Algorithms to define abnormal growth in children: external validation and head-to-head comparison
2019
Scherdel, Pauline | Matczak, Soraya | Léger, Juliane | Martinez-Vinson, Christine | Goulet, Olivier | Brauner, Raja | Nicklaus, Sophie | Resche-Rigon, Matthieu | Chalumeau, Martin | Heude, Barbara | Université de Montpellier (UM) | Equipe 1 : EPOPé - Épidémiologie Obstétricale, Périnatale et Pédiatrique (CRESS - U1153) ; Université Paris Descartes - Paris 5 (UPD5)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)) ; Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM) | Hôpital Necker - Enfants Malades [AP-HP] ; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP) | AP-HP Hôpital universitaire Robert-Debré [Paris] ; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP) | Fondation Ophtalmologique Adolphe de Rothschild [Paris] | Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA) ; Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS) | Université Bourgogne Franche-Comté [COMUE] (UBFC) | Equipe 2 : ECSTRA - Epidémiologie Clinique, STatistique, pour la Recherche en Santé (CRESS - U1153) ; Université Paris Diderot - Paris 7 (UPD7)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)) ; Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM) | Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)) ; Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM) | Equipe 6 : ORCHAD - Origines précoces de la santé du développement de l'enfant (CRESS - U1153) ; Université Paris Descartes - Paris 5 (UPD5)-Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)) ; Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris Descartes - Paris 5 (UPD5)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Recherche Agronomique (INRA)-Université Paris Diderot - Paris 7 (UPD7)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)
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Show more [+] Less [-]English. Background: Growth monitoring of apparently healthy children aims at early detection of serious conditions by use of both clinical expertise and algorithms that define abnormal growth. The seven existing algorithms provide contradictory definitions of growth abnormality and have a low level of validation. Objective: An external validation study with head-to-head comparison of the seven algorithms combined with study of the impact of use of the World Health Organization (WHO) versus national growth charts on algorithm performance. Study design: With a case-referent approach, we retrospectively applied all algorithms to growth data for children with Turner syndrome, growth hormone deficiency, or celiac disease (n= 341) as well as apparently healthy children (n=3,406). Sensitivity, specificity, and theoretical reduction in time to diagnosis for each algorithm were calculated for each condition by using the WHO or national growth charts. Results: Among the two algorithms with high specificity (> 98%), the Grote clinical decision rule had higher sensitivity than the Coventry consensus (4.6% to 54% vs 0% to 8.9%, p<0.05) and offered better theoretical reduction in time to diagnosis (median 0.0 to 0.9 years vs 0 years, p<0.05). Sensitivity values were significantly higher with the WHO than national growth charts at the expense of specificity. Conclusion: The Grote clinical decision rule had the best performance for early detection of the three studied diseases, but its limited potential for reducing time to diagnosis suggests the need for better-performing algorithms based on appropriate growth charts.
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