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. 2020 Jun;17(3):823-830.
doi: 10.1111/iwj.13340. Epub 2020 Mar 15.

Models for predicting skin tears: A comparison

Robyn Rayner  1   2 , Keryln Carville  1   2 , Gavin Leslie  1 , Satvinder S Dhaliwal  3
Affiliations

Models for predicting skin tears: A comparison

Robyn Rayner et al. Int Wound J. 2020 Jun.

Abstract

A recently published model that predicted the risk of skin tears in older adults was compared with seven additional published models. Four models were excluded because of limitations in research design. Four models were compared for their relative predictive performance and accuracy using sensitivity, specificity, and the area under the curve (AUC), which involved using receiver-operating characteristic analysis. The predictive ability of the skin tear models differed with the AUC ranging between 0.673 and 0.854. Based on the predictive ability, the selection of models could lead to different clinical decisions and health outcomes. The model, which had been adjusted for potential confounders consisted of five variables (male gender, history of skin tears, history of falls, clinical skin manifestations of elastosis, and purpura), was found to be the most parsimonious for predicting skin tears in older adults (AUC 0.854; 81.7% sensitivity; 81.4% specificity). Effective models serve as important clinical tools for identifying older individuals at risk of skin tears and can better direct more timely and targeted prevention strategies that improve health outcomes and reduce health care expenditure.

Keywords: aged-care residents; elderly; predictive models; skin tears.

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Figures

Figure 1
Figure 1
Comparison of receiver‐operating characteristic (ROC) curves for the five skin tear models
Figure 2
Figure 2
Predicted risk of skin tears at 6 months for females with clinical manifestations of purpura and elastosis
Figure 3
Figure 3
Predicted risk of skin tears at 6 months for males with clinical manifestations of purpura and elastosis
See this image and copyright information in PMC

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