Improving pressure injury risk assessment using real-world data from skilled nursing facilities: A cohort study.

This study aimed to improve the predictive accuracy of the Braden assessment for pressure injury risk in skilled nursing facilities (SNFs) by incorporating real-world data and training a survival model. A comprehensive analysis of 126 384 SNF stays and 62 253 in-house pressure injuries was conducted using a large calibrated wound database. This study employed a time-varying Cox Proportional Hazards model, focusing on variations in Braden scores, demographic data and the history of pressure injuries.

Towards an AI-Based Objective Prognostic Model for Quantifying Wound Healing.

Chronic wounds affect millions of people worldwide every year. An adequate assessment of a wound's prognosis is critical to wound care, guiding clinical decision making by helping clinicians understand wound healing status, severity, triaging and determining the efficacy of a treatment regimen. The current standard of care involves using wound assessment tools, such as Pressure Ulcer Scale for Healing (PUSH) and Bates-Jensen Wound Assessment Tool (BWAT), to determine wound prognosis.

Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study.

Background: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective.