Growth monitoring as an early detection tool: a systematic review.

Abstract

Growth monitoring of apparently healthy children aims at early detection of serious underlying disorders. However, existing growth-monitoring practices are mainly based on suboptimal methods, which can result in delayed diagnosis of severe diseases and inappropriate referrals. We did a systematic review to address two key and interconnected questions underlying growth monitoring: which conditions should be targeted, and how should abnormal growth be defined? We systematically searched for studies reporting algorithms for growth monitoring in children and studies comparing the performance of new WHO growth charts with that of other growth charts. Among 1556 identified citations, 69 met the inclusion criteria. Six target conditions have mainly been studied: Turner syndrome, coeliac disease, cystic fibrosis, growth hormone deficiency, renal tubular acidosis, and small for gestational age with no catch-up after 2 or 3 years. Seven algorithms to define abnormal growth have been proposed in the past 20 years, but their level of validation is low, and their overall sensitivities and specificities vary substantially; however, the Grote and Saari clinical decision rules seem the most promising. Two studies reported that WHO growth charts had poorer performance compared with other existing growth charts for early detection of target conditions. Available data suggest a large gap between the widespread implementation of growth monitoring and its level of evidence or the clinical implications of early detection of serious disorders in children. Further investigations are needed to standardise the practice of growth monitoring, with a consensus on a few priority target conditions and with internationally validated clinical decision rules to define abnormal growth, including the selection of appropriate growth charts.

• Authors: Pauline Scherdel, Leo Dunkel, Paula van Dommelen, Olivier Goulet, Jean-François Salaün, Raja Brauner, Barbara Heude, Martin Chalumeau
• Journal: The lancet. Diabetes & endocrinology (Lancet Diabetes Endocrinol) Vol. 4 Issue 5 Pg. 447-56 (05 2016) ISSN: 2213-8595 [Electronic] England
• PMID: 26777129 (Publication Type: Journal Article, Review, Systematic Review)
• Copyright: Copyright © 2016 Elsevier Ltd. All rights reserved.

Topics

• Algorithms
• Early Diagnosis
• Growth Charts
• Growth Disorders (diagnosis)
• Humans
• Mass Screening (standards)