Use of Diabetes Technologies and Retinopathy in Adults With Type 1 Diabetes

Importance

Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision loss. Outcomes of continuous glucose monitoring (CGM) and insulin pump use in DR are not well understood.

Objective

To assess the use of CGM, insulin pump, or both, and DR and proliferative diabetic retinopathy (PDR) in adults with type 1 diabetes (T1D).

Design, Setting, and Participants

A retrospective cohort study of adults with T1D in a tertiary diabetes center and ophthalmology center was conducted from 2013 to 2021, with data analysis performed from June 2022 to April 2023.

Exposure

Use of diabetes technologies, including insulin pump, CGM, and both CGM and insulin pump.

Main Outcomes and Measures

The primary outcome was development of DR or PDR. A secondary outcome was the progression of DR for patients in the longitudinal cohort. Multivariable logistic regression models assessed for development of DR and PDR and association with CGM and insulin pump use.

Results

A total of 550 adults with T1D were included (median age, 40 [IQR, 28-54] years; 54.4% female; 24.5% Black or African American; and 68.4% White), with a median duration of diabetes of 20 (IQR, 10-30) years, and median hemoglobin A1c (HbA1c) of 7.8% (IQR, 7.0%-8.9%). Overall, 62.7% patients used CGM, 58.2% used an insulin pump, and 47.5% used both; 44% (244 of 550) of the participants had DR at any point during the study. On univariate analysis, CGM use was associated with lower odds of DR and PDR, and CGM with pump was associated with lower odds of PDR (all P < .05), compared with no CGM use. Multivariable logistic regression adjusting for age, sex, race and ethnicity, diabetes duration, microvascular and macrovascular complications, insurance type, and mean HbA1c, showed that CGM was associated with lower odds of DR (odds ratio [OR], 0.52; 95% CI, 0.32-0.84; P = .008) and PDR (OR, 0.42; 95% CI, 0.23-0.75; P = .004), compared with no CGM use. In the longitudinal analysis of participants without baseline PDR, 79 of 363 patients (21.8%) had progression of DR during the study.

Conclusions and Relevance

In this cohort study of adults with T1D, CGM use was associated with lower odds of developing DR and PDR, even after adjusting for HbA1c. These findings suggest that CGM may be useful for diabetes management to mitigate risk for DR and PDR.

Machine Learning Models for Prediction of Diabetic Microvascular Complications

IMPORTANCE AND AIMS

Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN).

METHODS

A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics.

RESULTS

Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance.

CONCLUSIONS AND RELEVANCE

There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study

Background

SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials.

Methods

Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis.

Findings

Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]).

Interpretation

In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD.

Funding

National Institutes of Health, United States Department of Veterans Affairs.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Understanding Providers' Readiness and Attitudes Toward Autoantibody Screening: A Mixed-Methods Study

Screening for autoantibodies associated with type 1 diabetes can identify people most at risk for progressing to clinical type 1 diabetes and provide an opportunity for early intervention. Drawbacks and barriers to screening exist, and concerns arise, as methods for disease prevention are limited and no cure exists today. The availability of novel treatment options such as teplizumab to delay progression to clinical type 1 diabetes in high-risk individuals has led to the reassessment of screening programs. This study explored awareness, readiness, and attitudes of endocrinology providers toward type 1 diabetes autoantibody screening.

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM

Objective

To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin.

Design

Federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Setting

10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021.

Participants

4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments.

Exposure

The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort.

Main outcomes measures

The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated.

Results

4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease.

Conclusions

Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Factors Associated With Discordant A1C-Estimated and Measured Average Glucose Among Hospitalized Patients With Diabetes

In this retrospective analysis, we explored the correlation between measured average glucose (mAG) and A1C-estimated average glucose (eAG) in hospitalized patients with diabetes and identified factors associated with discordant mAG and eAG at the transition from home to hospital. Having mAG lower than eAG was associated with Black race, other race, increasing length of stay, community hospital setting, surgery, fever, metformin use, certain inpatient diets, home antihyperglycemic treatment, and coded type 1 or type 2 diabetes. Having mAG higher than eAG was associated with certain discharge services (e.g., intensive care unit), higher BMI, hypertension, tachycardia, higher albumin, higher potassium, anemia, inpatient glucocorticoid use, and treatment with home insulin, secretagogues, and glucocorticoids. These factors should be considered when using patients' A1C as an indicator of outpatient glycemic control to determine the inpatient antihyperglycemic regimens.

Association of Long-Term, New-Onset, and Postsurgical Diabetes With Survival in Patients With Resectable Pancreatic Cancer: A Retrospective Cohort Study

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis. Identifying modifiable risk factors, such as diabetes, is crucial. In the context of PDAC diagnosis, diabetes manifests as long-term (LTD), new-onset (NOD), or postsurgical (PSD) phenotypes. The link between these diabetes phenotypes and PDAC survival is debated.

MATERIALS AND METHODS

We performed a retrospective study on patients with resectable PDAC who underwent pancreatectomy at Johns Hopkins Hospital from 2003 to 2017. We utilized the National Death Index and electronic medical records to determine vital status. We categorized diabetes as LTD, NOD, or PSD based on the timing of diagnosis relative to pancreatic resection. Using multivariable Cox models, we assessed hazard ratios (HRs) for survival times associated with each phenotype, considering known PDAC prognostic factors.

RESULTS

Of 1556 patients, the 5-year survival was 19% (95% CI, 17-21). No significant survival differences were observed between diabetes phenotypes and non-diabetic patients. NOD and PSD presented nonsignificant increased risks of death (aHR: 1.14 [95% CI, 0.8-1.19] and 1.05 [95% CI, 0.89-1.25], respectively). LTD showed no survival difference (aHR, 0.98; 95% CI, 0.99-1.31).

CONCLUSIONS

No link was found between diabetes phenotypes and survival in resectable PDAC patients. Comprehensive prospective studies are required to validate these results.

Association of Metformin With the Development of Age-Related Macular Degeneration

Importance

Age-related macular degeneration (AMD) is a leading cause of blindness with no treatment available for early stages. Retrospective studies have shown an association between metformin and reduced risk of AMD.

Objective

To investigate the association between metformin use and age-related macular degeneration (AMD).

Design, Setting, and Participants

The Diabetes Prevention Program Outcomes Study is a cross-sectional follow-up phase of a large multicenter randomized clinical trial, Diabetes Prevention Program (1996-2001), to investigate the association of treatment with metformin or an intensive lifestyle modification vs placebo with preventing the onset of type 2 diabetes in a population at high risk for developing diabetes. Participants with retinal imaging at a follow-up visit 16 years posttrial (2017-2019) were included. Analysis took place between October 2019 and May 2022.

Interventions

Participants were randomly distributed between 3 interventional arms: lifestyle, metformin, and placebo.

Main Outcomes and Measures

Prevalence of AMD in the treatment arms.

