Degree of hyperglycemia independently associates with hospital mortality and length of stay in critically ill, nondiabetic patients: Results from the ANZICS CORE binational registry

Postoperative Length of Stay in Patients With Stress Hyperglycemia Compared to Patients With Diabetic Hyperglycemia: A Retrospective Cohort Study

BACKGROUND

Postoperative hospital length of stay (LOS) is longer in patients with diabetes than in patients without diabetes. Stress hyperglycemia (SH) in patients without a history of diabetes has been associated with adverse postoperative outcomes. The effect of SH on postoperative LOS is uncertain. The aim of this study is to compare postoperative LOS in patients with SH to patients with diabetic hyperglycemia (DH) following noncardiac surgery.

METHODS

We carried out a retrospective cohort study of inpatients with at least two glucose measurements ≥180 mg/dL. Two groups were compared. Patients with SH had no preoperative history of diabetes. Patients were considered to have DH if they had an established preoperative diagnosis of diabetes mellitus or a preoperative hemoglobin A1c (HbA1c) ≥6.5%. The primary outcome measure was hospital LOS.

RESULTS

We included 270 patients with postoperative hyperglycemia-82 in the SH group and 188 in the DH group. In a linear regression analysis, hospital LOS was longer in the SH group than in the DH group (10.4 vs 7.3 days; P = .03). Within the SH group, we found no association between LOS and prompt treatment of hyperglycemia within 12 hours (P = .43), insulin dose per day (P = .89), or overall mean glucose (P = .13).

CONCLUSIONS

Postoperative LOS was even longer in patients with SH than in patients with DH, representing a potential target for quality improvement efforts. We did not, however, find evidence that improved treatment of SH was associated with reduction in LOS.

Hyperglycaemia and Its Prognostic Value in Patients with COVID-19 Admitted to the Hospital in Lithuania

BACKGROUND AND OBJECTIVES

Increased blood glucose levels atadmission are frequently observed in COVID-19 patients, even in those without pre-existing diabetes. Hyperglycaemia is associated with an increased incidence of severe COVID-19 infection. The aim of this study was to evaluate the association between hyperglycaemia at admission with the need for invasive mechanical ventilation (IMV) and in-hospital mortality in patients without diabetes who were hospitalized for COVID-19 infection.

MATERIALS AND METHODS

This retrospective observational study was conducted at Vilnius University Hospital Santaros Clinics, Lithuania with adult patients who tested positive for severe acute respiratory syndrome coronavirus 2 SARS-CoV-2 and were hospitalized between March 2020 and May 2021. Depersonalized data were retrieved from electronic medical records. Based on blood glucose levels on the day of admission, patients without diabetes were divided into 4 groups: patients with hypoglycaemia (blood glucose below 4.0 mmol/L), patients with normoglycaemia (blood glucose between ≥4.0 mmol/L and <6.1 mmol/L), patients with mild hyperglycaemia (blood glucose between ≥6.1 mmol/L and <7.8 mmol/L), and patients with intermittent hyperglycaemia (blood glucose levels ≥7.8 mmol/L and <11.1 mmol/L). A multivariable binary logistic regression model was created to determine the association between hyperglycaemia and the need for IMV. Survival analysis was performed to assess the effect of hyperglycaemia on outcome within 30 days of hospitalization.

RESULTS

Among 1945 patients without diabetes at admission, 1078 (55.4%) had normal glucose levels, 651 (33.5%) had mild hyperglycaemia, 196 (10.1%) had intermittent hyperglycaemia, and 20 (1.0%) had hypoglycaemia. The oddsratio (OR) for IMV in patients with intermittent hyperglycaemia was 4.82 (95% CI 2.70-8.61, p < 0.001), and the OR was 2.00 (95% CI 1.21-3.31, p = 0.007) in those with mild hyperglycaemia compared to patients presenting normal glucose levels. The hazardratio (HR) for 30-day in-hospital mortality in patients with mild hyperglycaemia was 1.62 (95% CI 1.10-2.39, p = 0.015), while the HR was 3.04 (95% CI 2.01-4.60, p < 0.001) in patients with intermittent hyperglycaemia compared to those with normoglycaemia at admission.

