School of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Western Australia, Australia.
Silver Chain Group, Perth, Western Australia, Australia.
Int Wound J. 2020 Jun;17(3):823-830. doi: 10.1111/iwj.13340. Epub 2020 Mar 15.
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.
最近发表的一个预测老年人皮肤撕裂风险的模型与另外七个已发表的模型进行了比较。由于研究设计的限制，有四个模型被排除在外。使用接收者操作特性分析，比较了四个模型的相对预测性能和准确性，包括灵敏度、特异性和曲线下面积（AUC）。皮肤撕裂模型的预测能力不同，AUC 范围在 0.673 到 0.854 之间。基于预测能力，模型的选择可能会导致不同的临床决策和健康结果。已经调整了潜在混杂因素的包含五个变量（男性性别、皮肤撕裂史、跌倒史、弹性组织病的临床皮肤表现和紫癜）的模型被发现是预测老年人皮肤撕裂最简约的模型（AUC 0.854；81.7%的敏感性；81.4%的特异性）。有效的模型是识别有皮肤撕裂风险的老年人的重要临床工具，可以更好地指导更及时和有针对性的预防策略，改善健康结果并降低医疗支出。
