Glucose monitoring in the acutely ill patient with diabetes mellitus

Application of the New "Points in Range" Metrics in the Assessment of In-Hospital Glycemic Control

Introduction Capillary blood glucose (CBG) monitoring remains the most used testing form in hospitals and allows for "points in range (PIR)" metric calculation. This study was conceived to evaluate the metabolic control in patients with diabetes mellitus (DM) at a hospital through PIR metrics. Methods This was an observational cross-sectional study conducted on October 9, 2020, that included non-critical adults admitted to Centro Hospitalar Universitário do Porto (except pregnant/postpartum women) with DM under CBG monitoring and a minimum of 24 hours of hospitalization. Glycemic control was evaluated by previous day CBG monitoring. Results The study sample consisted of 110 patients with DM (93.6% type 2) with a median number of CBG tests of 4.00 (1.00) and a median CBG of 166.20 (69.41) mg/dL, SD 41.93 ± 27.20 mg/dL, and variation coefficient of 22.56 ± 12.51%. Points below range were 0.5%, with 0% below 54 mg/dL. The points in ranges 70-140 mg/dL and 140-180 mg/dL were 32.8% and 22.0%, respectively, and the total number of patients with all points in range 70-180 mg/dL was 19 (17.3%), with only 3 (2.7%) having all points in range 140-180 mg/dL and 10 (9.1%) in range 70-140 mg/dL. Regarding points above range (PAR), 29.9% and 14.8% points were at levels 1 and 2 hyperglycemia, respectively, and 15 (13.6%) patients had all points above 180 mg/dL. Correlations were identified between PAR and the total number of CBG assessments (ρ = 0.689, p < 0.001). Conclusion We conclude that in-hospital glycemic control remains suboptimal: only few have adequate control according to the PIR metrics despite low glycemic variability. PIR metrics are a new, valuable, simple and valid way to take better advantage of CBG monitoring at no added cost.

Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods

Objectives

The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care.

Methods

This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives.

Results

The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables.

Conclusions

It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.

Monitoring glycemic control: the importance of self-monitoring of blood glucose

Several methods, each with differing utility and limitations, exist for monitoring glycemic control. Hemoglobin A1c (HbA1c) is considered the standard measure of long-term glycemic control, and HbA1c levels are strongly associated with complications of diabetes. However, HbA1c does not provide "real-time" information about individual hyperglycemic or hypoglycemic excursions. Urine glucose testing is noninvasive and inexpensive, but it is dependent on the patient's individual renal threshold and can only detect glucose concentrations above this threshold. As such, urine testing cannot be recommended for diabetes management that aims for near-normoglycemia. Self-monitoring of blood glucose (SMBG) complements HbA1c by providing real-time blood glucose data. It is an educational tool for both patients and their healthcare providers to understand the effects of diet, exercise, and medications on day-to-day glycemic control. However, guidelines from various international diabetes organizations vary in their level of specificity regarding the frequency and timing of self-monitoring. SMBG should be implemented for all patients as part of an overall diabetes management plan that includes specific instruction on how, when, and why to test.

Self-efficacy impacts self-care and HbA1c in young adults with Type I diabetes

OBJECTIVE

The present study examined self-efficacy and self-esteem as basic aspects of the self that influence self-care and physiological outcomes among young adults with Type I diabetes. The two aims of this study were 1) to examine the cross-sectional and longitudinal role of the self-variables as they predict self-care and HbA1c and 2) to test whether self-care mediates the association between the self variables and HbA1c using cross-sectional and longitudinal data.

METHODS

One hundred ten participants were recruited from a regional diabetes outpatient clinic. Inclusion criteria were age (18-35 years) and duration of diabetes (>1 year before recruitment). Participants were 61% female and 88% white. In addition, the sample had an average annual income between $24,999 and $34,999, and 85% had completed some or all of college. The average duration of diabetes was 15 years.

RESULTS

Using multiple regression analyses we found that, compared with self-esteem, self-efficacy was a better predictor of all aspects of self-care and HbA1c in cross-sectional analyses, in addition to diet and exercise self-care, and a better predictor of HbA1c in longitudinal analyses. The data also supported the cross-sectional and longitudinal mediational model in which better self-care helped account for the association between greater self-efficacy and better HbA1c.

CONCLUSIONS

Self-efficacy is an important factor for management of self-care practices and physiological outcomes among young adults with Type I diabetes, and self-care may be an important mechanism by which self-efficacy influences HbA1c levels.