CONCLUSIONS

In COVID-19 patients without pre-existing diabetes, the presence of hyperglycaemia at admission is indicative of COVID-19-induced alterations in glucose metabolism and stress hyperglycaemia. Hyperglycaemia at admission in COVID-19 patients without diabetes is associated with an increased risk of invasive mechanical ventilation and in-hospital mortality. This finding highlights the importance for clinicians to carefully consider and select optimal support and treatment strategies for these patients. Further studies on the long-term consequences of hyperglycaemia in this specific population are warranted.

Stress-Induced Hyperglycemia is a Risk Factor for Surgical-Site Infections in Nondiabetic Patients with Open Leg Fractures

Background

Nondiabetic patients with open leg fractures who have elevated blood glucose levels on arrival in the emergency department have an increased risk of surgical-site infections (SSIs).

Objective

This study evaluates the association between the incidence of SSIs in nondiabetic patients with an open leg fracture and blood glucose levels registered on arrival in the ER. We also analyzed the correlation between patients' days of hospital stay and the incidence of SSIs and the time elapsed between the damage control with external fixation and final fixation and the incidence of SSI.

Methods

We retrospectively studied nondiabetic patients admitted to our emergency unit from 2017 to 2021 with a diagnosis of open leg fracture consecutively treated. Based on the diagnosis of SSIs, all enrolled patients were divided into two groups based on the developed (group A) or not developed (group B) SSIs within 1 year after surgery. All patients enrolled in the study underwent damage control within 24 hours after admission to the ER. At stabilization of general clinical and local wound conditions, all patients underwent definitive surgery.

Results

We enrolled 80 patients. In group A, glycemia on arrival in the ER was on average 148.35 ± 19.59 mg/dl, and in group B, it was 122.61 ± 22.22 mg/dl (p value: 0.0001). In group A, glycemia in the first postoperative day was on average 113.81 ± 21.07 mg/dl, and in group B, it was 99.02 ± 17.60 mg/dl (p value: 0.001). In group A, the average hospitalization was 57.92 ± 42.43 days, and in group B, it was 18.41 ± 14.21 days (p value: 0.01). Through Youden's J, we therefore analyzed the value with the highest sensitivity and specificity which proved to be 132 mg/dl.

Conclusion

Our findings show that nondiabetic patients with SIH have a significantly increased risk of SSIs compared to patients without SIH within 1 year after surgery. Patients with open leg fractures with SIH have a significantly higher average hospital stay than patients without SIH. Further studies are needed to confirm 132 mg/dl of blood glucose levels as a value to stratify the risk of SSIs in these patients.

Prognostic significance of the stress hyperglycemia ratio in critically ill patients

BACKGROUND

The stress hyperglycemia ratio (SHR) has demonstrated a noteworthy association with unfavorable cardiovascular clinical outcomes and heightened in-hospital mortality. Nonetheless, this relationship in critically ill patients remains uncertain. This study aims to elucidate the correlation between SHR and patient prognosis within the critical care setting.

METHODS

A total of 8978 patients admitted in intensive care unit (ICU) were included in this study. We categorized SHR into uniform groups and assessed its relationship with mortality using logistic or Cox regression analysis. Additionally, we employed the restricted cubic spline (RCS) analysis method to further evaluate the correlation between SHR as a continuous variable and mortality. The outcomes of interest in this study were in-hospital and 1-year all-cause mortality.

RESULTS

In this investigation, a total of 825 (9.2%) patients experienced in-hospital mortality, while 3,130 (34.9%) individuals died within the 1-year follow-up period. After adjusting for confounding variables, we identified a U-shaped correlation between SHR and both in-hospital and 1-year mortality. Specifically, within the SHR range of 0.75-0.99, the incidence of adverse events was minimized. For each 0.25 increase in the SHR level within this range, the risk of in-hospital mortality rose by 1.34-fold (odds ratio [OR]: 1.34, 95% CI: 1.25-1.44), while a 0.25 decrease in SHR within 0.75-0.99 range increased risk by 1.38-fold (OR: 1.38, 95% CI: 1.10-1.75).

CONCLUSION

There was a U-shaped association between SHR and short- and long-term mortality in critical ill patients, and the inflection point of SHR for poor prognosis was identified at an SHR value of 0.96.

