Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
Department of Pediatrics, Chang Gung University College of Medicine, Kaohsiung, Taiwan.
Front Immunol. 2022 Oct 3;13:1031387. doi: 10.3389/fimmu.2022.1031387. eCollection 2022.
Kawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort.
A single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan.
Our diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.
This work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.
川崎病(KD)是儿童获得性心脏病的主要病因。KD 诊断的主要挑战在于其与其他儿童发热性疾病(FC)具有相似的临床特征。我们旨在确定我们的算法是否适用于台湾队列。
通过当地 KD 专家对疑似急性 KD 的患者进行前瞻性招募,纳入台湾长庚纪念医院的单一中心队列进行 KD 分析。我们之前开发的基于计算机的两步算法已在美国的五个中心进行了进一步验证。这项首次盲法多中心试验验证了我们的方法,其具有足够的敏感性和阳性预测值,可以识别在美国各地中心诊断的大多数 KD 患者。这项研究涉及台湾长庚纪念医院的 418 例 KD 和 259 例 FC。
我们的诊断算法保留了敏感性(418 例中的 379 例;90.7%)、特异性(259 例中的 223 例;86.1%)、阳性预测值(409 例中的 379 例;92.7%)和阴性预测值(247 例中的 223 例;90.3%),与之前美国 2016 年单中心和美国 2020 年 5 中心的结果相当。只有 4.7%(418 例中的 15 例)KD 和 2.3%(259 例中的 6 例)FC 患者被认为不确定。该算法识别出 50 例 KD 患者中的 18 例(36%)出现 2 或 3 个主要标准。在 418 例 KD 患者中,157 例为 1 岁以下婴儿,其中 89.2%(157 例中的 140 例)被正确分类。在 44 例患有冠状动脉异常的 KD 患者中,我们的诊断算法正确识别出 43 例(97.7%),包括所有发现扩张性冠状动脉的患者,但有 1 例在 8 周后发现缓解。
这项工作证明了我们的算法方法和诊断可移植性在台湾的适用性。