-survivors with an area under the curve of 0.808. However, LGI does not have any discriminatory power in predicting adverse visual outcomes.

LGI can serve as a valuable tool in predicting early in-hospital mortality in acute methanol poisoning.

Stress-induced hyperglycemia (SIH) is a physiological response to acute or chronic stress characterized by elevated blood glucose levels. It is prevalent in both patients with and without diabetes, particularly those with acute or critical illnesses. The development of SIH is characterized by complex interactions among catecholamines, cortisol, and inflammatory mediators such as cytokines, resulting in increased hepatic glucose production and insulin resistance. While mild to moderate SIH may provide a protective mechanism during stress, prolonged or excessive hyperglycemia can exacerbate inflammation and oxidative stress, contributing to adverse outcomes in conditions such as acute myocardial infarction, heart failure, and cerebrovascular diseases. The stress-hyperglycemia ratio (SHR), defined as the ratio of admission glucose to estimated mean glucose (derived from glycated hemoglobin [HbA1c]), has emerged as a valuable tool for quantifying stress hyperglycemia. Unlike absolute glucose levels, the SHR accounts for background hyperglycemia and provides a more accurate indicator of the relative glucose elevation associated with critical illness. Extensive research has demonstrated a U-shaped or J-shaped relationship of the SHR with disease outcomes, indicating that both low and high SHRs are associated with increased mortality and morbidity. The SHR has shown significant predictive value in cardiovascular diseases (e.g., acute coronary syndrome, heart failure), cerebrovascular diseases (e.g., acute ischemic stroke, intracerebral hemorrhage), and infectious diseases (e.g., sepsis, pneumonia). It also plays a role in other conditions, such as acute pancreatitis and certain cancers. The ease of calculating the SHR from widely available admission glucose and HbA1c tests makes it a practical and valuable prognostic marker in clinical settings. This review examines the relationship between the SHR and critical illnesses, highlighting its mechanisms and predictive value across various diseases.

The triglyceride-glucose (TyG) index is a marker for insulin resistance (IR) linked to diabetes complications and poor outcomes. Its connection to all-cause mortality in non-diabetic critically ill patients is unknown. This study aims to investigate the TyG index's impact on mortality in this population, evaluating how IR affects their prognosis.

This study is retrospective observational research utilizing data from the eICU Collaborative Research Database. A total of 14,089 non-diabetic critically ill patients were included and categorized into three groups based on the TyG index measured on the first day of admission (T1, T2, and T3). Kaplan-Meier survival analysis was performed to compare the 28-day mortality rates among the different groups. Cox proportional hazards models were used to assess the relationship between the TyG index and 28-day mortality. Additionally, we conducted sensitivity analyses, subgroup analyses, and interaction analyses to assess the robustness of the results.

During the observation period, 730 patients (5.18%) died in the ICU, while 1,178 patients (8.36%) died in the hospital. The 28-day ICU mortality rate and hospital mortality rate significantly increased with higher TyG index values (P < 0.001). Cox proportional hazards models were used to assess the relationship between the TyG index and 28-day mortality. Specifically, Cox proportional hazards models were used to assess the relationship between the TyG index and 28-day mortality. Furthermore, the analysis showed a nonlinear effect of the TyG index on mortality in non-diabetic critically ill patients, with a critical point at 9.94. While Below 9.94, ICU and hospital mortality rates rose with higher TyG index values. But above 9.94, mortality didn't significantly increase despite further rises in the TyG index. Sensitivity and subgroup analyses confirmed the robustness of these results, and E-value analysis indicated strong resistance to unmeasured confounding factors.

The TyG index demonstrates a significant positive correlation with all-cause mortality in non-diabetic critically ill patients, exhibiting a nonlinear relationship. Consequently, the TyG index serves as a crucial tool for identifying high-risk patients, thereby assisting clinicians in formulating more effective monitoring and intervention strategies.

To explore the association between diabetes and stress-induced hyperglycemia with skeletal muscle expression of key genes related to glucose transport.

This is a cross-sectional study. Skeletal muscle biopsies were taken from the left vastus muscle of critically ill adult patients within 24 hours of intensive care unit admission, and the expression of the genes of interest, namely insulin receptor substrate 1 (IRS1), insulin receptor substrate 2 (IRS2), solute carrier family 2 member 1 (SLC2A1), and solute carrier family 2 member 4 (SLC2A4), was analyzed using quantitative polymerase chain reaction. The primary analysis was planned to compare the gene expression pattern between patients with and without diabetes mellitus. The secondary analyses compared the gene expression in subgroups of patients with different levels of glycemia, glycemic variability, and glycemic gap.

A total of 50 consecutive patients (15 with diabetes mellitus and 35 without diabetes mellitus) were included from April 2018 to September 2018. No differences in gene expression were found between patients with or without diabetes mellitus. Individuals with hyperglycemia > 200 mg/dL at intensive care unit admission exhibited a downregulation of IRS1 compared to those without (0.4 [0.1-0.8] versus 1.1 [0.3-2.2], p = 0.04). Similarly, patients with a glycemic gap ≥ 80 mg/dL exhibited a downregulation of IRS1 compared to those with a glycemic gap < 80 mg/dL (0.3 [0.1-0.7] versus 1 [0.4-2] p=0.04). There was no difference in gene expression between patients with glycemic variability higher or lower than 40 mg/dL.

No significant changes were found in skeletal muscle expression of IRS1, IRS2, SLC2A1, and SLC2A4 in critically ill patients with or without diabetes mellitus. However, IRS1 was downregulated in patients with stress-induced hyperglycemia.

Advanced glycation end-products (AGEs) can enter patients' circulation through exogenous sources, such as enteral nutrition formulae. Circulating AGEs, specifically carboxymethyllysine, can promote insulin resistance and activation of pro-inflammatory pathways leading to oxidative stress, cell death, and organ failure. Suboptimal kidney function increases the risk of elevated circulating AGEs because levels are controlled through urinary excretion. Our aim was to determine associations between carboxymethyllysine intake and glycemic control as well as clinical outcomes in critically ill patients and explore these in the subset of patients with an acute kidney injury (AKI).

This was a retrospective cohort study. Data were extracted from electronic medical records. Patients were eligible if they were ≥18 years and received enteral nutrition, with known carboxymethyllysine content, for ≥3 days. AKI was defined using the Kidney Disease: Improving Global Outcomes guidelines. Linear and logistic regression models were used to determine adjusted associations.

