A pro-reparative bioelectronic device for controlled delivery of ions and biomolecules.

Wound healing is a complex physiological process that requires precise control and modulation of many parameters. Therapeutic ion and biomolecule delivery has the capability to regulate the wound healing process beneficially. However, achieving controlled delivery through a compact device with the ability to deliver multiple therapeutic species can be a challenge. Bioelectronic devices have emerged as a promising approach for therapeutic delivery. Here, we present a pro-reparative bioelectronic device designed to deliver ions and biomolecules for wound healing applications. The device incorporates ion pumps for the targeted delivery of H+ and zolmitriptan to the wound site. In vivo studies using a mouse model further validated the device's potential for modulating the wound environment via H+ delivery that decreased M1/M2 macrophage ratios. Overall, this bioelectronic ion pump demonstrates potential for accelerating wound healing via targeted and controlled delivery of therapeutic agents to wounds. Continued optimization and development of this device could not only lead to significant advancements in tissue repair and wound healing strategies but also reveal new physiological information about the dynamic wound environment.

Risk Factors for Hospital-Acquired Pressure Injury in Adult Critical Care Patients.

BACKGROUND: Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Relative contributions of risk factors for the incidence of pressure injury in adult critical care patients are not well understood. OBJECTIVE: To develop and validate a model to identify risk factors associated with hospital-acquired pressure injuries among adult critical care patients. METHODS: This retrospective cohort study included 23 806 adult patients (28 480 encounters) with an intensive care unit stay at an academic quaternary care center. Patient encounters were randomly split (7:3) into training and validation sets. The training set was used to develop a multivariable logistic regression model using the least absolute shrinkage and selection operator method. The model's performance was evaluated with the validation set. RESULTS: Independent risk factors identified by logistic regression were length of hospital stay, preexisting diabetes, preexisting renal failure, maximum arterial carbon dioxide pressure, minimum arterial oxygen pressure, hypotension, gastrointestinal bleeding, cellulitis, and minimum Braden Scale score of 14 or less. On validation, the model differentiated between patients with and without pressure injury, with area under the receiver operating characteristic curve of 0.85, and performed better than a model with Braden Scale score alone (P < .001). CONCLUSIONS: A model that identified risk factors for hospital-acquired pressure injury among adult critical care patients was developed and validated using a large data set of clinical variables. This model may aid in selecting high-risk patients for focused interventions to prevent formation of hospital-acquired pressure injuries.

Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients.

Accurately measuring the risk of pressure injury remains the most important step for effective prevention and intervention. Time-dependent risk factors for pressure injury development in the adult intensive care unit setting are not well understood. OBJECTIVES: To develop and validate a dynamic risk prediction model to estimate the risk of developing a hospital-acquired pressure injury among adult ICU patients. DESIGN: ICU admission data were split into training and validation sets. With death as a competing event, both static and dynamic Fine-Gray models were developed to predict hospital-acquired pressure injury development less than 24, 72, and 168 hours postadmission. Model performance was evaluated using Wolbers' concordance index, Brier score, net reclassification improvement, and integrated discrimination improvement. SETTING AND PARTICIPANTS: We performed a retrospective cohort study of ICU patients in a tertiary care hospital located in San Francisco, CA, from November 2013 to August 2017. MAIN OUTCOMES AND MEASURES: Data were extracted from electronic medical records of 18,019 ICU patients (age ≥ 18 yr; 21,220 encounters). Record of hospital-acquired pressure injury data was captured in our institution's incident reporting system. The information is periodically reviewed by our wound care team. Presence of hospital-acquired pressure injury during an encounter and hospital-acquired pressure injury diagnosis date were provided. RESULTS: The dynamic model predicting hospital-acquired pressure injury more than 24 hours postadmission, including predictors age, body mass index, lactate serum, Braden scale score, and use of vasopressor and antifungal medications, had adequate discrimination ability within 6 days from time of prediction (c = 0.73). All dynamic models produced more accurate risk estimates than static models within 26 days postadmission. There were no significant differences in Brier scores between dynamic and static models. CONCLUSIONS AND RELEVANCE: A dynamic risk prediction model predicting hospital-acquired pressure injury development less than 24 hours postadmission in ICU patients for up to 7 days postadmission was developed and validated using a large dataset of clinical variables readily available in the electronic medical record.