Survey highlights important discrepancies between definitions of paediatric abnormal growth taught to medical students in 23 European countries

New French height velocity growth charts: An innovative big-data approach based on routine measurements

AIM

Height velocity is considered a key auxological tool to monitor growth, but updated height velocity growth charts are lacking. We aimed to derive new French height velocity growth charts by using a big-data approach based on routine measurements.

METHODS

We extracted all growth data of children aged 1 month-18 years from the electronic medical records of 42 primary care physicians, between 1 January 1990 and 8 February 2018, throughout the French metropolitan territory. We derived annual and biannual height velocity growth charts until age 15 years by using the Lambda-Mu-Sigma method. These new growth charts were compared to the 1979 French and 2009 World Health Organisation (WHO) ones.

RESULTS

New height velocity growth charts were generated with 193 124 and 209 221 annual and biannual values from 80 204 and 87 260 children, respectively, and showed good internal fit. Median curves were close to the 1979 French or 2009 WHO ones, but SD curves displayed important differences. Similar results were found with the biannual height velocity growth charts.

CONCLUSION

We produced new height velocity growth charts until age 15 years by using a big-data approach applied to measurements routinely collected in clinical practice. These updated growth charts could help optimise growth-monitoring performance.

Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature

Assessment and management of children with growth failure has improved greatly over recent years. However, there remains a strong potential for further improvements by using novel digital techniques. A panel of experts discussed developments in digitalization of a number of important tools used by pediatric endocrinologists at the third 360° European Meeting on Growth and Endocrine Disorders, funded by Merck KGaA, Germany, and this review is based on those discussions. It was reported that electronic monitoring and new algorithms have been devised that are providing more sensitive referral for short stature. In addition, computer programs have improved ways in which diagnoses are coded for use by various groups including healthcare providers and government health systems. Innovative cranial imaging techniques have been devised that are considered safer than using gadolinium contrast agents and are also more sensitive and accurate. Deep-learning neural networks are changing the way that bone age and bone health are assessed, which are more objective than standard methodologies. Models for prediction of growth response to growth hormone (GH) treatment are being improved by applying novel artificial intelligence methods that can identify non-linear and linear factors that relate to response, providing more accurate predictions. Determination and interpretation of insulin-like growth factor-1 (IGF-1) levels are becoming more standardized and consistent, for evaluation across different patient groups, and computer-learning models indicate that baseline IGF-1 standard deviation score is among the most important indicators of GH therapy response. While physicians involved in child growth and treatment of disorders resulting in growth failure need to be aware of, and keep abreast of, these latest developments, treatment decisions and management should continue to be based on clinical decisions. New digital technologies and advancements in the field should be aimed at improving clinical decisions, making greater standardization of assessment and facilitating patient-centered approaches.

Algorithms to Define Abnormal Growth in Children: External Validation and Head-To-Head Comparison

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 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.