External validation of a predictive model for aged-related skin tears in Fitzpatrick skin types I-IV cohort.

Older Australians are at increased risk of skin tears with the risk not always recognised or the injury able to be prevented. This study externally validated Rayner et al. (2019) Skin Tear Risk Prediction Model in an independent aged cohort with a Fitzpatrick skin types I-IV from across multiple residential-care sites, over a 6-month period. A total of 362 individuals aged between 65 and 102.5 years completed the study. In all, 165-residents sustained one or more skin tears. Logistic regression analysis was conducted of the five variables (gender, previous history of skin tears, previous history of falls, purpura and solar elastosis) identified in the skin tear model. The skin tear model provided 'good' to nearly 'very good discrimination' for correctly classifying residents at-risk or not-at-risk (area under the curve of 0.799 [95% confidence interval, CI: 0.75-0.84]). The skin tear model correctly predicted 75.8% (sensitivity) of participants with skin tears and 71.6% (specificity) of residents without skin tears. The model demonstrated it could work as a screening tool to identify older individuals at risk of skin tears and would benefit clinical practice as it was easy to use, was reproducible, and had good accuracy across aged-care residents with a Fitzpatrick skin type I-IV.

Models for predicting skin tears: A comparison.

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.

A risk model for the prediction of skin tears in aged care residents: A prospective cohort study.

The objective of this study was to construct a predictive model to identify aged care residents at risk of future skin tears. Extensive data about individual characteristics, skin characteristics, and skin properties were gathered from 173 participants at baseline and at 6 months. A predictive model, developed using multivariable logistic regression, identified five variables that significantly predicted the risk of skin tear at 6 months. These included: a history of skin tears in the previous 12 months (OR 3.82 [1.64-8.90], P = 0.002), purpura ≤20 mm in size (OR 3.64 [1.42-9.35], P = 0.007), a history of falls in the previous 3 months (OR 3.37 [1.54-7.41], P = 0.002), clinical manifestations of elastosis (OR 3.19 [1.38-7.38], P = 0.007), and male gender (OR 3.08 [1.22-7.77], P = 0.017). The predictive model yielded an area under the receiver operating characteristic curve of 0.854 with an 81.7% sensitivity and an 81.4% specificity. This predictive model could inform a simple but promising bedside tool for identifying older individuals at risk of skin tears.

Measurement of morphological and physiological skin properties in aged care residents: a test-retest reliability pilot study.

This test-retest pilot study investigated the intra-rater reliability and reproducibility of non-invasive technologies to objectively quantify morphological (colour, thickness and elasticity) and physiological (transepidermal water loss (TEWL), hydration, sebum and pH) skin properties in an aged care population. Three consecutive measurements were taken from five anatomical skin sites, with the mean of each measurement calculated. The intra-class correlation coefficient (ICC) and the standard error of measurement (SEM) were used to examine the intra-rater reliability and reproducibility of measurements. Non-invasive technologies in this study showed almost perfect reliability for ultrasound measurements of the subepidermal low echogenicity band (SLEB) (ρ = 0·95-0·99) and skin thickness (ρ = 0·95-0·99) across all sites. The ICC was substantial to almost perfect for pH (ρ = 0·76-0·88) and viscoelasticity (ρ = 0·67-0·91) across all sites. Hydration (ρ = 0·53-0·85) and skin retraction (ρ = 0·57-0·99) measurements ranged from moderate to almost perfect across all sites. TEWL and elasticity were substantial to almost perfect across four sites. Casual sebum levels and most colour parameters showed poor ICC. The use of non-invasive technologies in this study provided an objective and reliable means for quantifying ageing skin and may offer future studies a valuable option for assessing skin tear risk.