Results

Of 1592 participants, 514 (32.3%) were in the lifestyle arm, 549 (34.5%) were in the metformin arm, and 529 (33.2%) were in the placebo arm. All 3 arms were balanced for baseline characteristics including age (mean [SD] age at randomization, 49 [9] years), sex (1128 [71%] male), race and ethnicity (784 [49%] White), smoking habits, body mass index, and education level. AMD was identified in 479 participants (30.1%); 229 (14.4%) had early AMD, 218 (13.7%) had intermediate AMD, and 32 (2.0%) had advanced AMD. There was no significant difference in the presence of AMD between the 3 groups: 152 (29.6%) in the lifestyle arm, 165 (30.2%) in the metformin arm, and 162 (30.7%) in the placebo arm. There was also no difference in the distribution of early, intermediate, and advanced AMD between the intervention groups. Mean duration of metformin use was similar for those with and without AMD (mean [SD], 8.0 [9.3] vs 8.5 [9.3] years; P = .69). In the multivariate models, history of smoking was associated with increased risks of AMD (odds ratio, 1.30; 95% CI, 1.05-1.61; P = .02).

Conclusions and Relevance

These data suggest neither metformin nor lifestyle changes initiated for diabetes prevention were associated with the risk of any AMD, with similar results for AMD severity. Duration of metformin use was also not associated with AMD. This analysis does not address the association of metformin with incidence or progression of AMD.

Prediction of Next Glucose Measurement in Hospitalized Patients by Comparing Various Regression Methods: Retrospective Cohort Study

BACKGROUND

Continuous glucose monitors have shown great promise in improving outpatient blood glucose (BG) control; however, continuous glucose monitors are not routinely used in hospitals, and glucose management is driven by point-of-care (finger stick) and serum glucose measurements in most patients.

OBJECTIVE

This study aimed to evaluate times series approaches for prediction of inpatient BG using only point-of-care and serum glucose observations.

METHODS

Our data set included electronic health record data from 184,320 admissions, from patients who received at least one unit of subcutaneous insulin, had at least 4 BG measurements, and were discharged between January 1, 2015, and May 31, 2019, from 5 Johns Hopkins Health System hospitals. A total of 2,436,228 BG observations were included after excluding measurements obtained in quick succession, from patients who received intravenous insulin, or from critically ill patients. After exclusion criteria, 2.85% (3253/113,976), 32.5% (37,045/113,976), and 1.06% (1207/113,976) of admissions had a coded diagnosis of type 1, type 2, and other diabetes, respectively. The outcome of interest was the predicted value of the next BG measurement (mg/dL). Multiple time series predictors were created and analyzed by comparing those predictors and the index BG measurement (sample-and-hold technique) with next BG measurement. The population was classified by glycemic variability based on the coefficient of variation. To compare the performance of different time series predictors among one another, R2, root mean squared error, and Clarke Error Grid were calculated and compared with the next BG measurement. All these time series predictors were then used together in Cubist, linear, random forest, partial least squares, and k-nearest neighbor methods.

RESULTS

The median number of BG measurements from 113,976 admissions was 12 (IQR 5-24). The R2 values for the sample-and-hold, 2-hour, 4-hour, 16-hour, and 24-hour moving average were 0.529, 0.504, 0.481, 0.467, and 0.459, respectively. The R2 values for 4-hour moving average based on glycemic variability were 0.680, 0.480, 0.290, and 0.205 for low, medium, high, and very high glucose variability, respectively. The proportion of BG predictions in zone A of the Clarke Error Grid analysis was 61%, 59%, 27%, and 53% for 4-hour moving average, 24-hour moving average, 3 observation rolling regression, and recursive regression predictors, respectively. In a fully adjusted Cubist, linear, random forest, partial least squares, and k-nearest neighbor model, the R2 values were 0.563, 0.526, 0.538, and 0.472, respectively.

CONCLUSIONS

When analyzing time series predictors independently, increasing variability in a patient's BG decreased predictive accuracy. Similarly, inclusion of older BG measurements decreased predictive accuracy. These relationships become weaker as glucose variability increases. Machine learning techniques marginally augmented the performance of time series predictors for predicting a patient's next BG measurement. Further studies should determine the potential of using time series analyses for prediction of inpatient dysglycemia.

Improving Continuous Glucose Monitoring Uptake in Underserved Youth with Type 1 Diabetes: The IMPACT Study

Background: Continuous glucose monitoring (CGM) improves glycemic control. Less than half of youth with type 1 diabetes (T1D) use CGM, with disparities among minority and low-income youth. The aim of this study was to determine if trial CGM use increases uptake of personal CGM. Methods: T1D youth were provided sample CGM placement at the point of care, with CGM education and app setup. Follow-up calls at 5 and 10 days assessed CGM data, and desire to continue using CGM. Follow-up at 3-6 months recorded CGM use, CGM data, and A1c. Participants completed surveys at enrollment, 10 days, and 3 months. Differences were assessed between baseline and follow-up. Results: Of the 26 enrolled participants with T1D, 15 were CGM naive, and 11 were prior CGM users. The mean age was 14.1 ± 2.9 years, 65% male, 42% were Black, 12% were Hispanic, 65% were on public insurance, and 43% had household income of <$50,000. The median duration of diabetes was 4.6 years (interquartile range 2.4-7.7), mean baseline A1c was 10.7% ± 2.4%. After trial CGM use, 85% of participants reported wanting personal CGM, and at 3-6 months follow-up 76% had obtained one and 43% were using a personal CGM. There were no improvements in A1C or time in range, but participants reported an increase in the perceived benefits of CGM usage (4.0 vs. 4.3, p = 0.03). Conclusions: Placing a sample CGM at the point of care can improve uptake of personal CGM and may help mitigate disparities in CGM use in minority and underserved youth. Long-term studies are needed to determine how similar interventions impact glycemic control and patient outcomes. ClinicalTrials.gov: NCT04721145.

Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes

Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.

Racial Disparities in Access and Use of Diabetes Technology Among Adult Patients With Type 1 Diabetes in a U.S. Academic Medical Center

OBJECTIVE

Recent studies highlight racial disparities in insulin pump (PUMP) and continuous glucose monitor (CGM) use in children and adolescents with type 1 diabetes (T1D). This study explored racial disparities in diabetes technology among adult patients with T1D.

RESEARCH DESIGN AND METHODS

This was a retrospective clinic-based cohort study of adult patients with T1D seen consecutively from April 2013 to January 2020. Race was categorized into non-Black (reference group) and Black. The primary outcomes were baseline and prevalent technology use, rates of diabetes technology discussions (CGMdiscn, PUMPdiscn), and prescribing (CGMrx, PUMPrx). Multivariable logistic regression analysis evaluated the association of technology discussions and prescribing with race, adjusting for social determinants of health and diabetes outcomes.

RESULTS

Among 1,258 adults with T1D, baseline technology use was significantly lower for Black compared with non-Black patients (7.9% vs. 30.3% for CGM; 18.7% vs. 49.6% for PUMP), as was prevalent use (43.6% vs. 72.1% for CGM; 30.7% vs. 64.2% for PUMP). Black patients had adjusted odds ratios (aORs) of 0.51 (95% CI 0.29, 0.90) for CGMdiscn and 0.61 (95% CI 0.41, 0.93) for CGMrx. Black patients had aORs of 0.74 (95% CI 0.44, 1.25) for PUMPdiscn and 0.40 (95% CI, 0.22, 0.70) for PUMPrx. Neighborhood context, insurance, marital and employment status, and number of clinic visits were also associated with the outcomes.

CONCLUSIONS

Significant racial disparities were observed in discussions, prescribing, and use of diabetes technology. Further research is needed to identify the causes behind these disparities and develop and evaluate strategies to reduce them.

Glycemic Outcomes of Hospitalized Patients on Ambulatory Humulin-R U-500 Insulin

OBJECTIVE

Managing hospitalized patients on ambulatory U-500 insulin is challenging because of limited guidance on how to safely adjust insulin doses during admission. We sought to evaluate glycemic outcomes in relation to inpatient insulin doses in patients receiving U-500 prior to hospitalization.

METHODS

Retrospective study of hospitalized patients on ambulatory U-500 seen consecutively from January 2015 to December 2019. Primary outcomes were inpatient hypoglycemia, hyperglycemia, and normoglycemia at different insulin dosages expressed as weight-based (unit/kg/d) inpatient total daily dose (TDD) and ratio of inpatient to outpatient TDD.

RESULTS

We identified 66 admissions of 46 unique patients. The median (interquartile range) body mass index was 41.0 kg/m2 (35.1, 46.8), home TDD 212 units (120, 300), and home insulin dose 1.6 units/kg/d (1.1, 2.2). The median (interquartile range) inpatient insulin dose was 0.7 unit/kg/d (0.3, 1.0) and the ratio of inpatient to outpatient TDD was 0.4 (0.2, 0.8). Hyperglycemia persisted throughout the hospitalization. For the outcomes of hyperglycemia and normoglycemia, we found no association between increased levels of insulin dosages. For the outcome of hypoglycemia, significantly higher odds were observed when non-fasting patients received an inpatient TDD that was either > 40% of their home TDD or > 0.6 unit/kg/d of insulin.

CONCLUSION

Patients on ambulatory U-500 have significant hyperglycemia during admission. Inpatient insulin doses of 40% of home TDD or ≤ 0.6 unit/kg were not associated with increased hypoglycemia risk. Further prospective studies are needed to determine effective doses in these high-risk patients.

Association between dysglycemia and the Charlson Comorbidity Index among hospitalized patients with diabetes

AIM

Inpatient dysglycemia has been linked to short-term mortality, but longer-term mortality data are lacking. Our aim was to evaluate the association between inpatient dysglycemia and one-year mortality risk.

METHODS

Retrospective chart review of adults with diabetes hospitalized between 2015 and 2019. The Charlson Comorbidity Index (CCI) was used to estimate 1-year mortality risk, stratified into low (CCI ≤ 5) and high risk (CCI ≥6). Simple and multivariable logistic regression was used to evaluate the association between dysglycemic measures and high mortality risk.

RESULTS

Among 22,639 unique admissions, BG ≥ 180, ≥300, ≤70, <54 and <40 mg/dL were associated with adjusted odds of 1.43 (95 % CI, 1.33, 1.54), 1.58 (95 % CI, 1.48, 1.68), 2.16 (95 % CI, 2.01, 2.32), 2.58 (95 % CI, 2.32, 2.86), and 2.56 (95 % CI, 2.19, 2.99) for high mortality risk, respectively. Older age and Black race were positively associated with hyperglycemia and hypoglycemia. Myocardial infarction, congestive heart failure (CHF), and moderate to severe liver disease were most strongly associated with hyperglycemia, while renal disease, CHF, peripheral vascular disease, and peptic ulcer disease were most strongly associated with hypoglycemia.

CONCLUSIONS

Inpatient hypoglycemia and hyperglycemia were both positively associated with higher one-year mortality risk, with stronger magnitude of association observed for hypoglycemia. The association appears to be mediated mainly by presence of diabetes-related complications.

Hospital Diabetes Meeting 2022

The annual Virtual Hospital Diabetes Meeting was hosted by Diabetes Technology Society on April 1 and April 2, 2022. This meeting brought together experts in diabetes technology to discuss various new developments in the field of managing diabetes in hospitalized patients. Meeting topics included (1) digital health and the hospital, (2) blood glucose targets, (3) software for inpatient diabetes, (4) surgery, (5) transitions, (6) coronavirus disease and diabetes in the hospital, (7) drugs for diabetes, (8) continuous glucose monitoring, (9) quality improvement, (10) diabetes care and educatinon, and (11) uniting people, process, and technology to achieve optimal glycemic management. This meeting covered new technology that will enable better care of people with diabetes if they are hospitalized.

Machine Learning Models for Inpatient Glucose Prediction

PURPOSE OF REVIEW

Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dynamic factors, machine learning approaches can be leveraged for prediction of glucose trends in the hospital to mitigate and prevent suboptimal hypoglycemic and hyperglycemic outcomes. Our aim was to review the clinical evidence for the role of machine learning-based models in predicting hospitalized patients' glucose trajectory.

RECENT FINDINGS

The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from both continuous glucose monitors and large electronic health records (EHRs). With increasing sample sizes, inclusion of a greater number of predictor variables, and use of more advanced machine learning algorithms, there has been a trend in recent years towards increasing predictive accuracy for glycemic outcomes in the hospital setting. While current models predicting glucose trajectory offer promising results, they have not been tested prospectively in the clinical setting. Accurate machine learning algorithms have been developed and validated for prediction of hypoglycemia and hyperglycemia in the hospital. Further work is needed in implementation/integration of machine learning models into EHR systems, with prospective studies to evaluate effectiveness and safety of such clinical decision support on glycemic and other clinical outcomes.

Insulinoma mimic: methadone-induced hypoglycaemia

Methadone use for opioid use disorder and chronic pain has increased since the start of the century with about 4.4 million dispensed prescriptions in 2009. With increased use of methadone, there has been increasing reporting of less commonly reported side effects (ie, hypoglycaemia). Here, we describe a woman in her 70s with history of opioid use disorder on methadone, stage 4 chronic kidney disease and prior hypoglycaemic episodes who initially presented with perforated gastric ulcer requiring surgical repair. Her perioperative course was complicated by profound hyperinsulinaemic hypoglycaemia. Given concern for methadone-induced hypoglycaemia, methadone was discontinued with monitoring of subsequent blood glucose, insulin, C peptide, proinsulin, β-hydroxybutyrate and blood methadone levels. As the serum methadone levels decreased, insulin levels substantially decreased in parallel. After 21 days off methadone, dextrose infusion was discontinued with restoration of euglycaemia. In a patient with hyperinsulinaemic hypoglycaemia and methadone use, it is important to consider discontinuing methadone and re-evaluate fasting glucose levels prior to an extensive and invasive insulinoma workup.

