Data_Sheet_1_A Novel Framework for Phenotyping Children With Suspected or Confirmed Infection for Future Biomarker Studies.docx

Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI.
Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0– <16 years: the Alder Hey emergency department (n = 1,120), Alder Hey pediatric intensive care unit (n = 355), Erasmus emergency department (n = 1,993), Maasstad emergency department (n = 714) and St. Mary's hospital (n = 200) cohorts. Biomarkers including procalcitonin (PCT) (4 cohorts), neutrophil gelatinase-associated lipocalin-2 (NGAL) (3 cohorts) and resistin (2 cohorts) were compared for their ability to classify patients according to current standards (dichotomous classification of SBI vs. non-SBI), vs. a proposed PERFORM classification algorithm that assign patients to one of eleven categories. These categories were based on clinical phenotype, test outcomes and C-reactive protein level and accounted for the uncertainty of final diagnosis in many febrile children. The success of the biomarkers was measured by the Area under the receiver operating Curves (AUCs) when they were used individually or in combination.
Results: Using the new PERFORM classification system, patients with clinically confident bacterial diagnosis (“definite bacterial” category) had significantly higher levels of PCT, NGAL and resistin compared with those with a clinically confident viral diagnosis (“definite viral” category). Patients with diagnostic uncertainty had biomarker concentrations that varied across the spectrum. AUCs were higher for classification of “definite bacterial” vs. “definite viral” following the PERFORM algorithm than using the “SBI” vs. “non-SBI” classification; summary AUC for PCT was 0.77 (95% CI 0.72–0.82) vs. 0.70 (95% CI 0.65–0.75); for NGAL this was 0.80 (95% CI 0.69–0.91) vs. 0.70 (95% CI 0.58–0.81); for resistin this was 0.68 (95% CI 0.61–0.75) vs. 0.64 (0.58–0.69) The three biomarkers combined had summary AUC of 0.83 (0.77–0.89) for “definite bacterial” vs. “definite viral” infections and 0.71 (0.67–0.74) for “SBI” vs. “non-SBI.”
Conclusion: Biomarkers of bacterial infection were strongly associated with the diagnostic categories using the PERFORM classification system in five independent cohorts. Our proposed algorithm provides a novel framework for phenotyping children with suspected or confirmed infection for future biomarker studies.

研究背景：针对存在严重细菌感染（serious bacterial infection, SBI）风险的儿童，现有生物标志物（biomarker）的诊断准确率有限，这可能源于SBI的参考标准尚不完善。本研究旨在评估一种全新的分类算法在SBI风险儿童的生物标志物筛选中的诊断效能。
研究方法：本研究纳入五项已发表的前瞻性观察性生物标志物筛选研究数据，研究对象均为0~<16岁的儿童，分别来自：奥尔德黑急诊科队列（n=1120）、奥尔德黑儿童重症监护室队列（n=355）、伊拉斯谟急诊科队列（n=1993）、马斯塔德急诊科队列（n=714）以及圣玛丽医院队列（n=200）。本研究对比了多种生物标志物的患者分类能力，包括降钙素原（procalcitonin, PCT，覆盖4个队列）、中性粒细胞明胶酶相关脂质运载蛋白-2（neutrophil gelatinase-associated lipocalin-2, NGAL，覆盖3个队列）以及抵抗素（resistin，覆盖2个队列）。对比标准分为两类：一是当前通用的二分法分类（SBI与非SBI），二是本研究提出的PERFORM分类算法，后者可将患者划分为11个类别。该11个类别基于临床表型、检测结果以及C反应蛋白（C-reactive protein）水平构建，可反映多数发热儿童最终诊断的不确定性。生物标志物的分类效能以受试者工作特征曲线下面积（Area under the receiver operating characteristic curve, AUC）进行评估，分别评估单一生物标志物以及多标志物联合应用的情况。
研究结果：采用全新的PERFORM分类系统时，临床确诊细菌感染的患者（「明确细菌感染」类别）的降钙素原、中性粒细胞明胶酶相关脂质运载蛋白-2以及抵抗素水平，显著高于临床确诊病毒感染的患者（「明确病毒感染」类别）。存在诊断不确定性的患者，其生物标志物浓度呈连续分布状态。相较于「SBI与非SBI」二分法分类，采用PERFORM算法进行「明确细菌感染」与「明确病毒感染」分类时，各生物标志物的AUC均更高：降钙素原的合并AUC分别为0.77（95%置信区间：0.72~0.82）与0.70（95%置信区间：0.65~0.75）；中性粒细胞明胶酶相关脂质运载蛋白-2分别为0.80（95%置信区间：0.69~0.91）与0.70（95%置信区间：0.58~0.81）；抵抗素分别为0.68（95%置信区间：0.61~0.75）与0.64（95%置信区间：0.58~0.69）。三种生物标志物联合应用时，「明确细菌感染」与「明确病毒感染」分类的合并AUC为0.83（95%置信区间：0.77~0.89），而「SBI与非SBI」分类的合并AUC为0.71（95%置信区间：0.67~0.74）。
研究结论：在五项独立队列中，细菌感染相关生物标志物与PERFORM分类系统的诊断类别存在显著关联。本研究提出的分类算法为疑似或确诊感染儿童的表型分型提供了全新框架，可用于后续的生物标志物研究。