Age moderates the relationships between obesity, glucose variability, and intensive care unit mortality: a retrospective cohort study

Association of glycemic variability with death and severe consciousness disturbance among critically ill patients with cerebrovascular disease: analysis of the MIMIC-IV database

BACKGROUND

The association of glycemic variability with severe consciousness disturbance and in-hospital all-cause mortality in critically ill patients with cerebrovascular disease (CVD) remains unclear, This study aimed to investigate the association of glycemic variability with cognitive impairment and in-hospital death.

METHOD

We extracted all blood glucose measurements of patients diagnosed with CVD from the Medical Information Mart for Intensive Care IV (MIMIC-IV). Glycemic variability was defined as the coefficient of variation (CV), which was determined using the ratio of standard deviation and the mean blood glucose levels. Cox hazard regression models were applied to analyze the link between glycemic variability and outcomes. We also analyzed non-linear relationship between outcome indicators and glycemic variability using restricted cubic spline curves.

RESULTS

The present study included 2967 patients diagnosed with cerebral infarction and 1842 patients diagnosed with non-traumatic cerebral hemorrhage. Log-transformed CV was significantly related to cognitive impairment and in-hospital mortality, as determined by Cox regression. Increasing log-transformed CV was approximately linearly with the risk of cognitive impairment and in-hospital mortality.

CONCLUSION

High glycemic variability was found to be an independent risk factor for severe cognitive decline and in-hospital mortality in critically ill patients with CVD. Our study indicated that enhancing stability of glycemic variability may reduced adverse outcomes in patients with severe CVD.

Glycemic variability and in-hospital death of critically ill patients and the role of ventricular arrhythmias

BACKGROUND

Abnormal glycemic variability is common in the intensive care unit (ICU) and is associated with increased in-hospital mortality and major adverse cardiovascular events, but little is known about whether adverse outcomes are partly mediated by ventricular arrhythmias (VA). We aimed to explore the association between glycemic variability and VA in the ICU and whether VA related to glycemic variability mediate the increased risk of in-hospital death.

METHODS

We extracted all measurements of blood glucose during the ICU stay from The Medical Information Mart for Intensive Care IV (MIMIC-IV) database version 2.0. Glycemic variability was expressed by the coefficient of variation (CV), which was calculated by the ratio of standard deviation (SD) and average blood glucose values. The outcomes included the incidence of VA and in-hospital death. The KHB (Karlson, KB & Holm, A) is a method to analyze the mediation effect for nonlinear models, which was used to decompose the total effect of glycemic variability on in-hospital death into a direct and VA-mediated indirect effect.

RESULTS

Finally, 17,756 ICU patients with a median age of 64 years were enrolled; 47.2% of them were male, 64.0% were white, and 17.8% were admitted to the cardiac ICU. The total incidence of VA and in-hospital death were 10.6% and 12.8%, respectively. In the adjusted logistic model, each unit increase in log-transformed CV was associated with a 21% increased risk of VA (OR 1.21, 95% CI: 1.11-1.31) and a 30% increased risk (OR 1.30, 95% CI: 1.20-1.41) of in-hospital death. A total of 3.85% of the effect of glycemic variability on in-hospital death was related to the increased risk of VA.

CONCLUSION

High glycemic variability was an independent risk factor for in-hospital death in ICU patients, and the effect was caused in part by an increased risk of VA.

Modifiable predictive factors and all-cause mortality in the non-hospitalized elderly population: An umbrella review of meta-analyses

OBJECTIVE

This umbrella review aimed to summarize the association between modifiable predictive factors and all-cause mortality in the non-hospitalized elderly population, and estimated the credibility and strength of the current evidence.

METHODS

PubMed, Embase, Web of science, and EBSCOhost were searched up to February 28, 2022. Random-effect summary effect sizes and 95% confidence intervals (CIs), heterogeneity, small-study effect, excess significance bias, as well as 95% prediction intervals (PIs) were calculated. Methodological quality was assessed with the Assessment of Multiple Systematic Reviews 2 (AMSTAR-2) tool. The credibility of the included meta-analyses was graded from convincing to weak using established criteria. This umbrella review was registered with PROSPERO, CRD 42021282183.

RESULTS

In total, 32 predictive factors involving 49 associations extracted from 35 meta-analyses were analyzed. Forty-three of the 49 (87.8%) associations presented nominal significant effects by the random-effect model (P < 0.05), of which 34 had harmful associations and nine had beneficial associations with all-cause mortality. Frailty (FRAIL scale), low short physical performance battery (SPPB) score, and fewer daily steps carried a more than three-fold risk for all-cause mortality. Convincing evidence showed that weight fluctuation, prefrailty and frailty status, sarcopenia, low SPPB score, fewer daily steps, and fatigue increased the risk of all-cause mortality, while daily moderate-to-vigorous physical activity (MVPA) duration and total physical activity participation reduced the risk of death. There were twenty, nine, five, and six associations that yielded highly suggestive, suggestive, weak, and non-significant grades of evidence. Thirty-four (69.4%) of the associations exhibited significant heterogeneity. Twenty-two associations presented 95% PIs excluding the null value, two indicated small-study effects, and three had evidence for excess significance bias, respectively. The methodological quality of most meta-analyses was rated as low (37.1%) or critically low (42.9%).

CONCLUSIONS

A summary of the currently available meta-analyses suggests that a broad range of modifiable predictive factors are significantly associated with all-cause mortality risk in the non-hospitalized elderly population. The most credible evidence indicates that physical function represented by frailty and sarcopenia, as well as physical activity, are significant predictors for all-cause mortality. This umbrella review may provide prognostic information to direct appropriate diagnostic evaluation and treatment goals in the future. More solid evidence is still needed coming from moderate-to-high quality meta-analyses.