Between 2015 and 2021, 2636 patients met the eligibility criteria, with 848 (32%) patients having an AKI. Most were male (n = 1752, 67%) with a median (interquartile range) Acute Physiology And Chronic Health Evaluation III score of 59 (45-77). For every 10-μmol increase in carboxymethyllysine provision, mean blood glucose increased by 0.05 mmol (95% CI, 0.03-0.07), and the odds of dying increased by 16% (odds ratio = 1.16; 95% CI, 1.06-1.27). A subgroup analysis indicated these associations persisted in patients with AKI but not in those without.

Carboxymethyllysine intake was associated with increased mean blood glucose and odds of dying in our study cohort.

Critically ill children requiring treatment in a pediatric intensive care unit (PICU) suffer from anorexia and/or feeding intolerance. The resulting macronutrient deficit associates with poor outcome. Until recently, this association formed the basis for initiating enteral or parenteral feeding early to improve outcome. The multicenter "Early-versus-Late-Parenteral-Nutrition-in-the-Pediatric-Intensive-Care-Unit" randomized controlled trial (PEPaNIC-RCT) addressed whether this association is causal. It showed that early supplementation of insufficient/contraindicated enteral nutrition with parenteral nutrition, as compared with accepting a macronutrient deficit throughout the first week in the PICU, did not improve outcome. On the contrary, it caused more infections and prolonged organ support and PICU stay, and adversely affected neurodevelopmental outcomes 2 and 4 years later. Harm was present in all subgroups and appeared explained by the macronutrient dose, more specifically the amino-acid dose, not lipid or glucose doses. These findings corroborated results from large-scale adult RCTs. Mechanisms of harm from early enhanced nutrition comprised suppressed cellular repair pathways like autophagy and ketogenesis, suppressed illness-induced alterations in thyroid hormone metabolism, more iatrogenic hyperglycemia, increased urea cycle activity through anabolic resistance, and induction of epigenetic modifications that mediate longer-term developmental impairments. These results came unexpected to many pediatric intensivists. Hence, the paradigm has only slowly begun to shift toward more restrictive macronutrient administration in the acute phase of critical illness. Benefits of early fasting responses have become clear, provided micronutrients are given to prevent deficiencies and refeeding syndrome. These insights open perspectives for studies investigating novel nutritional strategies to activate fasting-induced cellular repair while avoiding prolonged starvation.

This study aimed to explore the association between the stress hyperglycemia ratio (SHR) and short- and long-term outcomes in critically ill patients with sepsis.

This retrospective observational cohort study was conducted using the Medical Information Mart for Intensive Care-IV (MIMIC-IV v2.2) database. Patients were categorized into 4 SHR quartiles. The main focus was on in-hospital mortality and 1-year all-cause mortality as primary endpoints, while intensive care unit and hospital stays were considered as secondary outcomes. Regression and subgroup analyses were used to assess the correlation between SHR and the primary and secondary outcomes. Restricted cubic spline analysis was utilized to explore the nonlinear relationships between SHR and in-hospital and 1-year all-cause mortality.

This study included two groups of patients, comprising 7456 and 6564 individuals. The in-hospital and 1-year mortality was 11.96% and 17.96% in Cohort 1 and 2, respectively. SHR was associated with an elevated risk of in-hospital mortality (OR: 2.08, 95%CI 1.66-2.61) and 1-year mortality (HR: 1.70, 95% CI 1.42-2.04). Patients in SHR quartile 4 had a higher risk of in-hospital (OR: 1.86, 95% CI 1.51-2.30) and 1-year (HR: 1.44, 95% CI 1.23-1.69) mortality than those in quartile 2. Restricted cubic spline analysis showed a "J-shaped" relationship between SHR and all-cause mortality in both cohorts. The relationship between high SHR and mortality remained consistent across almost all predefined subgroups.

Our study suggests that high SHR is associated with increased in-hospital and 1-year mortality in critically ill sepsis patients. Further investigations are needed to validate these results.

Dysglycaemia metrics, defined as hyperglycaemia, increased glucose variability, hypoglycaemia and reduced time in the targeted blood glucose range (TIR), are linked to higher mortality. The relationship between dysglycaemia metrics and intensive care unit (ICU) mortality over time for patients with and without diabetes remains inconclusive, posing challenges for ICU medical staff in accurately identifying and distinguishing various risk factors and taking timely and appropriate responses.

To explore which dysglycaemia metrics within the first 7 days of ICU stay are associated with ICU mortality among patients with and without diabetes.

This retrospective cohort study included 712 patients without diabetes and 222 patients with diabetes. Clinical data were collected within the first 7 days of ICU stay. Binary logistic regression models were built to analyse which dysglycaemia metrics (hyperglycaemia, coefficient of variation [CV], hypoglycaemia and TIR) on the first day, over the first 3, 5 and 7 days of ICU stay were associated with ICU mortality.

In patients with diabetes, hyperglycaemia on the first day (OR: 4.90, 95% CI: 1.51-15.90, p = .008) and TIR <70% during the first 7 days of ICU stay (OR: 16.31, 95% CI: 1.50-176.89, p = .022) were associated with increased ICU mortality. In patients without diabetes, CV >20% on the first day (OR: 1.46, 95% CI: 1.03-2.07, p = .035), and TIR <70% during the first 3 (OR: 2.01, 95% CI: 1.35-2.98, p < .001) and 5 days (OR: 1.66, 95% CI: 1.09-2.54, p = .019) were associated with increased ICU mortality (p < .05). The proportion of hypoglycaemia did not significantly correlate with ICU mortality in patients with or without diabetes (p > .05).

Specific dysglycaemia metrics are associated with ICU mortality between patients with and without diabetes. In patients with diabetes, hyperglycaemia on the first day and TIR <70% on the first 7 days with higher mortality. In patients without diabetes, CV >20% on the first day and TIR <70% in the first 3 and 5 days are associated with higher mortality. Monitoring these metrics may potentially help develop strategies to decrease ICU mortality through individualized glycaemic management.

Close monitoring of dysglycaemia metrics, especially TIR, and personalized glucose management based on diabetic status may help identify high-risk ICU patients and improve targeted care strategies.

The effect of the modality of hydrocortisone administration on clinical outcomes in patients with septic shock remains uncertain. This systematic review and meta-analysis evaluate the impact of intermittent bolus and continuous infusion of hydrocortisone on these outcomes.

We searched the PubMed, Embase databases, and Cochrane Library for randomized controlled trials (RCTs) and cohort studies published from inception to January 1, 2023. We included studies involving adult patients with septic shock. All authors reported our primary outcome of short-term mortality and clearly compared the clinically relevant secondary outcomes (ICU length of stay, hospital length of stay, vasopressor-free days, hyperglycemia, hypernatremia, and ICU-acquired weakness [ICUAW]) of intermittent bolus and continuous infusion of hydrocortisone. Results were expressed as odds ratio (OR) and mean difference (MD) with accompanying 95% confidence interval (CI). The PROSPERO registration number is CRD42023392160.

