Glycemic variability in hospitalized patients: choosing metrics while awaiting the evidence

Assessing the temporal within-day glycemic variability during hospitalization in patients with type 2 diabetes patients using continuous glucose monitoring: a retrospective observational study

AIMS

Frequent and extensive within-day glycemic variability (GV) in blood glucose levels may increase the risk of hypoglycemia and long-term mortality in hospitalized patients with diabetes. We aimed to assess the amplitude and frequency of within-day GV in inpatients with type 2 diabetes and to explore the factors influencing within-day GV.

METHODS

We conducted a single-center, retrospective observational study by analyzing hospital records and 10-day real-time continuous glucose monitoring data. Within-day GV was assessed using the coefficient of variation (%CV). The primary outcome was the amplitude and frequency of within-day GV. The frequency of within-day GV was assessed by the consecutive days (CD) of maintaining within the target %CV range after first reaching it (CD after first reaching the target) and the maximum consecutive days of maintaining within the target %CV range (Max-CD). The target %CV range was less than 24.4%. We evaluated the factors influencing within-day GV using COX regression and Poisson regression models.

RESULTS

A total of 1050 cases were analyzed, of whom 86.57% reduced the amplitude of within-day GV before the sixth day of hospitalization. Of the 1050 hospitalized patients, 66.57% stayed within the target %CV range for less than two days after first reaching the target and 69.71% experienced a Max-CD of fewer than four days. Reducing the average postprandial glucose excursion (hazard ratio [HR]: 0.81, 95% confidence interval [CI]: 0.77-0.85; incidence rate ratios [IRR]: 0.72, 95% CI: 0.69-0.74) and the use of α-glucosidase inhibitors (IRR: 1.1, 95% CI: 1.01-1.18) and glucagon-like peptide-1 agonist (IRR: 1.30, 95% CI: 1.02-1.65) contributed to reducing the amplitude and decreasing the frequency of within-day GV. However, the use of insulin (HR: 0.64, 95% CI: 0.55-0.75; IRR: 0.86, 95% CI: 0.79-0.93) and glinide (HR: 0.47, 95% CI: 0.31-0.73; IRR: 0.84, 95% CI: 0.73-0.97) may lead to an increased frequency of within-day GV.

CONCLUSIONS

An increasing frequency of within-day GV was observed during the hospitalization in patients with type 2 diabetes, despite the effective reduction in the amplitude of within-day GV. Using medications designed to lower postprandial blood glucose could contribute to minimize the risk of frequent within-day GV.

Dysglycemia in ICU Patients

Dysglycemia has emerged as a very common challenge in critically ill patients, especially with regard to current coronavirus disease 2019 pandemic. Prediabetes, poorly controlled diabetes, pharmaceutical intervention in intensive care unit (ICU) with glucocorticoids, catecholamines and other medicines, and stress response all contribute to dysglycemia in critically ill patients. Early identification and management are the key to prevent further complications. Patient prognosis in terms of clinical outcome, length of ICU stay, and in-hospital morbidity/mortality are adversely affected by patient's dysglycemic status. Apart from hyperglycemia, the other three important pillars of dysglycemia are discussed in this article. Synopsis of early intervention have been captured from India-specific practice guidelines. Important landmark trials have also been captured in this article to provide a clarity on certain aspects of managing dysglycemia in ICUs. Hence, this review article is an attempt to bring forth the salient aspects in diagnosing and managing dysglycemia in critical care settings.

Comparison of Glycemic Variability and Hypoglycemic Events in Hospitalized Older Adults Treated with Basal Insulin plus Vildagliptin and Basal-Bolus Insulin Regimen: A Prospective Randomized Study