Stress-induced Hyperglycemia Ratio as an Independent Risk Factor of In-hospital Mortality in Nonresuscitation Intensive Care Units: A Retrospective Study

PURPOSE

To determine whether the stress-induced hyperglycemia ratio (SHR) is independently associated with in-hospital mortality in critically ill patients in nonresuscitation ICUs.

METHODS

In this retrospective cohort study, clinical- and laboratory-related data from patients first admitted to nonresuscitation ICUs were extracted from an open-access database of >50,000 ICU admissions. Patients were assigned to one of two groups according to an SHR threshold of 1.1. The primary end point of this study was the in-hospital mortality rate. The associations between SHR and length of stay in the ICU and hospital, duration of mechanical ventilation use, and vasopressor use were secondary end points. Logistic regression models were established in the analysis of in-hospital mortality risk, and areas under the receiver operating characteristic curve (AUC) were analyzed to investigate the association between the primary end point and SHR used alone or together with the Simplified Acute Physiology Scale (SAPS) II score. The Youden index, specificity, and sensitivity of SHR and SAPS-II were also assessed.

FINDINGS

In this study, 1859 patients were included, 187 of whom (10.06%) died during hospitalization. The group with an SHR of ≥1.1 had a greater in-hospital mortality rate (13.7% vs 7.4%; P < 0.001), longer length of stay both in the ICU and in the hospital, a longer duration of mechanical ventilation use, and a greater rate of vasopressor use. On adjustment for multivariate risk, a 0.1-point increment in SHR was significantly associated with in-hospital mortality (OR = 1.08; 95% CI, 1.00-1.16; P = 0.036). The AUC of the association between risk and the SAPS-II score was significantly greater than that with SHR (0.797 [95% CI, 0.576-0.664] vs 0.620 [95% CI, 0.764-0.830]; P < 0.001). The AUC with SAPS-II + SHR was significantly greater than that with SAPS-II used alone (0.802 [95% CI, 0.770-0.835] vs 0.797 [95% CI, 0.764-0.830]; P = 0.023). The Youden index, specificity, and sensitivity of SAPS-II + SHR were 0.473, 0.703, and 0.770, respectively.

IMPLICATIONS

Stress-induced hyperglycemia, as evaluated using the SHR, was associated with increased in-hospital mortality and worse clinical outcomes in these critically ill patients in nonresuscitation ICUs. SHR was an independent risk factor for in-hospital mortality, and when used together with the SAPS-II, added to the capacity to predict mortality in these patients in nonresuscitation ICUs. Prospective data are needed to validate the capacity of SHR in predicting in-hospital mortality in patients in the nonresuscitation ICU.

Relationship between stress hyperglycemia ratio and allcause mortality in critically ill patients: Results from the MIMIC-IV database

BACKGROUND

Stress hyperglycemia ratio (SHR) was developed to reduce the impact of long-term chronic glycemic factors on stress hyperglycemia levels, which have been linked to clinical adverse events. However, the relationship between SHR and the short- and long-term prognoses of intensive care unit (ICU) patients remains unclear.

METHODS

We retrospectively analyzed 3,887 ICU patients (cohort 1) whose initial fasting blood glucose and hemoglobin A1c data within 24 hours of admission were available and 3,636 ICU patients (cohort 2) who were followed-up for 1-year using the Medical Information Mart for Intensive Care IV v2.0 database. Patients were divided into two groups based on the optimal cutoff value of SHR, which was determined using the receiver operating characteristic (ROC) curve.

RESULTS

There were 176 ICU deaths in cohort 1 and 378 patients experienced all-cause mortality during 1 year of follow-up in cohort 2. The results of logistic regression revealed that SHR was associated with ICU death (odds ratio 2.92 [95% confidence interval 2.14-3.97] P < 0.001), and non-diabetic patients rather than diabetic patients showed an increased risk of ICU death. As per the Cox proportional hazards model, the high SHR group experienced a higher incidence of 1-year all-cause mortality (hazard ratio 1.55 [95% confidence interval 1.26-1.90] P < 0.001). Moreover, SHR had an incremental effect on various illness scores in predicting ICU all-cause mortality.

CONCLUSION

SHR is linked to ICU death and 1-year all-cause mortality in critically ill patients, and it has an incremental predictive value in different illness scores. Moreover, we found that non-diabetic patients, rather than diabetic patients, showed an increased risk of all-cause mortality.