Seven studies, including 554 patients, were included. The primary outcome of this meta-analysis showed no statistically significant difference in the short-term mortality between intermittent bolus and continuous infusion groups (OR=1.21, 95% CI: 0.84 to 1.73; P=0.31; Chi2 =9.06; I 2=34%). Secondary outcomes showed no statistically significant difference in the ICU length of stay (MD=-0.15, 95% CI: -2.31 to 2.02; P=0.89; Chi2 =0.95; I 2=0%), hospital length of stay (MD=0.63, 95% CI: -4.24 to 5.50; P=0.80; Chi2 =0.61; I 2=0%), vasopressor-free days (MD=-1.18, 95% CI: -2.43 to 0.06; P=0.06; Chi2 =2.48; I 2=60%), hyperglycemia (OR=1.27, 95% CI: 0.80 to 2.02; P=0.31; Chi2 =5.23; I 2=43%), hypernatremia (OR=0.93, 95% CI: 0.44 to 1.96; P=0.85; Chi2 =0.37; I 2=0%), or ICUAW (OR=0.83, 95% CI: 0.36 to 1.94; P=0.67; Chi2 =0.90; I 2=0%) between the two groups.

This meta-analysis indicated no significant difference in short-term mortality between intermittent bolus or continuous hydrocortisone infusion in patients with septic shock. Additionally, the hydrocortisone infusion method was not associated with ICU length of stay, hospital length of stay, vasopressor-free days, hyperglycemia, hypernatremia, or ICUAW.

The glycemic response to critical COVID-19 remains uncertain. We aimed to assess the association between COVID-19, insulin requirements, glycemic control, and mortality in intensive care unit (ICU) patients.

We conducted a retrospective observational study of 350 COVID-19 patients and 1067 non-COVID-19 patients admitted to the ICU. Insulin requirement was defined as the total units of exogenous insulin required to cover one gram of administered carbohydrates (insulin-to-carbohydrate ratio, ICR). We used multivariable generalized linear mixed-model (GLMM) analysis to assess the association of the interaction between COVID-19 and ICU-day with daily ICR, adjusted for fixed and time-dependent covariates. Glycemic control was assessed after stratification on diabetes and COVID-19. We used multivariable logistic regression analysis to assess the association between ICR and 90-day mortality.

The mean (95% CI) of the mean daily ICR among patients without diabetes was 0.09 (0.08-0.11) U/g and 0.15 (0.11-0.18) U/g in the non-COVID-19 group and COVID-19 group (p = .01), respectively. In diabetes patients, the corresponding ICRs were 0.52 (0.43-0.62) U/g and 0.59 (0.50-0.68) U/g (p = .32). In multivariable GLMM analysis, the interaction between COVID-19 and ICU-day was independently associated with ICR (risk estimate 1.22, 95% CI 1.15-1.31, p < .001). COVID-19 was associated with higher hypoglycemia prevalence irrespective of diabetes status, higher average glucose levels, more pronounced glucose variability, and a lower proportion of glucose values within target range among patients without diabetes. On multivariable logistic regression analysis, the adjusted odds ratio for 90-day mortality was 1.77 (95% CI 0.94-3.34, p = .076) per one unit increase in mean ICR.

In our cohort of ICU patients, COVID-19 was associated with higher daily insulin requirements per gram of administered carbohydrates, and worse glycemic control. We found no robust association between ICR and increased odds of death at 90 days.

Stress hyperglycaemia ratio (SHR) has been reported to be independently and significantly associated with various adverse cardiovascular events as well as mortality. Moreover, in-hospital heart failure following acute myocardial infarction has been demonstrated to account for majority of all heart failure (HF) cases with anterior myocardial infarction showing higher rates of HF. However, the association between SHR and in-hospital HF following an anterior ST-elevation myocardial infarction (STEMI) has not been reported earlier. Therefore, the present study aimed at identifying the relationship between SHR and in-hospital HF post STEMI.

In this retrospective study electronic health records of 512 patients who presented with anterior STEMI from 01 January 2022 to 31 January 2024 were analysed. Based on the development of in-hospital HF, the enrolled patients were stratified into two groups: Group I, comprising of 290 patients who developed in-hospital HF and Group II comprising of 222 patients who did not develop in-hospital HF. ROC and Multivariable logistic regression analyses were performed to assess the relationship between SHR and in-hospital HF.

The results revealed that SHR is a significant independent predictor of in-hospital HF (OR: 3.53; 95%CI: 2.02-6.15; p < 0.001). Apart from SHR, the results also identified age, nosocomial pneumonia, ventricular fibrillation, LVEF, and NT-pro-BNP levels as other independent predictors. ROC analysis showed that SHR independently had a moderate discriminative power with AUC: 0.683, 95% CI 0.605-0.762; p = 0.04, which was almost comparable to the combined predictive value of other independent risk factors (AUC: 0.726, 95% CI 0.677-0.784). Noticeably, combining SHR and other identified independent predictors demonstrated a significant predictive power (AUC: 0.813, 95% CI 0.757-0.881; p = 0.01).

SHR is an independent predictor for in-hospital HF in anterior wall STEMI patients.

Dysglycaemia in hospitalised patients is associated with poorer clinical outcomes, including cardiovascular events, longer hospital stays, and increased risk of mortality. Therefore, glucose monitoring is necessary to achieve best outcomes.

This audit assesses use of point-of-care (POC) blood glucose (BG) testing in Tallaght University Hospital (TUH) over an 8-day period. It evaluates compliance with international and TUH glucose monitoring protocols and determines frequency of diabetes team consultations for inpatient adults.

Data from an 8-day period (12/03/2023-19/03/2023) were extracted from the TUH COBAS-IT system and analysed. Invalid tests were excluded. Hyperglycaemia was defined as ≥ 10 mmol/L and hypoglycaemia as ≤ 3.9 mmol/L. Persistent hyperglycaemia was defined as two BG results of ≥ 10 mmol/L. A chart review was conducted on adult patients with persistent hyperglycaemia to assess for HbA1C results, diabetes diagnosis, and diabetes consult.

3,530 valid tests were included and analysed. 674 individual patients had tests done. 1,165 tests (33.00%) were hyperglycaemic and 75 (2.12%) were hypoglycaemic. 68.25% of adults with persistent hyperglycaemia had an HbA1C test performed or documented within three months. 42.71% of inpatient adults with persistent hyperglycaemia and a known diabetes diagnosis received a consult from the diabetes team.

