BACKGROUND: Evidence-based learning systems built on prediction models can support wound care community nurses (WCCNs) during diabetic foot ulcer care sessions. Several prediction models in the area of diabetic foot ulcer healing have been developed, most built on cardiovascular measurement data. Two other data types are patient information (i.e. sex and hemoglobin A1c) and wound characteristics (i.e. wound area and wound duration); these data relate to the status of the diabetic foot ulcer and are easily accessible for WCCNs. The aim of the study was to assess simple bedside wound characteristics for a prediction model for diabetic foot ulcer outcomes. METHOD: Twenty predictor variables were tested. A pattern prediction model was used to forecast whether a given diabetic foot ulcer would (i) increase in size (or not) or (ii) decrease in size. Sensitivity, specificity, and area under the curve (AUC) in a receiver-operating characteristics curve were calculated. RESULTS: A total of 162 diabetic foot ulcers were included. In combination, the predictor variables necrosis, wound size, granulation, fibrin, dry skin, and age were most informative, in total an AUC of 0.77. CONCLUSIONS: Wound characteristics have potential to predict wound outcome. Future research should investigate implementation of the prediction model in an evidence-based learning system.
BACKGROUND: Evidence-based learning systems built on prediction models can support wound care community nurses (WCCNs) during diabetic foot ulcer care sessions. Several prediction models in the area of diabetic foot ulcer healing have been developed, most built on cardiovascular measurement data. Two other data types are patient information (i.e. sex and hemoglobin A1c) and wound characteristics (i.e. wound area and wound duration); these data relate to the status of the diabetic foot ulcer and are easily accessible for WCCNs. The aim of the study was to assess simple bedside wound characteristics for a prediction model for diabetic foot ulcer outcomes. METHOD: Twenty predictor variables were tested. A pattern prediction model was used to forecast whether a given diabetic foot ulcer would (i) increase in size (or not) or (ii) decrease in size. Sensitivity, specificity, and area under the curve (AUC) in a receiver-operating characteristics curve were calculated. RESULTS: A total of 162 diabetic foot ulcers were included. In combination, the predictor variables necrosis, wound size, granulation, fibrin, dry skin, and age were most informative, in total an AUC of 0.77. CONCLUSIONS: Wound characteristics have potential to predict wound outcome. Future research should investigate implementation of the prediction model in an evidence-based learning system.
Entities:
Keywords:
chronic wound; diabetic foot ulcers; learning system; prediction model; wound care; wound care community nurse; wound observations
Authors: Zhen Wang; Rim Hasan; Belal Firwana; Tarig Elraiyah; Apostolos Tsapas; Larry Prokop; Joseph L Mills; Mohammad Hassan Murad Journal: J Vasc Surg Date: 2016-02 Impact factor: 4.268
Authors: Clara Bender; Simon Lebech Cichosz; Alberto Malovini; Riccardo Bellazzi; Louise Pape-Haugaard; Ole Hejlesen Journal: J Diabetes Sci Technol Date: 2021-02-15
Authors: Murat K Jakanov; Bazylbek S Zhakiev; Uteugaly G Karsakbayev; Bulat A Kurmanbayev; Kairat R Taishibayev; Serik K Sagynganov Journal: Med J Islam Repub Iran Date: 2021-08-18