Insulin Dosing and Glycemic Outcomes among Steroid-treated Hospitalized Patients

OBJECTIVE

To determine the optimal insulin-to-steroid dose ratio for attainment of glycemic control in hospitalized patients.

METHODS

We retrospectively studied data collected from the electronic health record within an academic medical center from 18,599 patient-days where patients were treated concurrently with insulin and steroids. Multivariate logistic regression analyses, which included demographic and clinical variables, assessed the relationships between the exposures of total and basal insulin-to-steroid ratios and the outcomes of glycemic control (all blood glucose readings on the following patient-day >70 and ≤180 mg/dl) and hypoglycemia within three subgroups of steroid dosing: low (≤10 mg prednisone equivalent dose [PED]), medium (>10 to ≤40 mg PED), and high (>40 mg PED).

RESULTS

Increased insulin-to-steroid ratio was associated with increased odds of both glycemic control and hypoglycemia. The optimal total insulin-to-steroid ratio for attaining glycemic control was 0.294 units/kg/10 mg PED in the low-dose subgroup, 0.257 units/kg/10 mg PED in the medium-dose subgroup, and 0.085 units/kg/10 mg PED in the high-dose subgroup. The optimal basal insulin-to-steroid ratio was 0.215 units/kg/10 mg PED in the low-dose subgroup, 0.126 units/kg/10 mg PED in the medium-dose subgroup, and 0.036 units/kg/10 mg PED in the high-dose subgroup.

CONCLUSIONS

Increasing insulin-to-steroid ratios are positively associated with glycemic control and hypoglycemia. Our study suggests that ∼0.3 units/kg/10 mg PED is an optimal dose for low and medium dose steroids, while ∼0.1 units/kg/10 mg PED is optimal for high dose steroids. Further prospective studies are needed to identify insulin regimens that will optimize glycemic control in steroid treated patients while minimizing hypoglycemia risk.

Continuous Ketone Monitoring Consensus Report 2021

This article is the work product of the Continuous Ketone Monitoring Consensus Panel, which was organized by Diabetes Technology Society and met virtually on April 20, 2021. The panel consisted of 20 US-based experts in the use of diabetes technology, representing adult endocrinology, pediatric endocrinology, advanced practice nursing, diabetes care and education, clinical chemistry, and bioengineering. The panelists were from universities, hospitals, freestanding research institutes, government, and private practice. Panelists reviewed the medical literature pertaining to ten topics: (1) physiology of ketone production, (2) measurement of ketones, (3) performance of the first continuous ketone monitor (CKM) reported to be used in human trials, (4) demographics and epidemiology of diabetic ketoacidosis (DKA), (5) atypical hyperketonemia, (6) prevention of DKA, (7) non-DKA states of fasting ketonemia and ketonuria, (8) potential integration of CKMs with pumps and automated insulin delivery systems to prevent DKA, (9) clinical trials of CKMs, and (10) the future of CKMs. The panelists summarized the medical literature for each of the ten topics in this report. They also developed 30 conclusions (amounting to three conclusions for each topic) about CKMs and voted unanimously to adopt the 30 conclusions. This report is intended to support the development of safe and effective continuous ketone monitoring and to apply this technology in ways that will benefit people with diabetes.

Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients

BACKGROUND

Inpatient glucose management can be challenging due to evolving factors that influence a patient's blood glucose (BG) throughout hospital admission. The purpose of our study was to predict the category of a patient's next BG measurement based on electronic medical record (EMR) data.

METHODS

EMR data from 184,361 admissions containing 4,538,418 BG measurements from five hospitals in the Johns Hopkins Health System were collected from patients who were discharged between January 1, 2015 and May 31, 2019. Index BGs used for prediction included the 5th to penultimate BG measurements (N = 2,740,539). The outcome was category of next BG measurement: hypoglycemic (BG  ≤  70 mg/dl), controlled (BG 71-180 mg/dl), or hyperglycemic (BG > 180 mg/dl). A random forest algorithm that included a broad range of clinical covariates predicted the outcome and was validated internally and externally.

FINDINGS

In our internal validation test set, 72·8%, 25·7%, and 1·5% of BG measurements occurring after the index BG were controlled, hyperglycemic, and hypoglycemic respectively. The sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·77/0·81, 0·77/0·89, and 0·73/0·91, respectively. On external validation in four hospitals, the ranges of sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·64-0·70/0·80-0·87, 0·75-0·80/0·82-0·84, and 0·76-0·78/0·87-0·90, respectively.

INTERPRETATION

A machine learning algorithm using EMR data can accurately predict the category of a hospitalized patient's next BG measurement. Further studies should determine the effectiveness of integration of this model into the EMR in reducing rates of hypoglycemia and hyperglycemia.

Stakeholder Perspectives on an Inpatient Hypoglycemia Informatics Alert: Mixed Methods Study

Background

Iatrogenic hypoglycemia is a common occurrence among hospitalized patients and is associated with poor clinical outcomes and increased mortality. Clinical decision support systems can be used to reduce the incidence of this potentially avoidable adverse event.

Objective

This study aims to determine the desired features and functionality of a real-time informatics alert to prevent iatrogenic hypoglycemia in a hospital setting.

Methods

Using the Agency for Healthcare Research and Quality Five Rights of Effective Clinical Decision Support Framework, we conducted a mixed methods study using an electronic survey and focus group sessions of hospital-based providers. The goal was to elicit stakeholder input to inform the future development of a real-time informatics alert to target iatrogenic hypoglycemia. In addition to perceptions about the importance of the problem and existing barriers, we sought input regarding the content, format, channel, timing, and recipient for the alert (ie, the Five Rights). Thematic analysis of focus group sessions was conducted using deductive and inductive approaches.

Results

A 21-item electronic survey was completed by 102 inpatient-based providers, followed by 2 focus group sessions (6 providers per session). Respondents universally agreed or strongly agreed that inpatient iatrogenic hypoglycemia is an important problem that can be addressed with an informatics alert. Stakeholders expressed a preference for an alert that is nonintrusive, accurate, communicated in near real time to the ordering provider, and provides actionable treatment recommendations. Several electronic medical record tools, including alert indicators in the patient header, glucose management report, and laboratory results section, were deemed acceptable formats for consideration. Concerns regarding alert fatigue were prevalent among both survey respondents and focus group participants.

Conclusions

The design preferences identified in this study will provide the framework needed for an informatics team to develop a prototype alert for pilot testing and evaluation. This alert will help meet the needs of hospital-based clinicians caring for patients with diabetes who are at a high risk of treatment-related hypoglycemia.