Increased adherence to hospital protocols for testing HbA1C in adults with persistent hyperglycaemia could improve treatment and clinical outcomes. Increased diabetes team consultation could facilitate appropriate treatment and improve patient outcomes in persistently hyperglycaemic adult patient populations.

Background: In this study, we explored the potential of predicting dysglycemia in patients who need to continuously manage blood glucose levels using a non-invasive method via electrocardiography (ECG). Methods: The data were collected from patients with diabetes, and heart rate variability (HRV) features were extracted via ECG processing. A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. Results: The dysglycemia prediction results at each time point, including at the time of blood glucose measurement, 15 min prior to measurement, and 30 min prior to measurement, exhibited no significant differences compared with the blood glucose measurement values. This result confirmed that the proposed artificial intelligence model for dysglycemia prediction performed well at each time point. Additionally, to determine the optimal number of features required for predicting dysglycemia, 77 HRV features were individually eliminated in the order of decreasing importance with respect to the prediction accuracy; the optimal number of features for the model to predict dysglycemia was determined to be 12. The dysglycemia prediction results obtained 30 min prior to measurement, which exhibited the highest prediction range in this study, were as follows: accuracy = 90.5, sensitivity = 87.52, specificity = 92.74, and precision = 89.86. Conclusions: Furthermore, we determined that no significant differences exist in the blood glucose prediction results reported in previous studies, wherein various vital signs and blood glucose values were used as model inputs, and the results obtained in this study, wherein only ECG data were used to predict dysglycemia.

Hyperglycemia upon admission is associated with poor prognosis of many cardiovascular diseases. However, the relationship of stress hyperglycemia ratio (SHR), admission blood glucose (ABG), and hemoglobin A1c (HbA1c) with pulmonary hypertension has not been reported. This study aimed to explore the association of hyperglycemia indices with disease severity and long-term adverse outcomes in patients with idiopathic pulmonary arterial hypertension (IPAH).

This multi-center cohort study included 625 consecutive patients diagnosed with or treated for IPAH between January 2015 and June 2023. SHR was calculated using the followings: ABG (mmol/L)/(1.59 × HbA1c [%] - 2.59). The primary endpoint was defined as clinical worsening events. Multivariable Cox regression and restricted cubic spline analyses were employed to evaluate the association of SHR, ABG, and HbA1c with endpoint events. The mediating effect of pulmonary hemodynamics was evaluated to investigate the potential mechanism between hyperglycemia and clinical outcomes.

During a mean follow-up period of 3.8 years, 219 (35.0%) patients experienced all-cause death or clinical worsening events. Hyperglycemia indices correlated with well-validated variables that reflected the severity of IPAH, such as the World Health Organization functional class, 6-min walk distance, and N-terminal pro-brain natriuretic peptide levels. Multivariable Cox regression analyses indicated that SHR (hazard ratio [HR] 1.328, 95% confidence intervals [CI]: 1.185, 1.489 per 0.1-unit increment, P < 0.001) and ABG (HR 1.317, 95% CI: 1.134, 1.529 per 1.0-unit increment, P < 0.001) were independent predictors of primary endpoint events. Mediation analysis indicated that pulmonary vascular resistance mediated 5.65% and 14.62% of the associations between SHR and ABG and clinical worsening events, respectively. The addition of SHR significantly improved reclassification, discrimination ability, and model fit beyond the clinical risk prediction model.

SHR is positively associated with clinical worsening in patients with IPAH. The association appeared to be partially mediated through the pathway of pulmonary vascular remodeling, indicating that SHR may serve as a valuable indicator for providing additional risk information.

This study endeavors to explore the ramifications of early dynamic blood glucose (BG) trajectories within the initial 48 h of intensive care unit (ICU) admission on mortality among critically ill heart failure (HF) patients.

The study employed a retrospective observational design, analyzing dynamic BG data of HF patients from the Medical Information Mart for Intensive Care IV database. The BG trajectory subphenotypes were identified using the hierarchical clustering based on the dynamic time-warping algorithm. The primary outcome of the study was 28-day mortality, with secondary outcomes including 180-day and 1-year mortality.

We screened a total of 21,098 HF patients and finally 15,092 patients were included in the study. Our results identified three distinct BG trajectory subphenotypes: increasing (n = 3503), stabilizing (n = 6250), and decreasing (n = 5339). The increasing subphenotype was associated with the highest mortality risk at 28 days, 180 days, and 1 year. The stabilizing and decreasing subphenotypes showed significantly lower mortality risks across all time points, with hazard ratios ranging from 0.85 to 0.88 (P<0.05 for all). Sensitivity analyses confirmed the robustness of these findings after adjusting for various covariates.

Increasing BG trajectory within 48 h of admission is significantly associated with higher mortality in patients with HF. It is necessary to devote greater attention to the early BG dynamic changes in HF patients to optimize clinical BG management and enhance patient prognosis.

Hyperglycaemia is common in intensive care unit (ICU) patients. Glycaemic monitoring and effective glycaemic control with insulin are crucial in the ICU to improve patient outcomes. However, glycaemic control and insulin use vary between ICU patients and hypo- and hyperglycaemia occurs. Therefore, we aim to provide contemporary data on glycaemic control and management, and associated outcomes, in adult ICU patients. We hypothesise that the occurrence of hypoglycaemia in acutely admitted ICU patients is lower than that of hyperglycaemia.

We will conduct a bi-centre cohort study of 300 acutely admitted adult ICU patients. Routine data will be collected retrospectively at baseline (ICU admission) and daily during ICU stay up to a maximum of 30 days. The primary outcome will be the number of patients with hypoglycaemia during their ICU stay. Secondary outcomes will be occurrence of severe hypoglycaemia, occurrence of hyperglycaemia, time below blood glucose target range, time above target range, all-cause mortality at Day 30, number of days alive without life support at Day 30 and number of days alive and out of hospital at Day 30. Process outcomes include the number of in-ICU days, glucose measurements (number of measurements and method) and use of insulin (including route of administration and dosage). All statistical analyses will be descriptive.

This cohort study will provide a contemporary overview of glucose evaluation and management practices in adult ICU patients and, thus, highlight potential areas for improvement through future clinical trials in this area.

Whether intensive glucose control reduces mortality in critically ill patients remains uncertain. Patient-level meta-analyses can provide more precise estimates of treatment effects than are currently available.

