Department of Dermatology, All India Institute of Medical Sciences, New Delhi, India.
Department of Dermatology, Birmingham Children's Hospital, Birmingham, U.K.
Br J Dermatol. 2017 Jun;176(6):1624-1632. doi: 10.1111/bjd.15221. Epub 2017 May 5.
Accurately diagnosing the subtype of epidermolysis bullosa (EB) is critical for management and genetic counselling. Modern laboratory techniques are largely inaccessible in developing countries, where the diagnosis remains clinical and often inaccurate.
To develop a simple clinical diagnostic tool to aid in the diagnosis and subtyping of EB.
We developed a matrix indicating presence or absence of a set of distinctive clinical features (as rows) for the nine most prevalent EB subtypes (as columns). To test an individual patient, presence or absence of these features was compared with the findings expected in each of the nine subtypes to see which corresponded best. If two or more diagnoses scored equally, the diagnosis with the greatest number of specific features was selected. The matrix was tested using findings from 74 genetically characterized patients with EB aged > 6 months by an investigator blinded to molecular diagnosis. For concordance, matrix diagnoses were compared with molecular diagnoses.
Overall, concordance between the matrix and molecular diagnoses for the four major types of EB was 91·9%, with a kappa coefficient of 0·88 [95% confidence interval (CI) 0·81-0·95; P < 0·001]. The matrix achieved a 75·7% agreement in classifying EB into its nine subtypes, with a kappa coefficient of 0·73 (95% CI 0·69-0·77; P < 0·001).
The matrix appears to be simple, valid and useful in predicting the type and subtype of EB. An electronic version will facilitate further testing.
准确诊断大疱性表皮松解症(EB)对于管理和遗传咨询至关重要。现代实验室技术在发展中国家基本无法获得,而这些国家的诊断仍然依赖临床且往往不够准确。
开发一种简单的临床诊断工具,以帮助诊断和分型 EB。
我们设计了一个矩阵,其中列出了九种最常见的 EB 亚型(作为列)的一组独特临床特征(作为行)的存在或缺失。为了测试个体患者,将这些特征的存在或缺失与九种亚型中的每一种的预期发现进行比较,以确定最符合的亚型。如果两个或更多诊断得分相等,则选择具有最多特定特征的诊断。该矩阵通过一位对分子诊断不知情的研究者使用 74 名年龄大于 6 个月的基因特征明确的 EB 患者的发现进行了测试。为了进行一致性比较,将矩阵诊断与分子诊断进行了比较。
总体而言,矩阵和分子诊断在四种主要 EB 类型之间的一致性为 91.9%,kappa 系数为 0.88(95%置信区间 0.81-0.95;P<0.001)。该矩阵在将 EB 分为九种亚型方面的准确率为 75.7%,kappa 系数为 0.73(95%置信区间 0.69-0.77;P<0.001)。
该矩阵似乎简单、有效且可用于预测 EB 的类型和亚型。电子版本将方便进一步测试。