A Novel Framework for Phenotyping Children With Suspected or Confirmed Infection for Future Biomarker Studies.
A Novel Framework for Phenotyping Children With Suspected or Confirmed Infection for Future Biomarker Studies.
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DOI：
10.3389/fped.2021.688272
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发表时间：
2021
影响因子：
2.6
通讯作者：
PERFORM consortium (Personalized Risk assessment in febrile children to optimize Real-life Management across the European Union)
中科院分区：
文献类型：
--
作者：
Nijman RG;Oostenbrink R;Moll HA;Casals-Pascual C;von Both U;Cunnington A;De T;Eleftheriou I;Emonts M;Fink C;van der Flier M;de Groot R;Kaforou M;Kohlmaier B;Kuijpers TW;Lim E;Maconochie IK;Paulus S;Martinon-Torres F;Pokorn M;Romaine ST;Calle IR;Schlapbach LJ;Smit FJ;Tsolia M;Usuf E;Wright VJ;Yeung S;Zavadska D;Zenz W;Levin M;Herberg JA;Carrol ED;PERFORM consortium (Personalized Risk assessment in febrile children to optimize Real-life Management across the European Union)
背景：对于有严重细菌感染（SBI）风险的儿童，生物标志物的诊断准确性有限，可能是由于 SBI 参考标准不完善所致。我们的目的是评估用于发现 SBI 风险儿童生物标志物的新分类算法的诊断性能。方法：我们使用了五项先前发表的前瞻性观察性生物标志物发现研究的数据，其中包括 0-<16 岁的患者：Alder Hey 急诊科 (n = 1,120)、Alder Hey 儿科重症监护病房 (n = 355)、伊拉斯谟急诊科 (n = 1,993)、马斯塔德急诊科 (n = 714) 和圣玛丽医院 (n = 200) 队列。比较了包括降钙素原 (PCT)（4 个队列）、中性粒细胞明胶酶相关脂质运载蛋白 2 (NGAL)（3 个队列）和抵抗素（2 个队列）在内的生物标志物根据当前标准（SBI 与非 SBI 的二分分类）对患者进行分类的能力，以及将患者分配到 11 个类别之一的拟议 PERFORM 分类算法。这些类别基于临床表型、测试结果和 C 反应蛋白水平，并解释了许多发热儿童最终诊断的不确定性。生物标志物的成功通过单独或组合使用时的受试者工作曲线下面积 (AUC) 来衡量。结果：使用新的 PERFORM 分类系统，与临床上确信病毒诊断（“确定病毒”类别）的患者相比，临床上确信细菌诊断（“确定细菌”类别）的患者的 PCT、NGAL 和抵抗素水平显着更高。诊断不确定的患者的生物标志物浓度在各个范围内变化。使用 PERFORM 算法进行“确定细菌”与“确定病毒”分类的 AUC 高于使用“SBI”与“非 SBI”分类的 AUC； PCT 的总结 AUC 为 0.77 (95% CI 0.72–0.82) vs. 0.70 (95% CI 0.65–0.75)；对于 NGAL，该值为 0.80 (95% CI 0.69–0.91) vs. 0.70 (95% CI 0.58–0.81)；对于抵抗素，该值为 0.68 (95% CI 0.61–0.75) 对比 0.64 (0.58–0.69)。三种生物标记物的总 AUC 分别为：“确定的细菌”与“确定的病毒”感染与“确定的病毒”感染，分别为 0.83 (0.77-0.89)；“SBI”与“确定的病毒”感染，其总 AUC 分别为 0.71 (0.67-0.74)。 “非 SBI。”结论：细菌感染的生物标志物与五个独立队列中使用 PERFORM 分类系统的诊断类别密切相关。我们提出的算法为未来生物标志物研究提供了一个新的框架，用于对疑似或确诊感染的儿童进行表型分析。
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.
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影响因子：
105.7
作者：
Craig, Jonathan C.;Williams, Gabrielle J.;McCaskill, Mary
通讯作者：
McCaskill, Mary
影响因子：
5.2
作者：
Carrol, ED;Thomson, APJ;Hart, CA
通讯作者：
Hart, CA
影响因子：
9.3
作者：
Leigh, Simon;Grant, Alison;Carrol, Enitan D.
通讯作者：
Carrol, Enitan D.
影响因子：
8.8
作者：
Levy, Mitchell M.;Evans, Laura E.;Rhodes, Andrew
通讯作者：
Rhodes, Andrew
DOI：
10.1001/jama.2016.11236
发表时间：
2016-08-23
期刊：
JAMA
影响因子：
--
作者：
Herberg JA;Kaforou M;Wright VJ;Shailes H;Eleftherohorinou H;Hoggart CJ;Cebey-López M;Carter MJ;Janes VA;Gormley S;Shimizu C;Tremoulet AH;Barendregt AM;Salas A;Kanegaye J;Pollard AJ;Faust SN;Patel S;Kuijpers T;Martinón-Torres F;Burns JC;Coin LJ;Levin M;IRIS Consortium
通讯作者：
IRIS Consortium