Hartman Rebecca I, Xue Yun, Karmouta Ryan, Tkachenko Elizabeth, Li Sara J, Li David G, Joyce Cara, Mostaghimi Arash
Department of Dermatology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Harvard Combined Dermatology Residency Training Program, Harvard Medical School, Boston, MA, USA.
Dermatol Res Pract. 2022 Aug 16;2022:2313896. doi: 10.1155/2022/2313896. eCollection 2022.
There is insufficient evidence to generate skin cancer screening guidelines at the population level, resulting in arbitrary variation in patient selection for screening skin examinations. This study was aimed at developing an easy-to-use predictive model of nonmelanoma skin cancer (NMSC) risk on screening total body skin examination (TBSE).
This epidemiologic assessment utilized data from a prospective, multicenter international study from primarily academic outpatient dermatology clinics. Potential predictors of NMSC on screening TBSE were identified and used to generate a multivariable model that was converted into a point-based scoring system. The performance characteristics of the model were validated in a second data set from two healthcare institutions in the United States.
8,501 patients were included. Statistically significant predictors of NMSC on screening TBSE included age, skin phototype, and history of NMSC. A multivariable model and point-based scoring system using these predictors exhibited high discrimination (AUC = 0.82).
A simple three-variable model, abbreviated as CAP (cancer history, age, phototype) can accurately predict the risk of NMSC on screening TBSE by dermatology. This tool may be used in clinical decision making to enhance the yield of screening TBSE.
目前尚无足够证据制定针对人群层面的皮肤癌筛查指南,这导致在选择接受皮肤检查筛查的患者时存在随意性差异。本研究旨在开发一种易于使用的预测模型,用于在筛查全身皮肤检查(TBSE)时评估非黑色素瘤皮肤癌(NMSC)的风险。
这项流行病学评估利用了一项前瞻性、多中心国际研究的数据,该研究主要来自学术性门诊皮肤科诊所。确定了在筛查TBSE时NMSC的潜在预测因素,并用于生成一个多变量模型,该模型被转换为基于分数的评分系统。该模型的性能特征在来自美国两家医疗机构的第二个数据集中得到验证。
纳入了8501名患者。在筛查TBSE时,NMSC的统计学显著预测因素包括年龄、皮肤光类型和NMSC病史。使用这些预测因素的多变量模型和基于分数的评分系统具有较高的区分度(AUC = 0.82)。
一个简单的三变量模型,简称为CAP(癌症病史、年龄、光类型),可以准确预测皮肤科在筛查TBSE时NMSC的风险。该工具可用于临床决策,以提高筛查TBSE的收益。