Background: The basal−bolus insulin regimen is recommended in hospitalized patients with diabetes mellitus (DM), but has an increased risk of hypoglycemia. We aimed to compare dipeptidyl peptidase 4 inhibitors (DPP4-i) and basal−bolus insulin glycemic outcomes in hospitalized type 2 DM patients. Methods and patients: Our prospective randomized study included 102 elderly T2DM patients (82 ± 9 years, HbA1c 6.6% ± 1.9). Glycemic control: A variability coefficient assessed by continuous glucose monitoring (Free Style® sensor), mean insulin dose and hypoglycemia rates obtained with the two treatments were analyzed. Results: No differences were found between groups in glycemic control (mean daily glycemia during the first 10 days: 152.6 ± 38.5 vs. 154.2 ± 26.3 mg/dL; p = 0.8). The total doses Kg/day were 0.40 vs. 0.20, respectively (p < 0.001). A lower number of hypoglycemic events (9% vs. 15%; p < 0.04) and lower glycemic coefficient of variation (22% vs. 28%; p < 0.0002) were observed in the basal−DPP4-i compared to the basal−bolus regimen group. Conclusions: Treatment of inpatient hyperglycemia with basal insulin plus DPP4-i is an effective and safe regimen in old subjects with T2DM, with a similar mean daily glucose concentration, but lower glycemic variability and fewer hypoglycemic episodes compared to the basal bolus insulin regimen.

The Association of Diabetes and Hyperglycemia on Inpatient Readmissions

OBJECTIVE

To evaluate the association between inpatient glycemic control and readmission in individuals with diabetes and hyperglycemia (DM/HG).

METHODS

Two data sets were analyzed from fiscal years 2011 to 2013: hospital data using the International Classification of Diseases, Ninth Revision (ICD-9) codes for DM/HG and point of care (POC) glucose monitoring. The variables analyzed included gender, age, mean, minimum and maximum glucose, along with 4 measures of glycemic variability (GV), standard deviation, coefficient of variation, mean amplitude of glucose excursions, and average daily risk range.

RESULTS

Of 66 518 discharges in FY 2011-2013, 28.4% had DM/HG based on ICD-9 codes and 53% received POC monitoring. The overall readmission rate was 13.9%, although the rates for individuals with DM/HG were higher at 18.9% and 20.6% using ICD-9 codes and POC data, respectively. The readmitted group had higher mean glucose (169 ± 47 mg/dL vs 158 ± 46 mg/dL, P < .001). Individuals with severe hypoglycemia and hyperglycemia had the highest readmission rates. All 4 GV measures were consistent and higher in the readmitted group.

CONCLUSION

Individuals with DM/HG have higher 30-day readmission rates than those without. Those readmitted had higher mean glucose, more extreme glucose values, and higher GV. To our knowledge, this is the first report of multiple metrics of inpatient glycemic control, including GV, and their associations with readmission.

Critical illness myopathy precedes hyperglycaemia and high glucose variability

BACKGROUND

Critical Illness Myopathy (CIM) is a serious ICU complication, and dysglycaemia is widely regarded as a risk factor. Although glucose variability (GV) has been independently linked to ICU mortality, an association with CIM has not been investigated. This study examines the relationship between CIM and GV.

METHODS

Retrospective investigation including ICU patients with SOFA ≥8, mechanical ventilation, and CIM diagnostics. Glucose readings were collected every 6 h throughout the first week of treatment, when CIM is thought to develop. GV was measured using standard deviation (SD), coefficient of variability (CV), mean absolute glucose (MAG), mean amplitude of glycaemic excursions (MAGE), and mean of daily difference (MODD).

RESULTS

74 patients were included, and 50 (67.6%) developed CIM. Time on glycaemic target (70-179 mg/dL), caloric and insulin intakes, mean, maximum and minimum blood glucose values were similar for all patients until the 5th day, after which CIM patients exhibited higher mean and maximum glucose levels. Significantly higher GV in CIM patients were observed on day 5 (SD, CV, MAG, MAGE), day 6 (MODD), and day 7 (SD, CV, MAG).

CONCLUSIONS

CIM patients developed transient increases in GV and hyperglycaemia only late in the first week, suggesting that myopathy precedes dysglycaemia.

Evaluating the effectiveness of a multi-faceted inpatient diabetes management program among hospitalised patients with diabetes mellitus

BACKGROUND

Diabetes mellitus (DM) is one of the most common chronic diseases. Individuals with DM are more likely to be hospitalised and stay longer than those without DM. Inpatient hypoglycemia and hyperglycemia, which are associated with adverse outcomes, are common, but can be prevented through hospital quality improvement programs.

METHODS

We designed a multi-faceted intervention program with the aim of reducing inpatient hypoglycemia and hyperglycemia. This was implemented over seven phases between September 2013 to January 2016, and covered all the non-critical care wards in a tertiary hospital. The program represented a pragmatic approach that leveraged on existing resources and infrastructure within the hospital. We calculated glucometric outcomes in June to August 2016 and compared them with those in June to August 2013 to assess the overall effectiveness of the program. We used regression models with generalised estimating equations to adjust for potential confounders and account for correlations of repeated outcomes within patients and admissions.

RESULTS

We observed significant reductions in patient-days affected by hypoglycemia (any glucose reading < 4 mmol/L: OR = 0.71, 95% CI: 0.61 to 0.83, p <  0.001), and hyperglycemia (any glucose reading > 14 mmol/L: OR = 0.84, 95% CI: 0.71 to 0.99, p = 0.041). Similar findings were observed for admission-level hypoglycemia and hyperglycemia. Further analyses suggested that these reductions started to occur four to 6 months post-implementation.

CONCLUSIONS

Our program was associated with sustained improvements in clinically relevant outcomes. Our described intervention could be feasibly implemented by other secondary and tertiary care hospitals by leveraging on existing infrastructure and work force.

Independent Association of Glucose Variability With Hospital Mortality in Adult Intensive Care Patients: Results From the Australia and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation Binational Registry

Supplemental Digital Content is available in the text.

Wide variations in blood glucose excursions in critically ill patients may influence adverse outcomes such as hospital mortality. However, whether blood glucose variability is independently associated with mortality or merely captures the excess risk attributable to hyperglycemic and hypoglycemic episodes is not established. We investigated whether blood glucose variability independently predicted hospital mortality in nonhyperglycemic critical care patients.

Practice Guidelines for Enteral Nutrition Management in Dysglycemic Critically Ill Patients: A Relook for Indian Scenario

Background and aim

Intensive-care practices and settings differ for India in comparison to other countries. While guidelines are available to direct the use of enteral nutrition (EN), there are no recommendations specific to nutritional management of EN in dysglycemic patients, specific to patients in Indian critical care settings. Advisory board meetings were arranged to develop the practice guidelines specific to the Indian context, for the use of EN in dysglycemic critically ill patients and to overcome challenges in this field.

Materials and methods

Two advisory board meetings were organized to review various existing guidelines, meta-analyses, randomized controlled trials (RCTs), controlled trials and review articles, for their contextual relevance and strength. Three rounds of Delphi voting were done to arrive at consensus on certain recommendations. A systematic grading of practice guidelines by the advisory board was done based on strength of the consensus voting and reviewed supporting evidences.

Results

Based on the literature review, the recommendations for developing the practice guidelines were made as per the grading criteria agreed upon by the advisory board. The recommendations were to address challenges regarding prediction and assessment of dysglycemia (DG), acceptable glycemic targets in such settings, general nutritional aspects pertaining to DG nutrition, and nutrition in various superspecialty cases in critical care settings, where DG is commonly encountered.

Conclusion

This paper summarizes the optimum EN practices for managing DG in critically ill patients. The practical solutions to overcome the challenges in this field are presented as practice guidelines at the end of each section. These guidelines are expected to provide guidance for EN management in dysglycemic critically ill patients. These guidelines also outline the model glycemic control task force and its roles in nutrition care as well as an intensive care unit DG nutrition protocol.

How to cite this article

Mehta Y, Mithal A, Kulkarni A, Reddy BR, Sharma J, Dixit S, et al. Practice Guidelines for Enteral Nutrition Management in Dysglycemic Critically Ill Patients: A Relook for Indian Scenario. Indian J Crit Care Med 2019;23(12):594–603.

Impact of Timing Between Insulin Administration and Meal Consumption on Glycemic Fluctuation and Outcomes in Hospitalized Patients With Type 2 Diabetes

BACKGROUND

The effect of time interval from insulin injection to meal consumption ("insulin-meal") on glycemic fluctuation and outcomes is not well understood.

OBJECTIVE

This study aims to investigate the impact of coordinated versus mismatched insulin-meal administration on glycemic fluctuation and outcomes among hospitalized patients with type 2 diabetes (T2D).

