Hauben Manfred, Hung Eric, Hsieh Wen-Yaw
Pfizer Inc., 235 East 42nd Street, Mail stop 150-3-80W, New York, NY 10017, USA.
Pfizer Inc., New York, NY, USA.
Ther Adv Drug Saf. 2017 Jan;8(1):4-16. doi: 10.1177/2042098616670799. Epub 2016 Nov 29.
Severe cutaneous adverse reactions (SCARs) are prominent in pharmacovigilance (PhV). They have some commonalities such as nonimmediate nature and T-cell mediation and rare overlap syndromes have been documented, most commonly involving acute generalized exanthematous pustulosis (AGEP) and drug rash with eosinophilia and systemic symptoms (DRESS), and DRESS and toxic epidermal necrolysis (TEN). However, they display diverse clinical phenotypes and variations in specific T-cell immune response profiles, plus some specific genotype-phenotype associations. A question is whether causation of a given SCAR by a given drug supports causality of the same drug for other SCARs. If so, we might expect significant intercorrelations between SCARs with respect to overall drug-reporting patterns. SCARs with significant intercorrelations may reflect a unified underlying concept.
We used exploratory factor analysis (EFA) on data from the United States Food and Drug Administration Adverse Event Reporting System (FAERS) to assess reporting intercorrelations between six SCARs [AGEP, DRESS, erythema multiforme (EM), Stevens-Johnson syndrome (SJS), TEN, exfoliative dermatitis (ExfolDerm)]. We screened the data using visual inspection of scatterplot matrices for problematic data patterns. We assessed factorability Bartlett's test of sphericity, Kaiser-Myer-Olkin (KMO) statistic, initial estimates of communality and the anti-image correlation matrix. We extracted factors principle axis factoring (PAF). The number of factors was determined by scree plot/Kaiser's rule. We also examined solutions with an additional factor. We applied various oblique rotations. We assessed the strength of the solution by percentage of variance explained, minimum number of factors loading per major factor, the magnitude of the communalities, loadings and crossloadings, and reproduced- and residual correlations.
The data were generally adequate for factor analysis but the amount of variance explained, shared variance, and communalities were low, suggesting caution in general against extrapolating causality between SCARs. SJS and TEN displayed most shared variance. AGEP and DRESS, the other SCAR pair most often observed in overlap syndromes, demonstrated modest shared variance, along with maculopapular rash (MPR). DRESS and TEN, another of the more commonly diagnosed pairs in overlap syndromes, did not. EM was uncorrelated with SJS and TEN.
The notion that causality of a drug for one SCAR bolsters support for causality of the same drug with other SCARs was generally not supported.
严重皮肤不良反应（SCARs）在药物警戒（PhV）中较为突出。它们具有一些共性，如非即刻性和T细胞介导，并且已记录到罕见的重叠综合征，最常见的是涉及急性泛发性脓疱病（AGEP）、伴有嗜酸性粒细胞增多和全身症状的药物疹（DRESS），以及DRESS和中毒性表皮坏死松解症（TEN）。然而，它们表现出多样的临床表型和特定T细胞免疫反应谱的差异，以及一些特定的基因型 - 表型关联。一个问题是，一种给定药物导致某种给定的SCAR是否支持该药物对其他SCAR的因果关系。如果是这样，我们可能会预期在SCARs之间就总体药物报告模式存在显著的相互关联。具有显著相互关联的SCARs可能反映了一个统一的潜在概念。
我们对来自美国食品药品监督管理局不良事件报告系统（FAERS）的数据进行探索性因子分析（EFA），以评估六种SCARs（AGEP、DRESS、多形红斑（EM）、史蒂文斯 - 约翰逊综合征（SJS）、TEN、剥脱性皮炎（ExfolDerm））之间的报告相互关联。我们通过直观检查散点图矩阵来筛选数据，以查找有问题的数据模式。我们评估了因子可分解性、巴特利特球形检验、凯泽 - 迈耶 - 奥尔金（KMO）统计量、共同度的初始估计值以及反图像相关矩阵。我们采用主成分分析法（PAF）提取因子。因子数量由碎石图/凯泽法则确定。我们还研究了增加一个因子的解决方案。我们应用了各种斜交旋转。我们通过解释的方差百分比、每个主要因子的最小因子载荷数量、共同度的大小、载荷和交叉载荷以及再生和残差相关性来评估解决方案的强度。
数据总体上适用于因子分析，但解释的方差量、共享方差和共同度较低，这表明一般应谨慎推断SCARs之间的因果关系。SJS和TEN显示出最大的共享方差。AGEP和DRESS是重叠综合征中最常观察到的另一对SCARs，它们表现出适度的共享方差，以及斑丘疹（MPR）。DRESS和TEN是重叠综合征中另一对较常诊断的组合，但它们没有。EM与SJS和TEN不相关。
一种药物对一种SCAR的因果关系支持该药物对其他SCARs因果关系的观点通常不成立。