A Rare Case of Subcutaneous Insulin Resistance Presumed to be due to Paraneoplastic Process in Pancreatic Adenocarcinoma

Objective

We describe a rare case of profound subcutaneous insulin resistance (SIR) presumed due to a paraneoplastic process caused by pancreatic adenocarcinoma that improved with intravenous insulin and tumor resection.

Methods

An 80-year-old man with previously well-controlled type 2 diabetes mellitus had worsening glycemic control (hemoglobin A1C increase of 6.5% to 8.6% over 4 months) following a recent diagnosis of pancreatic adenocarcinoma. His blood glucose was uncontrolled at 600 mg/dL despite rapid up-titration of a subcutaneous basal-bolus insulin regimen totaling 1000 units/d. Extensive evaluation of insulin resistance including insulin antibodies and anti-insulin receptor antibodies was negative. Due to clinical deterioration, the patient underwent pancreaticoduodenectomy before the completion of neoadjuvant chemotherapy. The patient received intravenous insulin before surgery, which resulted in rapid improvement in glycemic control. The patient’s blood glucose normalized, and he was maintained on metformin monotherapy following pancreaticoduodenectomy.

Results

This patient had evidence of SIR in the setting of pancreatic adenocarcinoma. SIR was likely a paraneoplastic process as glycemic control improved after tumor resection. Interestingly, the patient did not have hyperinsulinemia but rather evidence of β-cell dysfunction, which highlights the possibility of exogenous insulin resistance.

Conclusion

Paraneoplastic processes due to pancreatic adenocarcinoma can cause SIR, marked by profound hyperglycemia and deteriorating functional status. It is, therefore important to recognize this rare syndrome and appropriately escalate to a higher level of care and consider proceeding with tumor resection.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Predictors of Time-to-Repeat Point-of-Care Glucose Following Hypoglycemic Events in Hospitalized Patients

BACKGROUND

Previous studies have shown low adherence to the recommendation to repeat point-of-care glucose (POCG) within 15 minutes following the treatment of inpatient hypoglycemia. We sought to evaluate whether patient and clinical factors may predict time-to-repeat (TTR) POCG following hypoglycemic events in hospitalized adult patients.

METHODS

This was a retrospective cross-sectional analysis of 22 226 index hypoglycemic (≤70 mg/dL) readings (of 993 395 total POCG samples) from 6226 hospital admissions within the Johns Hopkins Health System over three years. Time-to-repeat was defined as the difference in time (minutes) between the index POCG and the next POCG sample. Multivariable logistic regression was used to evaluate the association of TTR with clinical, patient, and hospital factors.

RESULTS

The median (IQR) TTR was 49 (25-119) minutes, and 14.1% of index POCGs had a TTR ≤15. Severity of hypoglycemia, intensive care unit (ICU), intermediate care (IMC) and pediatrics admissions, and dextrose or glucagon administration were associated with higher adjusted odds of TTR ≤15 minutes. Admission to community hospitals, procedural units, surgery, and labor and delivery was associated with lower adjusted odds of TTR ≤15 minutes. Age, sex, insulin on board, secretagogue use, diabetes type, nutritional status, previous POCG value, and glycemic variability were not significantly associated.

CONCLUSION

There is low adherence to the recommendation to repeat a POCG within 15 minutes following the treatment of inpatient hypoglycemia, which may be mediated by both patient and hospital factors. Further studies are needed to understand the mediators and implications of this practice variability.

Regression From Prediabetes to Normal Glucose Regulation and Prevalence of Microvascular Disease in the Diabetes Prevention Program Outcomes Study (DPPOS)

OBJECTIVE

Regression from prediabetes to normal glucose regulation (NGR) was associated with reduced incidence of diabetes by 56% over 10 years in participants in the Diabetes Prevention Program Outcomes Study (DPPOS). In an observational analysis, we examined whether regression to NGR also reduced risk for microvascular disease (MVD).

RESEARCH DESIGN AND METHODS

Generalized estimating equations were used to examine the prevalence of aggregate MVD at DPPOS year 11 in people who regressed to NGR at least once (vs. never) during the Diabetes Prevention Program (DPP). Logistic regression assessed the relationship of NGR with retinopathy, nephropathy, and neuropathy, individually. Generalized additive models fit smoothing splines to describe the relationship between average A1C during follow-up and MVD (and its subtypes) at the end of follow-up.

RESULTS

Regression to NGR was associated with lower prevalence of aggregate MVD in models adjusted for age, sex, race/ethnicity, baseline A1C, and treatment arm (odds ratio [OR] 0.78, 95% CI 0.65-0.78, P = 0.011). However, this association was lost in models that included average A1C during follow-up (OR 0.95, 95% CI 0.78-1.16, P = 0.63) or diabetes status at the end of follow-up (OR 0.92, 95% CI 0.75-1.12, P = 0.40). Similar results were observed in examination of the association between regression to NGR and prevalence of nephropathy and retinopathy, individually. Risk for aggregate MVD, nephropathy, and retinopathy increased across the A1C range.

CONCLUSIONS

Regression to NGR is associated with a lower prevalence of aggregate MVD, nephropathy, and retinopathy, primarily due to lower glycemic exposure over time. Differential risk for the MVD subtypes begins in the prediabetes A1C range.

Association of Area Deprivation and Diabetic Ketoacidosis Readmissions: Comparative Risk Analysis of Adults vs Children With Type 1 Diabetes

OBJECTIVE

Recurrent diabetic ketoacidosis (DKA) is associated with mortality in adults and children with type 1 diabetes (T1D). We aimed to evaluate the association of area deprivation and other patient factors with recurrent DKA in pediatric patients compared with adults.

RESEARCH DESIGN AND METHODS

This cross-sectional study used the Maryland Health Services Cost Review Commission's database to identify patients with T1D admitted for DKA between 2012 and 2017. Area deprivation and other variables were obtained from the first DKA admission of the study period. Multivariable logistic regression analysis was performed to determine predictors of DKA readmissions. Interactions (Ints) evaluated differences among the groups.

RESULTS

There were 732 pediatric and 3305 adult patients admitted with DKA. Area deprivation was associated with higher odds of readmission in pediatric patients than in adults. Compared with the least deprived, moderately deprived pediatric patients had an OR of 7.87-(95% CI, 1.02 to 60.80) compared with no change in odds in adults for four or more readmissions (Pint < 0.01). Similar odds were observed in the most deprived pediatric patients, which differed significantly from the OR of 2.23 (95% CI, 1.16 to 4.25) in adults (Pint of 0.2). Moreover, increasing age, female sex, Hispanic ethnicity, and discharge against medical advice conferred a high odds for four or more readmissions in pediatric patients compared with adults.

CONCLUSION

Area deprivation was predictive of recurrent DKA admissions, with a more pronounced influence in pediatric than adult patients with T1D. Further studies are needed to understand the mechanisms behind these associations and address disparities specific to each population.