METHODS

Hospitalized patients with T2D who received at least 1 dose of insulin as part of sliding scale regimen were included. Data such as capillary blood glucose values and insulin-meal time intervals were collected.

RESULTS

A total of 215 patients with 840 insulin-meal encounters were eligible for the study. Compared to the insulin-meal mismatch group (n = 206), the coordinated insulin-meal administration group (n = 9) had lower mean glycemic fluctuation (6.5 [2.6] mmol/L vs 5.6 [2.5] mmol/L or 117 [47] mg/dL vs 100 [45] mg/dL). Encounters with the insulin-meal time interval of 30 to 45 minutes (n = 172) were associated with the lowest percentage of severe hyperglycemia occurrences (13%) as compared to encounters with time interval of 0 to 29 minutes (n = 280, 15%) and more than 45 minutes (n = 246, 16%).

CONCLUSION

Coordinated insulin-meal administration was associated with lower glycemic fluctuation among hospitalized patients with T2D.

Dysglycemia in the critically ill patient: current evidence and future perspectives

Dysglycemia in critically ill patients (hyperglycemia, hypoglycemia, glycemic variability and time in range) is a biomarker of disease severity and is associated with higher mortality. However, this impact appears to be weakened in patients with previous diabetes mellitus, particularly in those with poor premorbid glycemic control; this phenomenon has been called "diabetes paradox". This phenomenon determines that glycated hemoglobin (HbA1c) values should be considered in choosing glycemic control protocols on admission to an intensive care unit and that patients' target blood glucose ranges should be adjusted according to their HbA1c values. Therefore, HbA1c emerges as a simple tool that allows information that has therapeutic utility and prognostic value to be obtained in the intensive care unit.

Hypoglycemia Reduction Strategies in the ICU

PURPOSE OF REVIEW

We reviewed the strategies associated with hypoglycemia risk reduction among critically ill non-pregnant adult patients.

RECENT FINDINGS

Hypoglycemia in the ICU has been associated with increased mortality in a number of studies. Insulin dosing and glucose monitoring rules, response to impending hypoglycemia, use of computerization, and attention to modifiable factors extrinsic to insulin algorithms may affect the risk for hypoglycemia. Recurring use of intravenous (IV) bolus doses of insulin in insulin-resistant cases may reduce reliance upon higher IV infusion rates. In order to reduce the risk for hypoglycemia in the ICU, caregivers should define responses to interruption of continuous carbohydrate exposure, incorporate transitioning strategies upon initiation and interruption of IV insulin, define modifications of antihyperglycemic therapy in the presence of worsening renal function or chronic kidney disease, and anticipate the effects traceable to other medications and substances. Institutional and system-wide quality improvement efforts should assign priority to hypoglycemia prevention.

Glucose time series complexity as a predictor of type 2 diabetes

BACKGROUND

Complexity analysis of glucose profile may provide valuable information about the gluco-regulatory system. We hypothesized that a complexity metric (detrended fluctuation analysis, DFA) may have a prognostic value for the development of type 2 diabetes in patients at risk.

METHODS

A total of 206 patients with any of the following risk factors (1) essential hypertension, (2) obesity or (3) a first-degree relative with a diagnosis of diabetes were included in a survival analysis study for a diagnosis of new onset type 2 diabetes. At inclusion, a glucometry by means of a Continuous Glucose Monitoring System was performed, and DFA was calculated for a 24-h glucose time series. Patients were then followed up every 6 months, controlling for the development of diabetes.

RESULTS

In a median follow-up of 18 months, there were 18 new cases of diabetes (58.5 cases/1000 patient-years). DFA was a significant predictor for the development of diabetes, with ten events in the highest quartile versus one in the lowest (log-rank test chi2 = 9, df = 1, p = 0.003), even after adjusting for other relevant clinical and biochemical variables. In a Cox model, the risk of diabetes development increased 2.8 times for every 0.1 DFA units. In a multivariate analysis, only fasting glucose, HbA_1c and DFA emerged as significant factors.

CONCLUSIONS

Detrended fluctuation analysis significantly performed as a harbinger of type 2 diabetes development in a high-risk population. Complexity analysis may help in targeting patients who could be candidates for intensified treatment. Copyright © 2016 The Authors. Diabetes/Metabolism Research and Reviews Published by John Wiley & Sons Ltd.

