Hemoglobin glycation index, calculated from a single fasting glucose value, as a prediction tool for severe hypoglycemia and major adverse cardiovascular events in DEVOTE

IntroductionHemoglobin glycation index (HGI) is the difference between observed and predicted glycated hemoglobin A1c (HbA1c), derived from mean or fasting plasma glucose (FPG). In this secondary, exploratory analysis of data from DEVOTE, we examined: whether insulin initiation/titration affected the HGI; the relationship between baseline HGI tertile and cardiovascular and hypoglycemia risk; and the relative strengths of HGI and HbA1c in predicting these risks.Research design and methodsIn DEVOTE, a randomized, double-blind, cardiovascular outcomes trial, people with type 2 diabetes received once per day insulin degludec or insulin glargine 100 units/mL. The primary outcome was time to first occurrence of a major adverse cardiovascular event (MACE), comprising cardiovascular death, myocardial infarction or stroke; severe hypoglycemia was a secondary outcome. In these analyses, predicted HbA1c was calculated using a linear regression equation based on DEVOTE data (HbA1c=0.01313 FPG (mg/dL) (single value)+6.17514), and the population data were grouped into HGI tertiles based on the calculated HGI values. The distributions of time to first event were compared using Kaplan–Meier curves; HRs and 95% CIs were determined by Cox regression models comparing risk of MACE and severe hypoglycemia between tertiles.ResultsChanges in HGI were observed at 12 months after insulin initiation and stabilized by 24 months for the whole cohort and insulin-naive patients. There were significant differences in MACE risk between baseline HGI tertiles; participants with high HGI were at highest risk (low vs high, HR: 0.73 (0.61 to 0.87)95% CI; moderate vs high, HR: 0.67 (0.56 to 0.81)95% CI; p<0.0001). No significant differences between HGI tertiles were observed in the risk of severe hypoglycemia (p=0.0911). With HbA1c included within the model, HGI no longer significantly predicted MACE.ConclusionsHigh HGI was associated with a higher risk of MACE; this finding is of uncertain significance given the association of HGI with insulin initiation and HbA1c.Trial registration numberNCT01959529.

Initiation of insulin therapy in patients with type 2 diabetes: An observational study

The aim of the study was to assess the initiation of insulin therapy in patients with type 2 diabetes using health claims data on prescription medicines. The study evaluated time to insulin initiation and prescribing patterns of other anti-diabetic medicines before and after insulin initiation. Five years after starting non-insulin antidiabetic therapy, 6.4 % of patients were prescribed insulin, which is substantially lower compared to other similar studies. Among all patients who initiated insulin therapy in 2013, 30 % did not continue any other antidiabetic therapy. However, this proportion was lowered to 20 % in 2018. Before insulin initiation in 2018, metformin was prescribed in only 67 % of patients and sulfonylureas in 78 % of patients. Moreover, metformin and sulfonylureas were discontinued after insulin initiation in 26 and 37 % of patients, resp. More attention should be paid to the continuation of oral anti-diabetics, particularly metformin, after insulin initiation.

Impact of Body Habitus on the Outcomes of Pediatric Patients With Diabetic Ketoacidosis

OBJECTIVE To determine whether obese and overweight pediatric patients with new onset diabetic ketoacidosis (DKA) treated with continuous infusion insulin have increased time to subcutaneous insulin initiation or adverse events as compared with patients with normal body habitus. METHODS A retrospective, cohort study was designed that included patients 2 to 18 years of age admitted with new onset DKA who received continuous infusion insulin from January 1, 2011, to December 31, 2017. Patients were stratified according to BMI percentile with the primary outcome of time to initiation of subcutaneous insulin. Secondary endpoints included time to minimum beta-hydroxybutyrate, and incidence of hypoglycemia or other adverse events. RESULTS A total of 337 patients (46.6% male, 9.6 ± 3.8 years of age) met study criteria. Patients were classified by body habitus as obese (7.7%, n = 26), overweight (7.1%, n = 24), normal body weight (58.8%, n = 198), or underweight (26.4%, n = 89), based on BMI percentile. Most patients were initiated on insulin at 0.1 unit/kg/hr (86.7%) for 16.7 ± 7.0 hours. Time from continuous infusion insulin initiation to subcutaneous insulin was not different between body habitus groups, nor was hypoglycemia or the use of mannitol (p &gt; 0.05). Median time to lowest beta-hydroxybutyrate was greater for obese (26.4, IQR [13.9, 41.9]) and overweight (32.4, IQR [18.3, 47.0]) groups than for normal body habitus patients (16.5, IQR [12.3, 23.8]) (p &lt; 0.05). CONCLUSIONS Time to subcutaneous insulin and adverse events was not associated with body habitus, but obese and overweight patients may have delayed beta-hydroxybutyrate clearance.

Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study

Background Applications of machine learning for the early detection of diseases for which a clear-cut diagnostic gold standard exists have been evaluated. However, little is known about the usefulness of machine learning approaches in the decision-making process for decisions such as insulin initiation by diabetes specialists for which no absolute standards exist in clinical settings. Objective The objectives of this study were to examine the ability of machine learning models to predict insulin initiation by specialists and whether the machine learning approach could support decision making by general physicians for insulin initiation in patients with type 2 diabetes. Methods Data from patients prescribed hypoglycemic agents from December 2009 to March 2015 were extracted from diabetes specialists’ registries, resulting in a sample size of 4860 patients who had received initial monotherapy with either insulin (n=293) or noninsulin (n=4567). Neural network output was insulin initiation ranging from 0 to 1 with a cutoff of >0.5 for the dichotomous classification. Accuracy, recall, and area under the receiver operating characteristic curve (AUC) were calculated to compare the ability of machine learning models to make decisions regarding insulin initiation to the decision-making ability of logistic regression and general physicians. By comparing the decision-making ability of machine learning and logistic regression to that of general physicians, 7 cases were chosen based on patient information as the gold standard based on the agreement of 8 of the 9 specialists. Results The AUCs, accuracy, and recall of logistic regression were higher than those of machine learning (AUCs of 0.89-0.90 for logistic regression versus 0.67-0.74 for machine learning). When the examination was limited to cases receiving insulin, discrimination by machine learning was similar to that of logistic regression analysis (recall of 0.05-0.68 for logistic regression versus 0.11-0.52 for machine learning). Accuracies of logistic regression, a machine learning model (downsampling ratio of 1:8), and general physicians were 0.80, 0.70, and 0.66, respectively, for 43 randomly selected cases. For the 7 gold standard cases, the accuracies of logistic regression and the machine learning model were 1.00 and 0.86, respectively, with a downsampling ratio of 1:8, which were higher than the accuracy of general physicians (ie, 0.43). Conclusions Although we found no superior performance of machine learning over logistic regression, machine learning had higher accuracy in prediction of insulin initiation than general physicians, defined by diabetes specialists’ choice of the gold standard. Further study is needed before the use of machine learning–based decision support systems for insulin initiation can be incorporated into clinical practice.