Algorithms to Define Abnormal Growth in Children

External Validation and Head-to-Head Comparison

Pauline Scherdel; Soraya Matczak; Juliane Léger; Christine Martinez-Vinson; Olivier Goulet; Raja Brauner; Sophie Nicklaus; Matthieu Resche-Rigon; Martin Chalumeau; Barbara Heude

Disclosures

J Clin Endocrinol Metab. 2019;104(2):241-249. 

In This Article

Abstract and Introduction

Abstract

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) vs national growth charts on algorithm performance.

Design: With a case-referent approach, we retrospectively applied all algorithms to growth data for children with Turner syndrome, GH deficiency, or celiac disease (n = 341) as well as apparently healthy children (n = 3406). 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 higherwith 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.

Introduction

Growth monitoring of apparently healthy children aims at early detection of serious conditions.[1] This monitoring combines clinical expertise and the use of algorithms defining abnormal growth.[1] Substantial empirical evidence shows that the actual practice of growth monitoring is suboptimal worldwide, with long diagnostic delays for target conditions as severe as GH deficiency (GHD), Turner syndrome, celiac disease, Crohn disease, hypothalamic-optochiasmatic tumors, and chronic kidney disease,[2–4] and large numbers of futile referrals for children with normal variants of growth.[5–7] We have shown that the main cause of this suboptimal monitoring is the lack of standardization of primary care physician practice with validated tools.[8,9]

Seven algorithms have been proposed to define abnormal growth (Supplemental Table 1).[10] Some are based on straightforward unique thresholds and others on more complex combinations of auxological parameters (see example in Supplemental Figure 1). In a recent systematic review, the Grote and Saari clinical decision rules seemed the most promising, with high specificity (>96%) and acceptable sensitivity (>60%) in derivation samples.[10] However, the level of validation of these algorithms is low and limits their potential implementation in daily practice.[10] Furthermore, although these algorithms often provide contradictory indications about the abnormality of the growth of a given child, all available algorithms have never been compared head to head in terms of diagnostic performance.

External validation studies and head-to-head comparisons must take into account the international growth charts published by the World Health Organization (WHO) in 2006.[11,12] We and others have shown that for some countries, the distribution of height, weight, and body mass index (BMI) of contemporary children was closer to WHO growth charts than national references of their own country, except for the first 6 months of age.[10,13] Nevertheless, the potential impact of the growth charts used on the performance of algorithms to define abnormal growth has been investigated in only two studies, performed in Finland and the United States, showing higher sensitivity of national than WHO growth charts for detecting cystic fibrosis and Turner syndrome, with lower specificity.[14,15]

To improve growth-monitoring practices in the primary care setting by providing more validated and accurate tools, we performed, to our knowledge, the first external validation study with a head-to-head comparison of all existing algorithms and studied the potential impact of the use of WHO vs national French growth charts on their performance.

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