Effectiveness of regular versus glargine insulin in stable critical care patients receiving parenteral nutrition: a randomized controlled trial

STUDY OBJECTIVE

To compare the effectiveness and safety of two glycemic control regimens in stable critical care patients receiving parenteral nutrition (PN).

DESIGN

Prospective, randomized open-label clinical trial.

METHODS

Eligible postoperative critical care patients in the ICU began PN on the first to the seventh day of ICU admission. The PN admixture included regular insulin, in doses sufficient to maintain 3 or more goal blood glucose (BG) levels between 110 and 180 mg/dl. After 3 to 5 days of PN containing regular insulin, patients were randomized to 3 more days of regular insulin at the same dose or 80% of their total daily regular insulin dose provided in PN solution as glargine insulin. Capillary BG monitoring was performed every 6 hours.

RESULTS

Twenty one patients were randomized to each treatment group. Median APACHE II scores were not significantly different between the two groups within the first 24-hour of ICU admission. There were no significant differences between the two groups at day 3 for mean daily dextrose (306.9 ± 46.2 vs. 305.2 ± 52.2 g; p=0.913) or insulin (18.3 ± 8.8 vs. 19.5 ± 10.0 units; p=0.696) doses. The percentage of BG values in the goal (110-180 mg/dl), hyperglycemic (> 180 mg/dl), and hypoglycemic (< 70 mg/dl) BG levels were similar between the two groups (69.0% vs. 66.7%, p=0.567; 11.9% vs. 11.1%, p=0.780; 0% vs. 1.6%, p=0.124, respectively). Mean daily BG levels were not significantly different between the two groups on each of the 3 study days (day 1: 140 ± 20 vs. 131 ± 25 mg/dl, p=0.194; day 2: 136 ± 20 vs. 140 ± 18 mg/dl, p=0.498; day 3: 142 ± 15 vs. 140 ± 19 mg/dl; p=0.741).

CONCLUSION

These data suggest that, compared with regular insulin added to PN, glargine insulin results in similar glycemic control and rates of hyperglycemia and hypoglycemia in stable critical care patients.

Target attainment through algorithm design during intravenous insulin infusion

BACKGROUND

Algorithms were designed under a single model, to attain differing designated glycemic targets during intravenous insulin infusion, and evaluated in order to justify computerization of the model. The approximate maintenance rate (MR) of insulin infusion is discovered according to rate of change of blood glucose (BG) and previous insulin infusion rate (IR). During treatment, re-assignment of IR depends on MR and BG. For each MR, a roughly sigmoidal relationship between BG and IR is specified, such that the inflection point falls approximately at a true target BG.

MATERIALS AND METHODS

Performance at St. Francis Hospital, Evanston, IL, was examined during use of tabular algorithms targeting three distinct BG ranges, appropriate for the treatment of hyperglycemic hyperosmolar state, diabetic ketoacidosis, or hyperglycemia accompanying other critical illness. Group membership was defined according to algorithm used for patient treatment during the first 6 months of 2012. The group geometric mean (GGM) and multiplicative surrogate standard deviation (MSSD) are reported as group measures, respectively typifying the central tendency and variability of individual patient BG distributions.

RESULTS

Between first attainment of target range BG control and a data collection end point, BG data were evaluable during treatment courses for 58 patients. During this time frame, in the group treated with target 100-149 mg/dL, there were five episodes of BG <70 mg/dL for each of five patients, with the lowest being 57 mg/dL. The GGM (with multiplicative standard deviation) was 269.4 (÷/× 1.06) mg/dL for the algorithm having target 200-299 mg/dL (n = 3 treatment courses), 172.6 (÷/× 1.15) mg/dL for target 150-199 mg/dL (n = 7), and 131.3 (÷/× 1.19) mg/dL for target 100-149 mg/dL (n = 48). The values of MSSD for the three groups were (÷/× 1.14), (÷/× 1.20), and (÷/× 1.20), respectively.

CONCLUSIONS

The pilot series suggests that once target range BG is attained, maintenance of control within each of three distinct BG target ranges is achievable, according to choice of algorithm.

