Glycemic control is critical for managing transcatheter aortic valve replacement (TAVR) patients, especially those in intensive care units (ICUs). Emerging metrics such as the hemoglobin glycation index (HGI), stress hyperglycemia ratio (SHR), and glycemic variability (GV) offer advanced insights into glucose metabolism. However, their prognostic implications for short- and long-term outcomes post-TAVR remain underexplored.

This retrospective cohort study analyzed 3342 ICU-admitted TAVR patients via the MIMIC-IV database. Patients were stratified into tertiles for HGI, SHR, and GV levels. Survival analyses, including Kaplan‒Meier curves, Cox proportional hazards models and restricted cubic splines (RCSs), were used to assess associations between glycemic control metrics and 30-day and 365-day all-cause mortality in these patients. Sensitivity analyses, subgroup assessments, and external validation were also performed to verify the study findings.

During follow-up, 1.6% and 6.9% of patients experienced 30-day and 365-day mortality after TAVR, respectively. In the fully adjusted cox regression model, lower HGI (HR 1.48, 95% CI 1.05-2.09, P = 0.025) and higher SHR (HR 1.63, 95% CI 1.15-2.32, P = 0.006) were most significantly associated with an increased risk of 365-day mortality. Higher SHR was also significantly associated with an increased risk of 30-day mortality in patients (HR 2.92, 95% CI 1.32-6.45, P = 0.008). Both lower (HR 0.59, 95% CI 0.38-0.92, P = 0.019) and higher GV levels (HR 1.43, 95% CI 1.06-1.93, P = 0.020) were associated with the risk of 365-day mortality.

In critically ill TAVR patients, glycemic control metrics are closely associated with long-term all-cause mortality. The HGI, SHR, and GV provide prognostic insights into clinical outcomes that surpass conventional glucose measurements. These findings highlight the importance of personalized glycemic management strategies in improving TAVR patient outcomes.

Approximately 38% of the patient population in community hospitals throughout the United States has diabetes mellitus. Hypoglycemia is defined as less than 70 mg/dL. Patients with hypoglycemia are at more risk for clinically adverse outcomes including death. Critical care nurses must recognize the signs and symptoms of hypoglycemia and initiate prompt intervention to reduce the risk of mortality.

Introduction: Glycemia is an important factor among critically ill patients in the intensive care unit (ICU). There is conflicting evidence on the preferred strategy of blood glucose control among patients with diabetes in the ICU. We aimed to conduct a meta-analysis comparing tight with liberal blood glucose in critically ill patients with diabetes in the ICU. Methods: We systematically searched PubMed, Embase, and Cochrane Central for randomized controlled trials (RCTs) comparing tight versus liberal blood glucose control in critically ill patients with diabetes from inception to December 2023. We pooled odds-ratios (OR) and 95% confidence intervals (CI) with a random-effects model for binary endpoints. We used the Review Manager 5.17 and R version 4.3.2 for statistical analyses. Risk of bias assessment was performed with the Cochrane tool for randomized trials (RoB2). Results: Eight RCTs with 4474 patients were included. There was no statistically significant difference in all-cause mortality (OR 1.11; 95% CI 0.95-1.28; P = .18; I² = 0%) between a tight and liberal blood glucose control. RoB2 identified all studies at low risk of bias and funnel plot suggested no evidence of publication bias. Conclusion: In patients with diabetes in the ICU, there was no statistically significant difference in all-cause mortality between a tight and liberal blood glucose control. PROSPERO registration: CRD42023485032.

Hypoglycemia in critical care is a well-documented phenomenon, linking both physiological and clinical evidence to harmful outcomes and an increased risk of mortality. Its implications span medical and non-medical consequences, such as cardiovascular and cerebrovascular complications, and escalated health care expenses and hospitalization duration. Mitigation measures for modifiable risk factors and education for both patients and health care providers on hypoglycemia can effectively prevent the onset of inpatient hypoglycemia. This concise clinical review offers a brief overview of hypoglycemia in critically ill patients, encompassing its pathophysiology, etiology, diagnosis, management, and prevention.

The current study sets out to develop and validate a robust machine-learning model utilizing electronic health records (EHR) to forecast the risk of hypoglycemia among ICU patients in Jordan.

The present study drew upon a substantial cohort of 13,567 patients admitted 26,248 times to the intensive care unit (ICU) over 10 years from July 2012 to July 2022. The primary outcome of interest was the occurrence of any hypoglycemic episode during the patient's ICU stay. Developing and testing predictor models was conducted using Python machine-learning libraries.

A total of 1,896 were eligible to participate in the study, 206 experienced at least one hypoglycemic episode. Eight machine-learning models were trained to predict hypoglycemia. All models showed predicting power with a range of 74.53-99.69 for AUROC. Except for Naive Bayes, the six remaining models performed distinctly better than the basic logistic regression usually used for prediction in epidemiological studies. CatBoost model was consistently the best performer with the highest AUROC (0.99), accuracy and precision, sensitivity and specificity, and recall.

We used machine learning to anticipate the likelihood of hypoglycemia, which can significantly decrease hypoglycemia incidents and enhance patient outcomes.

Diabetes mellitus (DM) is relatively common following acute pancreatitis (AP), even after mild acute pancreatitis (MAP), the most frequent AP presentation, in which there is no overt beta cell injury. Post-AP related diabetes is widely misdiagnosed, resulting in potentially inappropriate treatment and worse outcomes than type 2 diabetes (T2D). Thus, it is important to understand risk across the spectrum of AP severity.

Biological mechanisms are unclear and may include local and systemic inflammation leading to beta cell dysfunction and insulin resistance, altered gut barrier and/or gut peptides and possibly islet autoimmunity, though no studies have specifically focused on MAP. While studies examining clinical risk factors on MAP exclusively are lacking, there are studies which include MAP. These studies vary in scientific rigor, approaches to rule out preexisting diabetes, variable AP severity, diagnostic testing methods, and duration of follow-up. Overall, disease related factors, including AP severity, as well as established T2D risk factors are reported to contribute to the risk for DM following AP.

Though numerous studies have explored risk factors for DM after AP, few studies specifically focused on MAP, highlighting a key knowledge gap that is relevant to the majority of patients with AP.

Intensive care unit-acquired weakness (ICU-AW; ICD-10 Code: G72.81) is a syndrome of generalized weakness described as clinically detectable weakness in critically ill patients with no other credible cause. The risk factors for ICU-AW include hyperglycemia, parenteral nutrition, vasoactive drugs, neuromuscular blocking agents, corticosteroids, sedatives, some antibiotics, immobilization, the disease severity, septicemia and systemic inflammatory response syndrome, multiorgan failure, prolonged mechanical ventilation (MV), high lactate levels, older age, female sex, and pre-existing systemic morbidities. There is a definite association between the duration of ICU stay and MV with ICU-AW. However, the interpretation that these are modifiable risk factors influencing ICU-AW, appears to be flawed, because the relationship between longer ICU stays and MV with ICU-AW is reciprocal and cannot yield clinically meaningful strategies for the prevention of ICU-AW. Prevention strategies must be based on other risk factors. Large multicentric randomized controlled trials as well as meta-analysis of such studies can be a more useful approach towards determining the influence of these risk factors on the occurrence of ICU-AW in different populations.

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.

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.