We pooled individual patient data from randomized trials investigating intensive glucose control in critically ill adults. The primary outcome was in-hospital mortality. Secondary outcomes included survival to 90 days and time to live cessation of treatment with vasopressors or inotropes, mechanical ventilation, and newly commenced renal replacement. Severe hypoglycemia was a safety outcome.

Of 38 eligible trials (n=29,537 participants), 20 (n=14,171 participants) provided individual patient data including in-hospital mortality status for 7059 and 7049 participants allocated to intensive and conventional glucose control, respectively. Of these 1930 (27.3%) and 1891 (26.8%) individuals assigned to intensive and conventional control, respectively, died (risk ratio, 1.02; 95% confidence interval [CI], 0.96 to 1.07; P=0.52; moderate certainty). There was no apparent heterogeneity of treatment effect on in-hospital mortality in any examined subgroups. Intensive glucose control increased the risk of severe hypoglycemia (risk ratio, 3.38; 95% CI, 2.99 to 3.83; P<0.0001).

Intensive glucose control was not associated with reduced mortality risk but increased the risk of severe hypoglycemia. We did not identify a subgroup of patients in whom intensive glucose control was beneficial. (Funded by the Australian National Health and Medical Research Council and others; PROSPERO number CRD42021278869.).

Introduction Acute insulin resistance (IR) and hyperglycemia are frequently observed during acute myocardial infarction (AMI), significantly influencing both immediate and long-term patient outcomes, irrespective of diabetic status. Neutrophilia and increased neutrophil activity, which are common in these scenarios, have been associated with poorer prognoses, as demonstrated in our recent findings. While it is well established that neutrophils and stress-induced hyperglycemia exacerbate inflammation and hinder recovery, the complex interplay between these factors and their combined impact on AMI prognosis remains inadequately understood. This study aims to investigate the effects of stress hyperglycemia and IR on AMI patients at the onset of the event and to elucidate the relationship between these metabolic disturbances and inflammatory markers, particularly neutrophils. Methods We conducted a longitudinal prospective study on 219 AMI patients at Elias Emergency Hospital in Bucharest, Romania, from April 2021 to September 2022. Patients were included within 24 hours of AMI with ST-segment elevation and excluded if they had acute infections or chronic inflammatory diseases. Blood samples were collected to study inflammatory biomarkers, including neutrophil extracellular traps (NETs), S100A8/A9, interleukin (IL)-1β, IL-18, and IL-6. Diabetic and pre-diabetic statuses were defined using glycated hemoglobin (HbA1c) and medical history (ADA 2019 criteria). To assess glycemic parameters, we employed the glycemia ratio (GR) and the homeostatic model assessment of insulin resistance (HOMA-IR) index, enabling a precise evaluation of stress hyperglycemia, acute IR, and their prognostic implications. Patients were stratified into groups based on GR calculations, categorized as under-average glycemia, normal glycemia, and stress hyperglycemia. Results The majority of patients in the stress hyperglycemia group exhibited an unfavorable prognosis. This group also demonstrated significantly elevated neutrophil counts and neutrophil-to-lymphocyte ratios (NLR). The GR was significantly and positively correlated with inflammation markers, including neutrophil count (Pearson's R = 0.181, P = 0.008) and NLR (Pearson's R = 0.318, P < 0.001), but showed no significant correlation with other evaluated inflammatory markers. Conclusions Our findings suggest that poor outcomes in AMI patients may be associated with stress hyperglycemia, as indicated by GR. AcuteIR, quantified by GR and HOMA-IR, exhibits a strong correlation with neutrophil count and NLR within the first 24 hours of AMI onset. However, no significant correlation was observed with other inflammatory markers, such as IL-1β, IL-18, and IL-6, underscoring the specific interplay between IR and neutrophil activity in this setting.

Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU).

Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency.

We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings.

We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.

Coronavirus disease 2019 (COVID-19) results in several complications and mortality in intensive care unit (ICU) patients. Limited studies have investigated the effect of enteral nutrition (EN) on the survival of COVID-19 patients in the ICU. The aim of this study was to investigate the association of EN with biochemical and pathological indices associated with mortality in ICU patients with COVID-19.

This case-control study was conducted on 240 patients with COVID-19 hospitalized in the ICU including 120 eventual nonsurvived as the cases and 120 survived patients as the controls. All of the patients received EN as a high protein high volume or standard formula. Data on general information, anthropometric measurements, and the results of lab tests were collected.

The recovered patients received significantly more high protein (60.8% vs. 39.6%, p = .004) and high volume (61.6% vs. 42.3%, p = .005) formula compared to the nonsurvived group. Mortality was inversely associated with high volume (odds ratio [OR]: 0.45 confidence interval [CI]95%, p = .008) and high protein (OR: 0.42 CI95%, p = .003) formula. The results remained significant after adjusting for age and sex. Further adjustment for underlying diseases, smoking, body mass index, and the acute physiology and chronic health evaluation II (APACHE II) score did not change the results.

The findings of the study showed that there was a significant inverse association between mortality and high volume and high protein formula in patients with COVID-19. Further investigation is warranted.

Maintaining glycemic control of critically ill patients may impact outcomes such as survival, infection, and neuromuscular recovery, but there is equipoise on the target blood levels, monitoring frequency, and methods.

The purpose was to update the 2012 Society of Critical Care Medicine and American College of Critical Care Medicine (ACCM) guidelines with a new systematic review of the literature and provide actionable guidance for clinicians.

The total multiprofessional task force of 22, consisting of clinicians and patient/family advocates, and a methodologist applied the processes described in the ACCM guidelines standard operating procedure manual to develop evidence-based recommendations in alignment with the Grading of Recommendations Assessment, Development, and Evaluation Approach (GRADE) methodology. Conflict of interest policies were strictly followed in all phases of the guidelines, including panel selection and voting.

We conducted a systematic review for each Population, Intervention, Comparator, and Outcomes question related to glycemic management in critically ill children (≥ 42 wk old adjusted gestational age to 18 yr old) and adults, including triggers for initiation of insulin therapy, route of administration, monitoring frequency, role of an explicit decision support tool for protocol maintenance, and methodology for glucose testing. We identified the best available evidence, statistically summarized the evidence, and then assessed the quality of evidence using the GRADE approach. We used the evidence-to-decision framework to formulate recommendations as strong or weak or as a good practice statement. In addition, "In our practice" statements were included when the available evidence was insufficient to support a recommendation, but the panel felt that describing their practice patterns may be appropriate. Additional topics were identified for future research.

This guideline is an update of the guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients. It is intended for adult and pediatric practitioners to reassess current practices and direct research into areas with inadequate literature. The panel issued seven statements related to glycemic control in unselected adults (two good practice statements, four conditional recommendations, one research statement) and seven statements for pediatric patients (two good practice statements, one strong recommendation, one conditional recommendation, two "In our practice" statements, and one research statement), with additional detail on specific subset populations where available.