Randomized controlled evaluation of an insulin pen storage policy

PURPOSE

Results of a quality-improvement project to enhance safeguards against "wrong-pen-to-patient" insulin pen errors by permitting secure bedside storage of insulin pens are reported.

METHODS

A cluster-randomized controlled evaluation was conducted at an academic medical center to assess adherence with institutional policy on insulin pen storage before and after implementation of a revised policy allowing pen storage in locking boxes in patient rooms. In phase 1 of the study, baseline data on policy adherence were captured for 8 patient care units (4 designated as intervention units and 4 designated as control units). In phase 2, policy adherence was assessed through direct observation during weekly audits after lock boxes were installed on intervention units and education on proper insulin pen storage was provided to nurses in all 8 units.

RESULTS

Phase 1 rates of adherence to insulin pen storage policy were 59% in the intervention units and 49% in the control units (p = 0.56). During phase 2, there was no significant change from baseline in control unit adherence (67%, p = 0.26), but adherence in intervention units improved significantly, to 89% (p = 0.005). Common types of observed nonadherence included pens being unsecured in patient rooms or nurses' pockets or left in patient-specific medication drawers after patient discharge.

CONCLUSION

An institutional policy change permitting secure storage of insulin pens close to the point of care, paired with nurse education, increased adherence more than education alone.

Development and Implementation of a Subcutaneous Insulin Clinical Decision Support Tool for Hospitalized Patients

BACKGROUND

Insulin is one of the highest risk medications used in hospitalized patients. Multiple complex factors must be considered in determining a safe and effective insulin regimen. We sought to develop a computerized clinical decision support (CDS) tool to assist hospital-based clinicians in insulin management.

METHODS

Adapting existing clinical practice guidelines for inpatient glucose management, a design team selected, configured, and implemented a CDS tool to guide subcutaneous insulin dosing in non-critically ill hospitalized patients at two academic medical centers that use the EpicCare® electronic medical record (EMR). The Agency for Healthcare Research and Quality (AHRQ) best practices in CDS design and implementation were followed.

RESULTS

A CDS tool was developed in the form of an EpicCare SmartForm, which generates an insulin regimen by integrating information about the patient's body weight, diabetes type, home and hospital insulin requirements, and nutritional status. Total daily recommended insulin doses are distributed into respective basal and nutritional doses with a tailored correctional insulin scale. Preimplementation, several approaches were used to communicate this new tool to clinicians, including emails, lectures, and videos. Postimplementation, a support team was available to address user technical issues. Feedback from stakeholders has been used to continuously refine the tool. Inclusion of the programming in the EMR vendor's community library has allowed dissemination of the tool outside our institution.

CONCLUSIONS

We have developed an EMR-based tool to guide SQ insulin dosing in non-critically ill hospitalized patients. Further studies are needed to evaluate adoption and clinical effectiveness of this intervention.

SAT-138 Predictors and Health Care Expenditures Associated with Recurrent Diabetic Ketoacidosis Admissions in Adults with Type 1 Diabetes

Introduction: Diabetic ketoacidosis (DKA) is a serious complication in patients with type 1 diabetes (T1D). Recurrent DKA admissions have been associated with increased mortality and substantial healthcare costs. In contrast to pediatrics, significantly less attention has been devoted to exploring and addressing recurrent DKA admissions in adults with T1D. We aimed to characterize the adult population of T1D admitted with DKA in Maryland, identify predictors of recurrent DKA admissions, and quantify their associated healthcare costs. Methods: We conducted a cross-sectional study using the Maryland Health Services Cost Review Commission's database, which collects data from all acute care hospitals in the state. We defined DKA using ICD diagnostic codes for ketoacidosis and hyperosmolality in T1D. We selected adult patients (age 20 or above) with T1D who lived in or bordering Maryland and had at least one admission with a primary diagnosis of DKA between January 1, 2012 and December 31, 2017. The predictors analyzed in this study represent patient characteristics from the first DKA admission. The primary outcome was readmission for DKA in the study period. The summation of admission charges was used to calculate DKA healthcare expenditures. Results: There were 6,147 DKA admissions by 3,368 T1D adults admitted in Maryland in the study period. Readmissions occurred in 915 (27%) patients, with 1-3 and >4 readmissions occurring in 700 (76%) and 487 (24%) of these patients, respectively. Overall, the patients admitted with DKA were predominately non-Hispanic (96%) white (45%) or black (45%) men (53%) between the ages 20-39 years old (48%) from areas of low to moderate socioeconomic status (SES). The majority of patients were insured by private (37%) and Medicaid (27%) insurance. The strongest predictors for DKA readmissions were female sex (OR 1.3; 95%CI 1.1-1.6), residing in an area of low SES (OR 1.5; 95% 1.1-2.0), being insured by Medicare (OR 1.7; 95%CI 1.3-2.2), Medicaid (OR 1.5 95%CI 1.2-1.8) or Self-pay/charity (OR 1.5; 95%CI 1.1-1.9), and discharge against medical advice (OR 1.6; 95%CI 1.2-2.3). Older and non-white patients had a reduced odds of readmission. The expenditure of all adult DKA admissions totaled 5.4 billion dollars. While patients who had DKA readmissions consisted of only 27% of those admitted with DKA, their total costs were 59% of these DKA charges at 3.2 billion dollars. Conclusion: There is a high prevalence of recurrent DKA admissions in adults with T1D. Younger patients, women, having non-private insurance and those living in areas of low SES have the highest odds for readmission. Although these patients are a minority, they account for a majority of DKA health expenditures. Further studies are needed to understand drivers of these admissions in order to intervene in this population at high risk for poor outcomes. Supported by a NIH T32 Grant T32 and Saudek Fellowship Award.

Association of Low Fasting Glucose and HbA1c With Cardiovascular Disease and Mortality: The MESA Study

Trials of intensive glucose control have not improved cardiovascular disease (CVD) risk in populations with type 2 diabetes; however, in the general population, reports are inconsistent about the effects of maintaining lower glucose levels. Some speculate that low glycemic values are associated with increased glycemic variability, which is in turn associated with higher CVD risk. It has also been suggested that fasting glucose and hemoglobin A1c (HbA1c) in the lower ranges have a different relationship with CVD and mortality. In 4990 participants from the Multi-Ethnic Study of Atherosclerosis, we used logistic regression to investigate associations of low fasting glucose (<80 mg/dL) and HbA1c (<5.0%) from baseline and averaged across follow-up with incident CVD and mortality over 13 years. We used normal glycemic ranges (80 to <100 mg/dL and 5.0 to <5.7%) as references and analyzed glycemic levels with visit-matched covariates. We adjusted for potential confounding by age, sex, race/ethnicity, education, income, smoking status, body mass index, total cholesterol level, cholesterol medications, high-density lipoprotein cholesterol, and hypertension. Low baseline glucose and HbA1c were positively, but not significantly, associated with mortality, whereas low average fasting glucose and HbA1c were strongly and significantly associated with incident CVD [glucose OR, 2.04 (95% CI, 1.38-3.00); HbA1c OR, 2.01 (95% CI, 1.58-2.55)] and mortality [glucose OR, 1.93 (95% CI, 1.33-2.79); HbA1c OR, 2.51 (95% CI, 2.00-3.15)]. These results were not due to type 2 diabetes or medication use. Glucose variability did not explain CVD risk beyond average glucose levels. Chronic low fasting glucose and HbA1c may be better indicators of risk than a single low measurement.

