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.

BackgroundPrediabetes 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. ObjectiveThe 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.MethodsThis 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 (HbA1c), 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.ResultsThe 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 A1c 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.ConclusionsThe Sweetch mobile intervention program is a safe and effective method of increasing PA and reducing weight and HbA1c in adults with prediabetes. If sustained over a longer period, this intervention would be expected to reduce diabetes risk in this population.Trial RegistrationClincialTrials.gov NCT02896010; https://clinicaltrials.gov/ct2/show/NCT02896010 (Archived by WebCite at http://www.webcitation.org/6xJYxrgse)

ObjectiveTo develop and validate a multivariable prediction model for insulin-associated hypoglycemia in non-critically ill hospitalized adults.Research design and methodsWe collected pharmacologic, demographic, laboratory, and diagnostic data from 128 657 inpatient days in which at least 1 unit of subcutaneous insulin was administered in the absence of intravenous insulin, total parenteral nutrition, or insulin pump use (index days). These data were used to develop multivariable prediction models for biochemical and clinically significant hypoglycemia (blood glucose (BG) of ≤70 mg/dL and <54 mg/dL, respectively) occurring within 24 hours of the index day. Split-sample internal validation was performed, with 70% and 30% of index days used for model development and validation, respectively.ResultsUsing predictors of age, weight, admitting service, insulin doses, mean BG, nadir BG, BG coefficient of variation (CVBG), diet status, type 1 diabetes, type 2 diabetes, acute kidney injury, chronic kidney disease (CKD), liver disease, and digestive disease, our model achieved a c-statistic of 0.77 (95% CI 0.75 to 0.78), positive likelihood ratio (+LR) of 3.5 (95% CI 3.4 to 3.6) and negative likelihood ratio (−LR) of 0.32 (95% CI 0.30 to 0.35) for prediction of biochemical hypoglycemia. Using predictors of sex, weight, insulin doses, mean BG, nadir BG, CVBG, diet status, type 1 diabetes, type 2 diabetes, CKD stage, and steroid use, our model achieved a c-statistic of 0.80 (95% CI 0.78 to 0.82), +LR of 3.8 (95% CI 3.7 to 4.0) and −LR of 0.2 (95% CI 0.2 to 0.3) for prediction of clinically significant hypoglycemia.ConclusionsHospitalized patients at risk of insulin-associated hypoglycemia can be identified using validated prediction models, which may support the development of real-time preventive interventions.

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.

ObjectiveTo identify patient and hospital predictors of recurrent diabetic ketoacidosis (DKA) admissions in adults in the USA with type 1 diabetes, focusing on socioeconomic indicators.Research design and methodsThis cross-sectional study used the National Readmission Database to identify adult patients with type 1 diabetes admitted for DKA between 2010 and 2015. The index DKA admission was defined as the first admission within the calendar year and the primary outcome was recurrent DKA admission(s) within the same calendar year. Multivariable logistic regression analysis was performed using covariates of patient and hospital factors at the index admission to determine the odds of DKA readmission(s).ResultsAmong 181 284 index DKA admissions, 39 693 (22%) had at least one readmission within the calendar year, of which 33 931 (86%) and 5762 (14%) had 1–3 and ≥4 DKA readmissions, respectively. When compared with the highest income quartile, patients in the first and second income quartiles had 46% (95% CI 30% to 64%) and 34% (95% CI 19% to 51%) higher odds of four or more DKA readmissions, respectively. Medicaid and Medicare insurance were both associated with a 3.3-fold adjusted risk (95% CI 3.0 to 3.7) for ≥4 readmissions compared with private insurance, respectively. Younger age, female sex, and discharge against medical advice were also predictive.ConclusionsLower socioeconomic status and Medicaid insurance are strong predictors of DKA readmissions in adults with type 1 diabetes in the USA. Further studies are needed to understand the mediators of this association to inform multilevel interventions for this high-risk population.Significance of the studyThe association of socioeconomic status (SES) and hospital admission for DKA has been studied in pediatrics with type 1 diabetes, but the data in adults are limited, and studies evaluating recurrent DKA admissions are scarcer. To our knowledge, this is the first study to describe predictors of recurrent DKA admissions in adults with type 1 diabetes on a national level in the USA. We found that those at highest risk of recurrent DKA are young women with low SES who had Medicaid or Medicare insurance. These findings should prompt further studies to explore the mediators of these disparities in patients with type 1 diabetes, as recurrent DKA results in high healthcare utilization and increased risk of long-term complications.

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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 1,528 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.

Introduction: Insulin pumps and continuous glucose monitors (CGM) have many benefits in the management of type 1 diabetes. Unfortunately disparities in technology access occur in groups with increased risk for adverse effects (eg, low socioeconomic status [SES], public insurance). Research Design & Methods: Using 2015 to 2016 data from 4,895 participants from the T1D Exchange Registry, a structural equation model (SEM) was fit to explore the hypothesized direct and indirect relationships between SES, insurance features, access to diabetes technology, and adverse clinical outcomes (diabetic ketoacidosis, hypoglycemia). SEM was estimated using the maximum likelihood method and standardized path coefficients are presented. Results: Higher SES and more generous insurance coverage were directly associated with CGM use (β = 1.52, SE = 0.12, P < .0001 and β = 1.21, SE = 0.14, P < .0001, respectively). Though SES displayed a small inverse association with pump use (β = -0.11, SE = 0.04, P = .0097), more generous insurance coverage displayed a stronger direct association with pump use (β = 0.88, SE = 0.10, P < .0001). CGM use and pump use were both directly associated with fewer adverse outcomes (β = -0.23, SE = 0.06, P = .0002 and β = -0.15, SE = 0.04, P = .0002, respectively). Both SES and insurance coverage demonstrated significant indirect effects on adverse outcomes that operated through access to diabetes technology (β = -0.33, SE = 0.09, P = .0002 and β = -0.40, SE = 0.09, P < .0001, respectively). Conclusions: The association between SES and insurance coverage and adverse outcomes was primarily mediated through diabetes technology use, suggesting that disparities in diabetes outcomes have the potential to be mitigated by addressing the upstream disparities in technology use.