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.