Nurse led protocols for control of glycaemia in critically ill patients: A systematic review

BACKGROUND

Blood glucose control in critically ill patients is challenging and can affect clinical outcomes. Several manual as well as automated approaches have been proposed over the time, however nursing staff still covers the key-role for optimization of glycemia throughout adjustment of insulin infusion and administration.

AIM

Systematic review to compare the efficacy/the effects of nurse led insulin infusion protocols versus standard approaches in patients admitted in the intensive care unit.

METHODS

All relevant studies evaluating nurse directed protocols for insulin administration in critically ill adults. Data was independently extracted and collected through a dedicated electronic form. The following outcomes have been recorded: the number (or percentage) of glycaemia measurements within the target range; the number of hypo- and hyper-glycaemic events, separately; the mean glycaemia; the lowest and highest glycemia values recorded; the time to reach the glycaemia target; the ICU length of stay and the ICU and the long-term (>30 days) mortality. Statistical analysis was conducted on the summary statistics of the selected articles (eg, means, medians, proportions). Unpaired nonparametric continuous data were compared through the Mann-Whitney U-test.

RESULTS

Glycaemic control as well as ICU length of stay and mortality are similar in both patients' groups. Specifically, the group of patients treated with standard modalities include those treated with doctors led protocols, paper charts or software-based approaches.

CONCLUSION

Overall, nurse led insulin protocols can effectively control blood glucose level among critically ill patients.

Metformin: Is it a drug for all reasons and diseases?

Metformin was first used to treat type 2 diabetes in the late 1950s and in 2022 remains the first-choice drug used daily by approximately 150 million people. An accumulation of positive pre-clinical and clinical data has stimulated interest in re-purposing metformin to treat a variety of diseases including COVID-19. In polycystic ovary syndrome metformin improves insulin sensitivity. In type 1 diabetes metformin may help reduce the insulin dose. Meta-analysis and data from pre-clinical and clinical studies link metformin to a reduction in the incidence of cancer. Clinical trials, including MILES (Metformin In Longevity Study), and TAME (Targeting Aging with Metformin), have been designed to determine if metformin can offset aging and extend lifespan. Pre-clinical and clinical data suggest that metformin, via suppression of pro-inflammatory pathways, protection of mitochondria and vascular function, and direct actions on neuronal stem cells, may protect against neurodegenerative diseases. Metformin has also been studied for its anti-bacterial, -viral, -malaria efficacy. Collectively, these data raise the question: Is metformin a drug for all diseases? It remains unclear as to whether all of these putative beneficial effects are secondary to its actions as an anti-hyperglycemic and insulin-sensitizing drug, or result from other cellular actions, including inhibition of mTOR (mammalian target for rapamycin), or direct anti-viral actions. Clarification is also sought as to whether data from ex vivo studies based on the use of high concentrations of metformin can be translated into clinical benefits, or whether they reflect a 'Paracelsus' effect. The environmental impact of metformin, a drug with no known metabolites, is another emerging issue that has been linked to endocrine disruption in fish, and extensive use in T2D has also raised concerns over effects on human reproduction. The objectives for this review are to: 1) evaluate the putative mechanism(s) of action of metformin; 2) analyze the controversial evidence for metformin's effectiveness in the treatment of diseases other than type 2 diabetes; 3) assess the reproducibility of the data, and finally 4) reach an informed conclusion as to whether metformin is a drug for all diseases and reasons. We conclude that the primary clinical benefits of metformin result from its insulin-sensitizing and antihyperglycaemic effects that secondarily contribute to a reduced risk of a number of diseases and thereby enhancing healthspan. However, benefits like improving vascular endothelial function that are independent of effects on glucose homeostasis add to metformin's therapeutic actions.

