Nanotherapeutic approach to treat diabetic foot ulcers using tissue-engineered nanofiber skin substitutes: A review

Background and aimsDiabetes mellitus (DM) is a chronic metabolic disease associated with long-term multisystem complications, among which nonhealing diabetic foot ulcers (DFUs) are recognized as major cause of morbidity and mortality. Treating DFUs with surgical procedures such as synthetic or biological skin grafts or skin substitutes has several limitations, where none of the currently available skin substitutes is ideal.MethodsOVID/Medline and PubMed databases were searched using the Medical Subject Heading (MeSH) or Title/Abstract words (“diabetic foot ulcers”, “skin substitutes”, and “nanofibers”), to identify published research studies on DFUs and nanofibers.ResultsElectrospinning nanotechnology is being used in the biomedical field to produce polymeric nanofibers impregnated with drugs for wound healing, burns and diabetic ulcers. Those nanofibers also enable seeding of cells into them and culturing them in vitro to synthesize tissue-like structures. Knowing the advantages of generating patient-specific induced pluripotent stem cells (iPSCs) and organoids in three-dimension (3D), including skin organoids, it is worth mingling these technologies to develop tissue-engineered biological skin substitutes.ConclusionNanofiber-skin substitutes hold promise for treatment of patients suffering from DFUs and inspire novel strategies that could be applied to other organ systems as well, introducing a new era of “regenerative and personalized medicine”.     

SALAD‐BAAR: A numerical risk score for hospital admission or emergency department presentation in ambulatory patients with cardiovascular disease

BackgroundWhile many interventions to reduce hospital admissions and emergency department (ED) visits for patients with cardiovascular disease have been developed, identifying ambulatory cardiac patients at high risk for admission can be challenging.HypothesisA computational model based on readily accessible clinical data can identify patients at risk for admission.MethodsElectronic health record (EHR) data from a tertiary referral center were used to generate decision tree and logistic regression models. International Classification of Disease (ICD) codes, labs, admissions, medications, vital signs, and socioenvironmental variables were used to model risk for ED presentation or hospital admission within 90 days following a cardiology clinic visit. Model training and testing were performed with a 70:30 data split. The final model was then prospectively validated.ResultsA total of 9326 patients and 46 465 clinic visits were analyzed. A decision tree model using 75 patient characteristics achieved an area under the curve (AUC) of 0.75 and a logistic regression model achieved an AUC of 0.73. A simplified 9‐feature model based on logistic regression odds ratios achieved an AUC of 0.72. A further simplified numerical score assigning 1 or 2 points to each variable achieved an AUC of 0.66, specificity of 0.75, and sensitivity of 0.58. Prospectively, this final model maintained its predictive performance (AUC 0.63–0.60).ConclusionNine patient characteristics from routine EHR data can be used to inform a highly specific model for hospital admission or ED presentation in cardiac patients. This model can be simplified to a risk score that is easily calculated and retains predictive performance.