In our impression, this is the first multicenter prospective study of pediatric dysglycemia (including hypoglycemia, hyperglycemia, and glucose variability) conducted in Southwest China that attempts to establish an association between dysglycemia and 28-day mortality in PICUs. We observed several important relationships. Firstly, GV was independently associated with the unfavorable outcome, and it may play a more predictive role in PICU mortality than hyperglycemia and hypoglycemia. These findings above emphasize the important role of glucose variability in children admitted to PICU. Secondly, MAG may be a dominant index reflecting glucose fluctuations, which considered speed and magnitude of change, and the time series among glucose values.
The strong relationship between increasing GV and poor prognosis has been consistently reported in adult patients (6-10,31). Fewer researches have been done on pediatrics, but similar insights have been obtained. In 2006, Wintergerst KA et al (11) retrospectively observed in 1094 PICU admissions in a single center that increased GV was related to increased hospitalization stay and mortality rates, but ignoring severity of the disease. Hirshberg E and his colleagues (22) performed a retrospective study of 863 nondiabetic PICU children during one year period and found that the incidence of glucose fluctuations (defined by having both hyperglycemia and hypoglycemia) was 6.8%, and glucose fluctuations were still associated with high mortality after adjusting for PRISM score. In another retrospective cohort of 101 critically ill children, researchers found that increased GV accompanied by increased mortality (12). Similarly, Bhutia TD et al (21) prospectively observed 170 critically ill children and found that GV was related to multi-organ dysfunction. Pinchefsky EF et al (32) found that GV also was independently relevant to deterioration in brain function in neonates with encephalopathy. Although these studies involve children in a single clinical setting, they add to the evidence that GV is associated with adverse outcomes. In our series, we further prospectively investigated the relationship between GV and deleterious effects in multicenter, and similar results were reported in our findings, that GV was associated with mortality, MODs, and ventilator/ICU-free days, which confirmed the strong predictor role of GV in PICU children. This association may be attributed to oscillatory glucose induction that exacerbates oxidative stress, promotes inflammatory responses, and regulates ROS-mediated NFκB/RAGE activation, resulting in Endothelial cells injury (33-35). Taken together, the above results emphasize the dangerous role of GV in critically ill patients. Management of GV may need to be added to glucose management regimens.
Our data are also complementary to findings from observational studies that evaluated associations between hypo/hyperglycemia and glucose variability during critical illness and mortality. The results of our study suggested that GV may be a more powerful independent risk factor with mortality in PICU patients than hyperglycemia and hypoglycemia. Consistent with our findings, Bagshaw et al (36) found that glucose fluctuation, defined as having both hypoglycemia and hyperglycemia, was relevant to a larger odds ratio of ICU (1.5 vs 1.2 vs 1.0, P<0.05) and hospital (1.4 vs 1.2 vs 1.0, P<0.05) mortality, compared to hypoglycemia only or neither. And several in vitro or animal experiments (37-39) have demonstrated that oscillating glucose aggravated oxidative stress than persistent hyperglycemia, resulting in accelerating cell injury and apoptosis. Quagliaro et al (37) demonstrated that in umbilical vein cells, the levels of protein kinase C-β were increased in the rapid glucose fluctuation group compared to the sustained hyperglycemia group.
From the perspective of clinical treatment, a single blood glucose value is susceptible to various factors such as medication, diet, and stress status. Therefore, hyperglycemia or hypoglycemia reflected by a single value may not accurately reflect the metabolic status of critically ill patients. On the contrary, GV is calculated by a few glucose values and may reveal dynamic glucose changes. Maybe it’s one of the explanations that glucose fluctuation may be a stronger predictor of mortality than hyperglycemia and hypoglycemia to some extent. It is therefore recommended that GV should be included as an important predictor in future studies of prognostic models in critically ill patients and clinicians should pay more attention to dynamic fluctuations than single glucose value.
Different from the previous study, which only took a single index that defined the GV, our study selected four indicators (SD, GLI, MAG, and ACACP). And the results showed most GV indicators had a moderate predicted value for the mortality of PICU children (AUC>0.7, P<0.05). Among the four indices, MAG obtained the largest AUC and was found to be a better reflection of GV than SD. Hermanides’s study (16) illustrated that the glucose profile demonstrated different MAG even in the presence of the same SD value, and MAG was superior to SD in the terms of predicting mortality. Variability should be considered the Speed and magnitude of change, and the time interval between glucose measurements, MAG did, but SD did not. Our retrospective study (17) found that GLI, a variability indicator that also takes arrange of estimations and time into consideration, had a better predictive power for PICU mortality compared to SD, CV, and MAGE (AUC: 0.626 vs 0.601, 0.599, 0.573). Combined previous studies (7,15) and our results, we should choose the GV index which considered the range, speed, time series, MAG, or GLI may be a good choice. Given the clinical practicality, we have introduced ACACP as a GV indicator and it is simple to transfer into a treatment protocol (24). Because this index focused on the comparison of changes between subsequent blood glucose values. Although the AUC of ACACP was smaller than MAG in our data set, its unique calculation and clinical significance suggest that GV metrics should reflect the change from one value to the next and guide clinical management to minimize this change, thereby facilitating glycemic management and improving prognosis in clinical practice. Finding indicators that contain information such as time series, rate, and range of change, and are convenient for clinical application is a potential, practical target for future treatment.
The multicenter, prospective design is a major strength of this study, particularly the choice to assess four GV indices in a large cohort of unselected PICU children and to explore their relationship with adverse outcomes. The results have stronger applicability than those of single-center studies. In addition, GV in the early stages may reflect a physiological stress response; and as treatment duration increases, it may be influenced by many factors related to treatment, including nutritional intake and drug usage (40). In the present study, GV metrics’ calculations included the first 72 hours of glucose values and excluded children using glucocorticoids or total parenteral nutrition, reducing the impact of medical management. Thus, GV in our study may provide a prior prediction of mortality.
Our study does have its limitations. First, there may be ascertainment bias as patients with abnormal blood glucose levels may have more frequent measurements, whereas patients with normal blood glucose do not measure as frequently. As a result, we may miss the record of hyperglycemia or hypoglycemia in some patients. Ideally, continuous glucose monitoring should be performed at predetermined time points; however, this will increase the amount of blood sampled and suffering from young children. Advances in CGM technology will facilitate the management or monitoring of early GV in critically ill children. We suggest that future studies exploring optimal glycemic control strategies for patients with high GV should make full use of CGM techniques. Secondly, our glucose measurements were obtained from different blood specimens, the homogeneity can’t be ensured. Finally, our study cannot conclude causality because the results of observational studies can only assume an association.