Stress-Induced Hyperglycemia: Consequences and Management

Hyperglycemia during stress is a common occurrence seen in patients admitted to the hospital. It is defined as a blood glucose level above 180mg/dl in patients without pre-existing diabetes. Stress-induced hyperglycemia (SIH) occurs due to an illness that leads to insulin resistance and decreased insulin secretion. Such a mechanism causes elevated blood glucose and produces a complex state to manage with external insulin. This article compiles various studies to explain the development and consequences of SIH in the critically ill that ultimately lead to an increase in mortality while also discussing the dire impact of SIH on certain acute illnesses like myocardial infarction and ischemic stroke. It also evaluates multiple studies to understand the management of SIH with insulin and proper nutritional therapy in the hospitalized patients admitted to the Intensive care unit (ICU) alongside the non-critical care unit. While emphasizing the diverse effects of improper control of SIH in the hospital, this article elucidates and discusses the importance of formulating a discharge plan due to an increased risk of type 2 diabetes in the recovered. 

Effect of glycemic gap upon mortality in critically ill patients with diabetes

AIMS/INTRODUCTION

Hyperglycemia, hypoglycemia, and blood glucose fluctuation are associated with the outcome in critically ill patients, but the target of blood glucose control is debatable especially in patients with diabetes regarding the status of blood glucose control before admission to ICU. This study aimed to investigate the association between the glycemic gap which is calculated as the mean blood glucose level during the first 7 days after admission to ICU minus the A1C-derived average glucose and the outcome of critically ill patients with diabetes.

METHOD

This study was undertaken in two intensive care units (ICUs) with a total of 30 beds. Patients with diabetes who were expected to stay for more than 24 h were enrolled, the HbA1c was tested within 3 days after admission and converted to the A1C-derived average glucose (ADAG) by the equation: ADAG = [(HbA1c * 28.7) - 46.7 ] * 18-1 , arterial blood glucose measurements were four per day routinely during the first 7 days after admission, the APACHE II score within the first 24 h, the mean blood glucose level (MGL), standard deviation (SD), and coefficient of variation (CV) during the first 7 days were calculated for each person, the GAPadm and GAPmean were calculated as the admission blood glucose and MGL minus the ADAG, respectively, the incidence of moderate hypoglycemia (MH) and severe hypoglycemia (SH), the total dosage of glucocorticoids and average daily dosage of insulin within 7 days, the duration of renal replacement therapy (RRT), ventilator-free hours, and non-ICU stay days within 28 days were also collected. The enrolled patients were divided into a survival group and a nonsurvival group according to survival or not at 28 days and 1 year after admission, and the relationship between parameters derived from blood glucose and mortality in the enrolled critically ill patients was explored.

RESULTS

Five hundred and two patients were enrolled and divided into a survival group (n = 310) and a nonsurvival group (n = 192). It was shown that the two groups had a comparable level of HbA1c, the nonsurvivors had a greater APACHE II, MGL, SD, CV, GAPadm , GAPmean , and a higher incidence of hypoglycemia. A lesser duration of ventilator-free, non-ICU stay, and a longer duration of RRT were recorded in the nonsurvival group, who received a lower carbohydrate intake, a higher daily dosage of insulin and glucocorticoid. GAPmean had the greatest predictive power with an AUC of 0.820 (95%CI: 0.781-0.850), the cut-off value was 3.60 mmol/L (sensitivity 78.2% and specificity 77.3%). Patients with a low GAPmean tended to survive longer than the high GAPmean group 1 year after admission.

CONCLUSIONS

Glycemic GAP between the mean level of blood glucose within the first 7 days after admission to ICU and the A1C-derived average glucose was independently associated with a 28 day mortality of critically ill patients with diabetes, the predictive power extended to 1 year. The incidence of hypoglycemia was associated with mortality either.

Absence of Stress Hyperglycemia Indicates the Most Severe Form of Blunt Liver Trauma

Background: Stress hyperglycemia is common in trauma patients. Increasing injury severity and hemorrhage trigger hepatic gluconeogenesis, glycogenolysis, peripheral and hepatic insulin resistance. Consequently, we expect glucose levels to rise with injury severity in liver, kidney and spleen injuries. In contrast, we hypothesized that in the most severe form of blunt liver injury, stress hyperglycemia may be absent despite critical injury and hemorrhage. Methods: All patients with documented liver, kidney or spleen injuries, treated at a university hospital between 2000 and 2020 were charted. Demographic, laboratory, radiological, surgical and other data were analyzed. Results: A total of 772 patients were included. In liver (n = 456), spleen (n = 375) and kidney (n = 152) trauma, an increase in injury severity past moderate to severe (according to the American Association for the Surgery of Trauma, AAST III-IV) was associated with a concomitant rise in blood glucose levels independent of the affected organ. While stress-induced hyperglycemia was even more pronounced in the most severe forms (AAST V) of spleen (median 10.7 mmol/L, p < 0.0001) and kidney injuries (median 10.6 mmol/L, p = 0.004), it was absent in AAST V liver injuries, where median blood glucose level even fell (5.6 mmol/L, p < 0.0001). Conclusions: Absence of stress hyperglycemia on hospital admission could be a sign of most severe liver injury (AAST V). Blood glucose should be considered an additional diagnostic criterion for grading liver injury.