Development, Implementation, and Evaluation of a Physician-Targeted Inpatient Glycemic Management Curriculum

OBJECTIVE

Diabetes is prevalent among hospitalized patients and there are multiple challenges to attaining glycemic control in the hospital setting. We sought to develop an inpatient glycemic management curriculum with stakeholder input and to evaluate the effectiveness of this educational program on glycemic control in hospitalized patients.

METHODS

Using the Six-Step Approach of Kern to Curriculum Development for Medical Education, we developed and implemented an educational curriculum for inpatient glycemic management targeted to internal medicine residents and hospitalists. We surveyed physicians (n = 73) and conducted focus group sessions (n = 18 physicians) to solicit input regarding educational deficits and desired format of the educational intervention. Based on feedback from the surveys and focus groups, we developed educational goals and objectives and a case-based curriculum, which was delivered over a 1-year period via in-person teaching sessions by 2 experienced diabetes physicians at 3 hospitals. Rates of hypoglycemia and hyperglycemia were evaluated among at-risk patient days using an interrupted time-series design.

RESULTS

We developed a mnemonic-based (SIGNAL) curriculum consisting of 10 modules, which covers key concepts of inpatient glycemic management and provides an approach to daily glycemic management: S = steroids, I = insulin, G = glucose, N = nutritional status, A = added dextrose, and L = labs. Following implementation of the curriculum, there was no difference in the rates of hyperglycemia in insulin-treated patients following the intervention; however, there was an increase in the rates of hypoglycemia defined as blood glucose (BG) ⩽ 70 mg/dL (5.6% vs 3.0%, P < .001) and clinically significant hypoglycemia defined as BG < 54 mg/dL (1.9% vs 0.8%, P = .01). There was poor penetration of the curriculum, with 60%, 20%, and 90% of the learning modules being delivered at the three participating hospitals, respectively.

CONCLUSIONS

In this pilot study, a physician-targeted educational curriculum was not associated with improved glycemic control. Adapting the intervention to increase penetration and integrating the curriculum into existing clinical decision support tools may improve the effectiveness of the educational program on glycemic outcomes.

Association of Hemoglobin A1c and Wound Healing in Diabetic Foot Ulcers

OBJECTIVE

This study evaluated the association between hemoglobin A_1c (A1C) and wound outcomes in patients with diabetic foot ulcers (DFUs).

RESEARCH DESIGN AND METHODS

We conducted a retrospective analysis of an ongoing prospective, clinic-based study of patients with DFUs treated at an academic institution during a 4.7-year period. Data from 270 participants and 584 wounds were included in the analysis. Cox proportional hazards regression was used to assess the incidence of wound healing at any follow-up time in relation to categories of baseline A1C and the incidence of long-term (≥90 days) wound healing in relation to tertiles of nadir A1C change and mean A1C change from baseline, adjusted for potential confounders.

RESULTS

Baseline A1C was not associated with wound healing in univariate or fully adjusted models. Compared with a nadir A1C change from baseline of -0.29 to 0.0 (tertile 2), a nadir A1C change of 0.09 to 2.4 (tertile 3) was positively associated with long-term wound healing in the subset of participants with baseline A1C <7.5% (hazard ratio [HR] 2.07; 95% CI 1.08-4.00), but no association with wound healing was seen with the mean A1C change from baseline in this group. Neither nadir A1C change nor mean A1C change were associated with long-term wound healing in participants with baseline A1C ≥7.5%.

CONCLUSIONS

There does not appear to be a clinically meaningful association between baseline or prospective A1C and wound healing in patients with DFUs. The paradoxical finding of accelerated wound healing and increase in A1C in participants with better baseline glycemic control requires confirmation in further studies.

Inpatient Glycemic Management of Non-cardiac CVD: Focus on Stroke and PVD

PURPOSE OF REVIEW

Hyperglycemia occurs frequently in hospitalized patients with stroke and peripheral vascular disease (PVD). Guidelines for inpatient glycemic management are not well established for this patient population. We will review the clinical impact of hyperglycemia in this acute setting and review the evidence for glycemic control.

RECENT FINDINGS

Hyperglycemia in acute stroke is associated with poor short and long-term outcomes, and perioperative hyperglycemia in those undergoing revascularization for PVD is linked to increased post-surgical complications. Studies evaluating tight glucose control do not demonstrate improvement in clinical outcomes, although the risk for hypoglycemia increases substantially. Additional studies are needed to evaluate tight glucose goals relative to our current standard of care and the role of permissive hyperglycemia. Given the limited data to guide glycemic management in these patient populations, it is recommended that general guidelines for inpatient glycemic control be followed. Special considerations should be made to address factors that may impact glucose management, including neurological deficits and clinical changes that occur in the postoperative state.

The Society for Vascular Surgery Wound, Ischemia, and foot Infection (WIfI) classification independently predicts wound healing in diabetic foot ulcers

OBJECTIVE

Previous studies have reported correlation between the Wound, Ischemia, and foot Infection (WIfI) classification system and wound healing time on unadjusted analyses. However, in the only multivariable analysis to date, WIfI stage was not predictive of wound healing. Our aim was to examine the association between WIfI classification and wound healing after risk adjustment in patients with diabetic foot ulcers (DFUs) treated in a multidisciplinary setting.

METHODS

All patients presenting to our multidisciplinary DFU clinic from June 2012 to July 2017 were enrolled in a prospective database. A Cox proportional hazards model accounting for patients' sociodemographics, comorbidities, medication profiles, and wound characteristics was used to assess the association between WIfI classification and likelihood of wound healing at 1 year.

RESULTS

There were 310 DFU patients enrolled (mean age, 59.0 ± 0.7 years; 60.3% male; 60.0% black) with 709 wounds, including 32.4% WIfI stage 1, 19.9% stage 2, 25.2% stage 3, and 22.4% stage 4. Mean wound healing time increased with increasing WIfI stage (stage 1, 96.9 ± 8.3 days; stage 4, 195.1 ± 10.6 days; P < .001). Likelihood of wound healing at 1 year was 94.1% ± 2.0% for stage 1 wounds vs 67.4% ± 4.4% for stage 4 (P < .001). After risk adjustment, increasing WIfI stage was independently associated with poor wound healing (stage 4 vs stage 1: hazard ratio, [HR] 0.44; 95% confidence interval, 0.33-0.59). Peripheral artery disease (HR, 0.73), increasing wound area (HR, 0.99 per square centimeter), and longer time from wound onset to first assessment (HR, 0.97 per month) also decreased the likelihood of wound healing, whereas use of clopidogrel was protective (HR, 1.39; all, P ≤ .04). The top three predictors of poor wound healing were WIfI stage 4 (z score, -5.40), increasing wound area (z score, -3.14), and WIfI stage 3 (z score, -3.11), respectively.

