Scherdel Pauline, Reynaud Rachel, Pietrement Christine, Salaün Jean-François, Bellaïche Marc, Arnould Michel, Chevallier Bertrand, Piloquet Hugues, Jobez Emmanuel, Cheymol Jacques, Bichara Emmanuelle, Heude Barbara, Chalumeau Martin
INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center (CRESS), early ORigins of the Child's Health and Development Team (ORCHaD), Paris Descartes University, Paris, France.
INSERM, UMR1153 Epidemiology and Biostatistics Sorbonne Paris Cité Center (CRESS), Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Paris Descartes University, Paris, France.
PLoS One. 2017 Apr 27;12(4):e0176464. doi: 10.1371/journal.pone.0176464. eCollection 2017.
Growth monitoring of apparently healthy children aims at early detection of serious conditions through the use of both clinical expertise and algorithms that define abnormal growth. Optimization of growth monitoring requires standardization of the definition of abnormal growth, and the selection of the priority target conditions is a prerequisite of such standardization.
To obtain a consensus about the priority target conditions for algorithms monitoring children's growth.
We applied a formal consensus method with a modified version of the RAND/UCLA method, based on three phases (preparatory, literature review, and rating), with the participation of expert advisory groups from the relevant professional medical societies (ranging from primary care providers to hospital subspecialists) as well as parent associations. We asked experts in the pilot (n = 11), reading (n = 8) and rating (n = 60) groups to complete the list of diagnostic classification of the European Society for Paediatric Endocrinology and then to select the conditions meeting the four predefined criteria of an ideal type of priority target condition.
Strong agreement was obtained for the 8 conditions selected by the experts among the 133 possible: celiac disease, Crohn disease, craniopharyngioma, juvenile nephronophthisis, Turner syndrome, growth hormone deficiency with pituitary stalk interruption syndrome, infantile cystinosis, and hypothalamic-optochiasmatic astrocytoma (in decreasing order of agreement).
This national consensus can be used to evaluate the algorithms currently suggested for growth monitoring. The method used for this national consensus could be re-used to obtain an international consensus.
对看似健康的儿童进行生长监测旨在通过运用临床专业知识和定义异常生长的算法来早期发现严重疾病。优化生长监测需要对异常生长的定义进行标准化，而选择优先目标疾病是这种标准化的前提条件。
就监测儿童生长的算法的优先目标疾病达成共识。
我们应用了一种基于兰德/加州大学洛杉矶分校方法的改进版正式共识方法，该方法包括三个阶段（准备阶段、文献综述阶段和评级阶段），相关专业医学协会（从初级保健提供者到医院专科医生）以及家长协会的专家咨询小组参与其中。我们要求试点组（n = 11）、阅读组（n = 8）和评级组（n = 60）的专家完成欧洲儿科内分泌学会的诊断分类列表，然后选择符合理想优先目标疾病类型的四个预定义标准的疾病。
专家们在133种可能的疾病中选出的8种疾病达成了强烈共识：乳糜泻、克罗恩病、颅咽管瘤、青少年肾单位肾痨、特纳综合征、伴有垂体柄中断综合征的生长激素缺乏症、婴儿胱氨酸病和下丘脑 - 视交叉神经胶质瘤（按共识程度从高到低排列）。
这一全国性共识可用于评估目前建议用于生长监测的算法。用于达成这一全国性共识的方法可再次用于达成国际共识。