Association of hyperglycaemia with hospital mortality in nondiabetic COVID-19 patients: A cohort study

Objective

Diabetes is a known risk factor for mortality in Coronavirus disease 2019 (COVID-19) patients. Our objective was to identify prevalence of hyperglycaemia in COVID-19 patients with and without prior diabetes and quantify its association with COVID-19 disease course.

Research design and methods

This observational cohort study included all consecutive COVID-19 patients admitted to John H Stroger Jr. Hospital, Chicago, IL from March 15, 2020 to May 3, 2020 and followed till May 15, 2020. The primary outcome was hospital mortality, and the studied predictor was hyperglycaemia [any blood glucose ≥7.78 mmol/L (140 mg/dL) during hospitalization].

Results

Of the 403 COVID-19 patients studied, 51 (12.7%) died; 335 (83.1%) were discharged while 17 (4%) were still in hospital. Hyperglycaemia occurred in 228 (56.6%) patients; 83 of these hyperglycaemic patients (36.4%) had no prior history of diabetes. Compared to the reference group no-diabetes/no-hyperglycaemia patients the no-diabetes/hyperglycaemia patients showed higher mortality [1.8% versus 20.5%, adjusted odds ratio 21.94 (95% confidence interval 4.04–119.0), P < 0.001]; improved prediction of death (P = 0.01) and faster progression to death (P < 0.01). Hyperglycaemia within the first 24 and 48 h was also significantly associated with mortality (odds ratio 2.15 and 3.31, respectively).

Conclusions

Hyperglycaemia without prior diabetes was common (20.6% of hospitalized COVID-19 patients) and was associated with an increased risk of and faster progression to death. Development of hyperglycaemia in COVID-19 patients who do not have diabetes is an early indicator of progressive disease.

Diabetes, obesity, metabolism, and SARS-CoV-2 infection: the end of the beginning

The increased prevalence of obesity, diabetes, and cardiovascular risk factors in people hospitalized with severe COVID-19 illness has engendered considerable interest in the metabolic aspects of SARS-CoV-2-induced pathophysiology. Here, I update concepts informing how metabolic disorders and their co-morbidities modify the susceptibility to, natural history, and potential treatment of SARS-CoV-2 infection, with a focus on human biology. New data informing genetic predisposition, epidemiology, immune responses, disease severity, and therapy of COVID-19 in people with obesity and diabetes are highlighted. The emerging relationships of metabolic disorders to viral-induced immune responses and viral persistence, and the putative importance of adipose and islet ACE2 expression, glycemic control, cholesterol metabolism, and glucose- and lipid-lowering drugs is reviewed, with attention to controversies and unresolved questions. Rapid progress in these areas informs our growing understanding of SARS-CoV-2 infection in people with diabetes and obesity, while refining the therapeutic strategies and research priorities in this vulnerable population.

[Nutritional support for critically ill patients suffering from SARS-CoV-2 infection]

Patients with severe cases of COVID-19 are at high nutritional risk during their ICU stay. Prolonged immobilization associated with an exacerbated systemic inflammatory response is a major provider of ICU-acquired muscle weakness. Early enteral nutrition is recommended to gradually reach the energy target of 25 kcal/kg/day and protein target of 1.3 g/kg/day around D4. The occurrence of a Refeeding syndrome should be closely monitored. In case of feeding intolerance refractory to a prokinetic treatment, complementary or total parenteral nutrition is advised, favouring new generation mixed lipid emulsions (containing fish oil) and regular monitoring of triglyceridemia. Nutrition care of critically ill patients should be carried out with limited procedures that may pose a risk of contamination for the healthcare staff.