CONCLUSIONS

Among patients with DFU, the WIfI classification system predicts wound healing at 1 year in both crude and risk-adjusted analyses. This is the first study to validate the WIfI score as an independent predictor of wound healing using multivariable analysis.

A Novel Approach for Fully Automated, Personalized Health Coaching for Adults with Prediabetes: Pilot Clinical Trial

Background

Prediabetes is a high-risk state for the future development of type 2 diabetes, which may be prevented through physical activity (PA), adherence to a healthy diet, and weight loss. Mobile health (mHealth) technology is a practical and cost-effective method of delivering diabetes prevention programs in a real-world setting. Sweetch (Sweetch Health, Ltd) is a fully automated, personalized mHealth platform designed to promote adherence to PA and weight reduction in people with prediabetes. 

Objective

The objective of this pilot study was to calibrate the Sweetch app and determine the feasibility, acceptability, safety, and effectiveness of the Sweetch app in combination with a digital body weight scale (DBWS) in adults with prediabetes.

Methods

This was a 3-month prospective, single-arm, observational study of adults with a diagnosis of prediabetes and body mass index (BMI) between 24 kg/m² and 40 kg/m². Feasibility was assessed by study retention. Acceptability of the mobile platform and DBWS were evaluated using validated questionnaires. Effectiveness measures included change in PA, weight, BMI, glycated hemoglobin (HbA_1c), and fasting blood glucose from baseline to 3-month visit. The significance of changes in outcome measures was evaluated using paired t test or Wilcoxon matched pairs test.

Results

The study retention rate was 47 out of 55 (86%) participants. There was a high degree of acceptability of the Sweetch app, with a median (interquartile range [IQR]) score of 78% (73%-80%) out of 100% on the validated System Usability Scale. Satisfaction regarding the DBWS was also high, with median (IQR) score of 93% (83%-100%). PA increased by 2.8 metabolic equivalent of task (MET)–hours per week (SD 6.8; P=.02), with mean weight loss of 1.6 kg (SD 2.5; P<.001) from baseline. The median change in A_1c was −0.1% (IQR −0.2% to 0.1%; P=.04), with no significant change in fasting blood glucose (−1 mg/dL; P=.59). There were no adverse events reported.

Conclusions

The Sweetch mobile intervention program is a safe and effective method of increasing PA and reducing weight and HbA_1c in adults with prediabetes. If sustained over a longer period, this intervention would be expected to reduce diabetes risk in this population.

Trial Registration

ClincialTrials.gov NCT02896010; https://clinicaltrials.gov/ct2/show/NCT02896010 (Archived by WebCite at http://www.webcitation.org/6xJYxrgse)

Update on management of diabetic foot ulcers

Diabetic foot ulcers (DFUs) are a serious complication of diabetes that results in significant morbidity and mortality. Mortality rates associated with the development of a DFU are estimated to be 5% in the first 12 months, and 5-year morality rates have been estimated at 42%. The standard practices in DFU management include surgical debridement, dressings to facilitate a moist wound environment and exudate control, wound off-loading, vascular assessment, and infection and glycemic control. These practices are best coordinated by a multidisciplinary diabetic foot wound clinic. Even with this comprehensive approach, there is still room for improvement in DFU outcomes. Several adjuvant therapies have been studied to reduce DFU healing times and amputation rates. We reviewed the rationale and guidelines for current standard of care practices and reviewed the evidence for the efficacy of adjuvant agents. The adjuvant therapies reviewed include the following categories: nonsurgical debridement agents, dressings and topical agents, oxygen therapies, negative pressure wound therapy, acellular bioproducts, human growth factors, energy-based therapies, and systemic therapies. Many of these agents have been found to be beneficial in improving wound healing rates, although a large proportion of the data are small, randomized controlled trials with high risks of bias.

The Case for Diabetes Population Health Improvement: Evidence-Based Programming for Population Outcomes in Diabetes

PURPOSE OF REVIEW

The goal of this review is to describe diabetes within a population health improvement framework and to review the evidence for a diabetes population health continuum of intervention approaches, including diabetes prevention and chronic and acute diabetes management, to improve clinical and economic outcomes.

RECENT FINDINGS

Recent studies have shown that compared to usual care, lifestyle interventions in prediabetes lower diabetes risk at the population-level and that group-based programs have low incremental medial cost effectiveness ratio for health systems. Effective outpatient interventions that improve diabetes control and process outcomes are multi-level, targeting the patient, provider, and healthcare system simultaneously and integrate community health workers as a liaison between the patient and community-based healthcare resources. A multi-faceted approach to diabetes management is also effective in the inpatient setting. Interventions shown to promote safe and effective glycemic control and use of evidence-based glucose management practices include provider reminder and clinical decision support systems, automated computer order entry, provider education, and organizational change. Future studies should examine the cost-effectiveness of multi-faceted outpatient and inpatient diabetes management programs to determine the best financial models for incorporating them into diabetes population health strategies.

The effect of vitamin D supplementation on glucose metabolism in type 2 diabetes mellitus: A systematic review and meta-analysis of intervention studies

AIMS

We aimed to assess whether vitamin D supplementation improves glucose metabolism in adults with type 2 diabetes.

METHODS

PubMed and Cochrane database were searched up to July 1st 2016 for randomized controlled trials that assessed the relationship between vitamin D supplementation and glucose metabolism (change in hemoglobin A1C (HbA1C) and fasting blood glucose (FBG)) among adults with type 2 diabetes.

RESULTS

Twenty nine trials (3324 participants) were included in the systematic review. Among 22 studies included in the meta-analysis, 19 reported HbA1C, 16 reported FBG outcomes and 15 were deemed poor quality. There was a modest reduction in HbA1C (-0.32% [-0.53 to -0.10], I2=91.9%) compared to placebo after vitamin D supplementation but no effect on FBG (-2.33mg/dl [-6.62 to 1.95], I2=59.2%). In studies achieving repletion of vitamin D deficiency (n=7), there were greater mean reductions in HbA1C (-0.45%, [-1.09 to 0.20]) and FBG (-7.64mg/dl [-16.25 to 0.97]) although not significant.

CONCLUSIONS

We found a modest reduction of HbA1C after vitamin D treatment in adults with type 2 diabetes albeit with substantial heterogeneity between studies and no difference in FBG. Larger studies are needed to further evaluate the glycemic effects of vitamin D treatment especially in patients with vitamin D deficiency.