Multiplicative surrogate standard deviation: a group metric for the glycemic variability of individual hospitalized patients

OBJECTIVE

Group metrics are described to quantify blood glucose (BG) variability of hospitalized patients.

METHODS

The "multiplicative surrogate standard deviation" (MSSD) is the reverse-transformed group mean of the standard deviations (SDs) of the logarithmically transformed BG data set of each patient. The "geometric group mean" (GGM) is the reverse-transformed group mean of the means of the logarithmically transformed BG data set of each patient. Before reverse transformation is performed, the mean of means and mean of SDs each has its own SD, which becomes a multiplicative standard deviation (MSD) after reverse transformation. Statistical predictions and comparisons of parametric or nonparametric tests remain valid after reverse transformation. A subset of a previously published BG data set of 20 critically ill patients from the first 72 h of treatment under the SPRINT protocol was transformed logarithmically. After rank ordering according to the SD of the logarithmically transformed BG data of each patient, the cohort was divided into two equal groups, those having lower or higher variability.

RESULTS

For the entire cohort, the GGM was 106 (÷/× 1.07) mg/dl, and MSSD was 1.24 (÷/× 1.07). For the subgroups having lower and higher variability, respectively, the GGM did not differ, 104 (÷/× 1.07) versus 109 (÷/× 1.07) mg/dl, but the MSSD differed, 1.17 (÷/× 1.03) versus 1.31 (÷/× 1.05), p = .00004.

CONCLUSIONS

By using the MSSD with its MSD, groups can be characterized and compared according to glycemic variability of individual patient members.

Effect of the approach to insulin therapy on glycaemic fluctuations and autonomic tone in hospitalized patients with diabetes

AIMS

Glycaemic variability (GV) is associated with mortality in acutely ill patients, but the mechanism is unknown. The objective of this study is to determine whether common approaches to insulin therapy have distinct effects on GV and autonomic tone.

METHODS

Hospitalized patients with diabetes were randomized to short-term intravenous (IV) or physiologic subcutaneous (SQ) insulin. Heart rate variability (HRV) and cardiac impedance (pre-ejection period, PEP) were used to estimate parasympathetic and sympathetic tone, respectively. GV was measured using a continuous glucose monitor.

RESULTS

Mean glucose tended to be lower initially in the SQ group (N = 16) compared with the IV group (N = 17) on day 1 (10.5 vs. 8.6 mmol/l, p = 0.05), but became non-significant during the transition off of the infusion. There was no difference in glycaemic lability index (GLI), continuous overlapping net glycaemic action (CONGA) or coefficient of variation (CV) on day 1, but by day 2, these measures were higher in the IV group (p < 0.05 for all). PEP was higher in the SQ group during (110 vs. 123 ms, p = 0.02) and after the intervention (104 vs. 126 ms, p = 0.004). Hypoglycaemia was similar in both groups. There were only small differences in HRV. Post-treatment PEP was inversely correlated with log GLI (r = -0.41, p = 0.03) but not other measures.

CONCLUSIONS

Short-term IV insulin is associated with an increase in multiple GV measures compared with optimal SQ insulin. However, GLI was the only predictor of PEP. Further research is needed to determine if interventions that minimize GV improve outcomes in the hospital.

Standardization versus customization of glucose reporting

Bergenstal et al. (Diabetes Technol Ther 2013;15:198-211) described an important approach toward standardization of reporting and analysis of continuous glucose monitoring and self-monitoring of blood glucose (SMBG) data. The ambulatory glucose profile (AGP), a composite display of glucose by time of day that superimposes data from multiple days, is perhaps the most informative and useful of the many graphical approaches to display glucose data. However, the AGP has limitations; some variations are desirable and useful. Synchronization with respect to meals, traditionally used in glucose profiles for SMBG data, can improve characterization of postprandial glucose excursions. Several other types of graphical display are available, and recently developed ones can augment the information provided by the AGP. There is a need to standardize the parameters describing glycemic variability and cross-validate the available computer programs that calculate glycemic variability. Clinical decision support software can identify and prioritize clinical problems, make recommendations for modifications of therapy, and explain its justification for those recommendations. The goal of standardization is challenging in view of the diversity of clinical situations and of computing and display platforms and software. Standardization is desirable but must be done in a manner that permits flexibility and fosters innovation.