The guidelines panel achieved consensus for adults and children regarding a preference for an insulin infusion for the acute management of hyperglycemia with titration guided by an explicit clinical decision support tool and frequent (≤ 1 hr) monitoring intervals during glycemic instability to minimize hypoglycemia and against targeting intensive glucose levels. These recommendations are intended for consideration within the framework of the patient's existing clinical status. Further research is required to evaluate the role of individualized glycemic targets, continuous glucose monitoring systems, explicit decision support tools, and standardized glycemic control metrics.

To determine the optimal initial insulin dosage for controlling hyperglycemia in COVID-19 patients receiving steroids, an area with limited data.

We retrospectively analyzed 156 COVID-19 patients with steroid-induced hyperglycemia treated with insulin. Patients were categorized by their total daily dose of subcutaneous insulin therapy when starting dexamethasone ⩾6 mg/day or equivalent dose of glucocorticoid: Group A (⩽0.29 units/kg), Group B (0.3-0.49 units/kg), Group C (0.5-0.69 units/kg), and Group B (⩾0.7 units/kg). Treatment failure was defined as mean blood glucose level > 280 mg/dL for two consecutive days after initiating insulin or any blood glucose ⩾ 400 mg/dL.

The mean age was 64 ± 14 years, with 50% male, and a mean body mass index of 26.9 ± 6.9 kg/m2. Most had preexisting type 2 diabetes (62%). Mean admission blood glucose and HbA1c were 233 ± 112 mg/dL and 7.8 ± 2.3%, respectively. Group A had the lowest HbA1c (6.7 ± 1.2%), while group D had the highest (9.8 ± 2.5%). Median daily dexamethasone dosage or equivalent was 36 (IQR 16.72) mg, with no significant differences in among groups. Group A had the lowest treatment failure rate. There were no significant differences in treatment failure rate between Groups B, C, and D. Additionally, there were no statistically significant differences in mean BG across the groups: Group A 232 ± 42 mg/dL, Group B 247 ± 57 mg/dL, Group C 247 ± 61 mg/dL, and Group D 227 ± 67 mg/dL (p = 0.2). Group D had a significantly higher rate of level 1 hypoglycemia (p = 0.008), while no differences in clinically significant hypoglycemia (level 2 or 3) were observed between groups.

Among patients requiring TDD ⩾ 0.3 units/kg/day, there was no significant difference in treatment failure rate between Groups B, C, and D. Group D had the highest rate of level 1 hypoglycemia. This initial insulin dosage for hospitalized COVID-19 patients on high-dose steroid therapy should be personalized.

It is recognized that patients' blood glucose fluctuates over time during acute disease episodes, especially during the outbreak of cardiovascular events, regardless of the presence of an abnormal blood glucose profile prior to admission to the hospital. Glucose fluctuations in patients with acute myocardial infarction (AMI) in the intensive care unit (ICU) are currently not adequately monitored and studied. We focused on blood glucose fluctuation values within 24 h of admission to assess their association with 30-day and 1-year mortality.

Data of patients with AMI aged 18 years or older from the Critical Care Medical Information Marketplace database III V1.4 were available for analysis in this research. Glucose data were obtained by measurement. A total of 390 of them were treated with PCI. The principal consequence was 30-day and 1-year mortality in patients with AMI. The effect of different glucose fluctuations within 24 h of admission on mortality was predicted by constructing a multivariate Cox regression model with four model adjustments and Kaplan-Meier survival curves. Additionally, we performed curve-fitting analyses to show the correlation between blood glucose fluctuations and risk of death.

We selected 1,699 AMI patients into our study through screening. The included population was categorized into three groups based on the tertiles of blood glucose fluctuation values within 24 h of admission to the ICU. The three groups were <25 mg/dl, 25-88 mg/dl and >88 mg/dl. By cox regression analysis, the group with the highest blood glucose fluctuation values (>88 mg/dl) had the most significant increase in 30-day and 1-year mortality after excluding confounding factors (30-day mortality adjusted HR = 2.11; 95% CI = 1.49-2.98 p < 0.001; 1-year mortality adjusted HR = 1.83; 95% CI = 1.40-2.39 p < 0.001). As demonstrated by the Kaplan-Meier survival curves, the group with the greatest fluctuations in blood glucose has the worst 30-day and 1-year prognosis.

The extent of glucose fluctuations in patients with AMI in the first 24 h after ICU admission is an essential predictor as to 30-day as well as 1-year mortality. When blood glucose fluctuates more than 88 mg/dl within 24 h, mortality increases significantly with the range of blood glucose fluctuations.

This study aimed to investigate the association between blood glucose levels on arrival at the hospital and 1-month survival and favorable neurological outcomes in patients with OHCA using a large Japanese dataset.

This study was a secondary analysis of data from the JAAM-OHCA Registry. Adult (≥18 years) patients with witnessed OHCA transported to emergency departments and registered in the database from June 2014 to December 2019 were included in the study. The primary and secondary endpoints were 1-month survival and 1-month favorable neurological outcomes (Glasgow-Pittsburgh Cerebral Performance Category score 1 or 2), respectively. Patients were categorized into the following four groups based on blood glucose levels on arrival at the hospital: <80 mg/dL, 80-179 mg/dL, 180-299 mg/dL, and ≥300 mg/dL.

This study included 11,387 patients. Survival rates were 1.3%, 3.1%, 7.0%, and 5.7% in the <80 mg/dL, 80-179 mg/dL, 180-299 mg/dL, and ≥ 300 mg/dL blood glucose groups, respectively. The rates of favorable neurological outcomes in each group were 0.4%, 1.5%, 3.3%, and 2.5%, respectively. Multivariable analysis showed that 180-299 mg/dL glucose was significantly associated with 1-month survival and favorable neurological outcomes compared with 80-179 mg/dL glucose (odds ratio [OR], 1.77; 95% confidence interval [CI], 1.34-2.31; p < 0.001 and OR, 1.52; 95 % Cl, 1.02-2.25; p = 0.035, respectively). In this study, blood glucose levels with the best outcomes likely ranged from 200 to 250 mg/dL based on the cubic spline regression model.

Blood glucose level of 180-299 mg/dL on arrival at the hospital was significantly associated with 1-month survival and favorable neurological outcomes compared to blood glucose level of 80-179 mg/dL in patients with OHCA.

