Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
BMC Med. 2011 Dec 6;9:130. doi: 10.1186/1741-7015-9-130.
Kawasaki disease is an acute vasculitis of infants and young children that is recognized through a constellation of clinical signs that can mimic other benign conditions of childhood. The etiology remains unknown and there is no specific laboratory-based test to identify patients with Kawasaki disease. Treatment to prevent the complication of coronary artery aneurysms is most effective if administered early in the course of the illness. We sought to develop a diagnostic algorithm to help clinicians distinguish Kawasaki disease patients from febrile controls to allow timely initiation of treatment.
Urine peptidome profiling and whole blood cell type-specific gene expression analyses were integrated with clinical multivariate analysis to improve differentiation of Kawasaki disease subjects from febrile controls.
Comparative analyses of multidimensional protein identification using 23 pooled Kawasaki disease and 23 pooled febrile control urine peptide samples revealed 139 candidate markers, of which 13 were confirmed (area under the receiver operating characteristic curve (ROC AUC 0.919)) in an independent cohort of 30 Kawasaki disease and 30 febrile control urine peptidomes. Cell type-specific analysis of microarrays (csSAM) on 26 Kawasaki disease and 13 febrile control whole blood samples revealed a 32-lymphocyte-specific-gene panel (ROC AUC 0.969). The integration of the urine/blood based biomarker panels and a multivariate analysis of 7 clinical parameters (ROC AUC 0.803) effectively stratified 441 Kawasaki disease and 342 febrile control subjects to diagnose Kawasaki disease.
A hybrid approach using a multi-step diagnostic algorithm integrating both clinical and molecular findings was successful in differentiating children with acute Kawasaki disease from febrile controls.
川崎病是一种婴幼儿急性血管炎,其特征是一系列临床体征,这些体征可能与儿童的其他良性疾病相似。病因仍不清楚,也没有基于实验室的特定测试来识别川崎病患者。如果在疾病早期进行治疗,预防冠状动脉瘤并发症的效果最佳。我们试图开发一种诊断算法,帮助临床医生区分川崎病患者和发热对照组,以便及时开始治疗。
尿肽组学分析和全血细胞类型特异性基因表达分析与临床多变量分析相结合,以提高川崎病患者与发热对照组的区分能力。
使用 23 个川崎病和 23 个发热对照组尿液肽样本的多维蛋白质鉴定比较分析显示了 139 个候选标志物,其中 13 个标志物在 30 个川崎病和 30 个发热对照组尿液肽样本的独立队列中得到了验证(ROC 曲线下面积(AUC)0.919)。对 26 个川崎病和 13 个发热对照组全血样本的细胞类型特异性分析(csSAM)显示了一个 32 个淋巴细胞特异性基因表达谱(AUC 0.969)。基于尿液/血液的生物标志物谱的整合和 7 个临床参数的多变量分析(AUC 0.803)有效地将 441 个川崎病和 342 个发热对照组患者分层,以诊断川崎病。
使用多步骤诊断算法,整合临床和分子发现的混合方法,成功地区分了患有急性川崎病的儿童和发热对照组。