Blood glucose management in intensive care units (ICU) remains a controversial topic. We assessed the association between time-weighted average glucose (TWAG) levels and ICU mortality in critically ill patients in a real-world study. This retrospective study included critically ill patients from the Medical Information Mart for Intensive Care IV database. Glycemic distance is the difference between TWAG in the ICU and preadmission usual glycemia assessed with glycated hemoglobin at ICU admission. The TWAG and glycemic distance were divided into 4 groups and 3 groups, and their associations with ICU mortality risk were evaluated using multivariate logistic regression. Restricted cubic splines were used to explore the non-linear relationship. A total of 4737 adult patients were included. After adjusting for covariates, compared with TWAG ≤ 110 mg/dL, the odds ratios (ORs) of the TWAG > 110 mg/dL groups were 1.62 (95% CI 0.97-2.84, p = 0.075), 3.41 (95% CI 1.97-6.15, p < 0.05), and 6.62 (95% CI 3.6-12.6, p < 0.05). Compared with glycemic distance at - 15.1-20.1 mg/dL, the ORs of lower or higher groups were 0.78 (95% CI 0.50-1.21, p = 0.3) and 2.84 (95% CI 2.12-3.82, p < 0.05). The effect of hyperglycemia on ICU mortality was more pronounced in non-diabetic and non-septic patients. TWAG showed a U-shaped relationship with ICU mortality risk, and the mortality risk was minimal at 111 mg/dL. Maintaining glycemic distance ≤ 20.1 mg/dL may be beneficial. In different subgroups, the impact of hyperglycemia varied.

Patients who experience gastrointestinal (GI) intolerance and hyperglycemia (or glucose intolerance) may not achieve appropriate caloric requirements and experience poor outcomes. The aim was to examine patient characteristics, disease severity, and enteral nutrition (EN) formula use in relation to feeding intolerance and healthcare resource utilization.

A retrospective, cross-sectional design using real-world data from PINC AI™ Healthcare Database, 2015-2019 was used. Critically ill hospitalized adults who required ≥3 days of 100% whey peptide-based EN, other peptide-based diets, or intact-protein standard and diabetic EN formulas were included. Primary outcomes were enteral feeding intolerance, including GI intolerance and hyperglycemia. Pairwise comparisons of other peptide-based and standard intact-protein groups with 100% whey-peptide were completed. Associations between EN group with GI intolerance and hyperglycemia, respectively, were evaluated via multivariable logistic regressions.

Across 67 US hospitals, 19,679 inpatients (3242,100% whey-peptide, 3121 other peptide-based, and 13,316 standard intact-protein) were included. The 100% whey-peptide group had higher severity of illness and frequencies of comorbidities compared with other peptide-based and standard intact-protein groups. Hospital length of stay, intensive care unit stay, and 30-day readmission were similar across peptide-based cohorts. After controlling for demographic, visit, and severity characteristics, odds of GI intolerance were 18% higher for the other peptide-based group and 15% higher for the standard intact-protein group compared with the 100% whey-peptide group (each P < 0.03). In secondary analysis, odds of hyperglycemia were 81% higher for the other peptide-based group compared with the subgroup of very high-protein/low carbohydrate 100% whey-peptide (P < 0.001).

Lower GI intolerance and greater glycemic control were associated with the use of 100% whey-peptide formulas relative to other formulas. Appropriate and optimal delivery of EN using specialized peptide-based formulas is a strategy to minimize feeding intolerance and benefit critically ill patients.

Data on hyperglycemia and glucose variability in relation to diabetes mellitus, either known or unknown in ICU-setting in COVID-19, are scarce. We prospectively studied daily glucose variables and mortality in strata of diabetes mellitus and glycosylated hemoglobin among mechanically ventilated COVID-19 patients.

We used linear-mixed effect models in mechanically ventilated COVID-19 patients to investigate mean and maximum difference in glucose concentration per day over time. We compared ICU survivors and non-survivors and tested for effect-modification by pandemic wave 1 and 2, diabetes mellitus, and admission HbA1c.

Among 232 mechanically ventilated COVID-19 patients, 21.1% had known diabetes mellitus, whereas 16.9% in wave 2 had unknown diabetes mellitus. Non-survivors had higher mean glucose concentrations (ß 0.62 mmol/l; 95%CI 0.20-1.06; ß 11.2 mg/dl; 95% CI 3.6-19.1; P = 0.004) and higher maximum differences in glucose concentrations per day (ß 0.85 mmol/l; 95%CI 0.37-1.33; ß 15.3; 95%CI 6.7-23.9; P = 0.001). Effect modification by wave, history of diabetes mellitus and admission HbA1c in associations between glucose and survival was not present. Effect of higher mean glucose concentrations was modified by pandemic wave (wave 1 (ß 0.74; 95% CI 0.24-1.23 mmol/l) ; (ß 13.3; 95%CI 4.3-22.1 mg/dl)) vs. (wave 2 (ß 0.37 (95%CI 0.25-0.98) mmol/l) (ß 6.7 (95% ci 4.5-17.6) mg/dl)).

Hyperglycemia and glucose variability are associated with mortality in mechanically ventilated COVID-19 patients irrespective of the presence of diabetes mellitus.

Traditionally, the workflow surrounding a general surgery patient allows for a period of evaluation and optimization of underlying medical issues to allow for risk modification; however, in the emergency, this optimization period is largely condensed because of its time-dependent nature. Because the lack of optimization can lead to complications, the ability to rapidly resuscitate the patient, proceed to procedural intervention to control the situation, and manage common medical comorbidities is paramount. This article provides an overview on these subjects.

Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU).

Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency.

We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings.

We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.

Existing results regarding the usage of glycemic control in critically ill patients for reduced morbidity and mortality have been based on clinical studies but could not be reproduced in large prospective studies. Current guidelines for glycemic control suggest a target blood glucose of 140-180 mg/dL, with lower targets being appropriate for some patients. The current study aims to provide additional evidence to this area, through the usage of real-world retrospective data of everyday clinical practice. We have used the large, credentialed access database MIMIC-IV to assess the effect of glycemic control to patient mortality. Glycemic control has been characterized by the percentage of time that the glucose measurements fall within pre-specified glucose bands. Results from logistic regression and survival analysis are reported, along with visualizations based on methods from the machine learning literature, which all suggest that increased time in low and high glucose values is related to increased ICU mortality and decreased survival.

Randomized, controlled trials have shown both benefit and harm from tight blood-glucose control in patients in the intensive care unit (ICU). Variation in the use of early parenteral nutrition and in insulin-induced severe hypoglycemia might explain this inconsistency.

We randomly assigned patients, on ICU admission, to liberal glucose control (insulin initiated only when the blood-glucose level was >215 mg per deciliter [>11.9 mmol per liter]) or to tight glucose control (blood-glucose level targeted with the use of the LOGIC-Insulin algorithm at 80 to 110 mg per deciliter [4.4 to 6.1 mmol per liter]); parenteral nutrition was withheld in both groups for 1 week. Protocol adherence was determined according to glucose metrics. The primary outcome was the length of time that ICU care was needed, calculated on the basis of time to discharge alive from the ICU, with death accounted for as a competing risk; 90-day mortality was the safety outcome.

Of 9230 patients who underwent randomization, 4622 were assigned to liberal glucose control and 4608 to tight glucose control. The median morning blood-glucose level was 140 mg per deciliter (interquartile range, 122 to 161) with liberal glucose control and 107 mg per deciliter (interquartile range, 98 to 117) with tight glucose control. Severe hypoglycemia occurred in 31 patients (0.7%) in the liberal-control group and 47 patients (1.0%) in the tight-control group. The length of time that ICU care was needed was similar in the two groups (hazard ratio for earlier discharge alive with tight glucose control, 1.00; 95% confidence interval, 0.96 to 1.04; P = 0.94). Mortality at 90 days was also similar (10.1% with liberal glucose control and 10.5% with tight glucose control, P = 0.51). Analyses of eight prespecified secondary outcomes suggested that the incidence of new infections, the duration of respiratory and hemodynamic support, the time to discharge alive from the hospital, and mortality in the ICU and hospital were similar in the two groups, whereas severe acute kidney injury and cholestatic liver dysfunction appeared less prevalent with tight glucose control.

In critically ill patients who were not receiving early parenteral nutrition, tight glucose control did not affect the length of time that ICU care was needed or mortality. (Funded by the Research Foundation-Flanders and others; TGC-Fast ClinicalTrials.gov number, NCT03665207.).

The aim of this study was to develop and validate a machine learning-based predictive model that predicts 90-day mortality in ICU trauma patients.

Data of patients with severe trauma were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The performances of mortality prediction models generated using nine machine learning extreme gradient boosting (XGBoost), logistic regression, random forest, AdaBoost, multilayer perceptron (MLP) neural networks, support vector machine (SVM), light gradient boosting machine (GBM), k nearest neighbors (KNN) and gaussian naive bayes (GNB). The performance of the model was evaluated in terms of discrimination, calibration and clinical application.

We found that the accuracy, sensitivity, specificity, PPV, NPV and F1 score of our proposed XGBoost model were 82.8%, 79.7%, 77.6%, 51.2%, 91.5% and 0.624, respectively. Among the nine models, the XGBoost model performed best. Compared with traditional logistic regression, the calibration curves of the XGBoost model and decision curve analysis (DCA) performed well.

Our study shows that the XGBoost model outperforms other machine learning models in predicting 90-day mortality in trauma patients. It can be used to assist clinicians in the early identification of mortality risk factors and early intervention to reduce mortality.

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.

Aneurysmal subarachnoid hemorrhage (aSAH) necessitating mechanical ventilation (MV) presents a serious challenge for intensivists. Laboratory blood tests reflect individual physiological and biochemical states, and provide a useful tool for identifying patients with critical condition and stratifying risk levels of death. This study aimed to determine the prognostic role of initial routine laboratory blood tests in these patients.

This retrospective cohort study included 190 aSAH patients requiring MV in the neurosurgical intensive care unit from December 2019 to March 2022. Follow-up evaluation was performed in May 2022 via routine outpatient appointment or telephone interview. The primary outcomes were death occurring within 7 days after discharge (short-term mortality) or reported at time of follow-up (long-term mortality). Clinico-demographic and radiological characteristics, initial routine laboratory blood tests (e.g., metabolic panels and arterial blood gas analysis), and treatment were analyzed and compared in relation to mortality. Multivariable logistic and Cox regression analyses, with adjustment of other clinical predictors, were performed to determine independent laboratory test predictors for short- and long-term mortality, respectively.

The patients had a median age of 62 years, with a median World Federation of Neurosurgical Societies grade (WFNS) score of 5 and a median modified Fisher grade (mFisher) score of 4. The short- and long-term mortality of this cohort were 60.5% and 65.3%, respectively. Compared with survivors, non-survivors had more severe disease upon admission based on neurological status and imaging features and a shorter disease course, and were more likely to receive conservative treatment. Initial ionized calcium was found to be independently associate with both short-term [adjusted odds ratio (OR): 0.92; 95% confidence interval (CI): 0.86 to 0.99; P=0.020] and long-term mortality [adjusted hazard ratio (HR): 0.95; 95% CI: 0.92 to 0.99; P=0.010], after adjusting for potential confounders. Moreover, the admission glucose level was found to be associated only with short-term mortality (adjusted OR: 1.19; 95% CI: 1.06 to 1.34; P=0.004).

Laboratory screening may provide a useful tool for the management of aSAH patients requiring MV in stratifying risk levels for mortality and for better clinical decision-making. Further study is needed to validate the effects of calcium supplementation and glucose-lowering therapy on the outcomes in this disease.

Acute hyperglycemia has been identified as a risk factor for acute kidney injury occurrence and mortality in various diseases. The aim of the current study was to investigate the relationship between stress-induced hyperglycemia and adverse outcomes in critically ill patients with AKI.

We extracted clinical data from Multiparameter Intelligent Monitoring in Intensive Care III version 1.4. Blood glucose and glycosylated hemoglobin during the first 24 h of ICU admission were used to calculate glycemic gap and stress hyperglycemia ratio (SHR). The outcomes included ICU mortality and need for renal replacement therapy. The association of the glycemic gap and SHR with outcomes were determined via logistic regression model and receiver-operating curves. The subgroup analysis of patients with and without diabetes was performed separately.

Higher glycemic gap and SHR were observed in patients who had increased need of RRT, higher mortality rates and longer ICU stay. Multivariate analysis demonstrated that higher glycemic gap (OR 1.01, 95%CI 1.00-1.02, P = 0.015), as well as SHR (OR 1.32; 95%CI 1.07-1.64, P = 0.009), were independently associated with ICU mortality after adjusting for potential covariates. In subgroup analysis, the association of glycemic gap and SHR were only significant in the non-diabetic population as for the outcome of ICU mortality (OR 2.25, 95%CI 1.64-3.08, P < 0.001 and OR 1.99; 95%CI 1.46-2.72, P < 0.001, respectively).

The glycemic gap and SHR might serve as a potential prognostic indicator of ICU mortality in critically ill patients with AKI, especially in the non-diabetic population.