Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature
| Title: | Important Tools for Use by Pediatric Endocrinologists in the Assessment of Short Stature |
|---|---|
| Authors: | Labarta JI; University of Zaragoza, Children’s Hospital Miguel Servet, Instituto de Investigación Sanitaria de Aragón, Unit of Endocrinology, Zaragoza, Spain; Ranke MB; University of Tübingen, Children’s Hospital, Clinic of Pediatric Endocrinology, Tübingen, Germany; Maghnie M; University of Genova, Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Genova, Italy; IRCCS Instituto Giannina Gaslini, Department of Pediatrics, Genova, Italy; Martin D; University of Witten/Herdecke and Tübingen University, Tübingen, Germany; Guazzarotti L; University of Milan, Luigi Sacco Hospital, Clinic of Pediatric, Milan, Italy; Pfäffle R; University of Leipzig, Department of Pediatrics, Leipzig, Germany; Koledova E; Global Medical Affairs, Merck KGaA, Darmstadt, Germany; Wit JM; Leiden University Medical Centre, Department of Paediatrics, Leiden, Netherlands |
| Source: | Journal of clinical research in pediatric endocrinology [J Clin Res Pediatr Endocrinol] 2021 Jun 02; Vol. 13 (2), pp. 124-135. Date of Electronic Publication: 2020 Oct 02. |
| Publication Type: | Journal Article; Review |
| Language: | English |
| Journal Info: | Publisher: Galenos Yayınevi Country of Publication: Turkey NLM ID: 101519456 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1308-5735 (Electronic) NLM ISO Abbreviation: J Clin Res Pediatr Endocrinol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Istanbul : Galenos Yayınevi; Original Publication: Istanbul : Turkish Pediatric Endocrinology and Diabetes Society |
| MeSH Terms: | Human Growth Hormone*/analysis ; Human Growth Hormone*/therapeutic use ; Artificial Intelligence*; Dwarfism/*diagnosis ; Endocrinology/*methods ; Pediatrics/*methods; Child ; Humans |
| Abstract: | 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. |
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| Contributed Indexing: | Keywords: Short stature; height monitoring; bone age; cranial imaging; growth hormone treatment; prediction models |
| Substance Nomenclature: | 12629-01-5 (Human Growth Hormone) |
| Entry Date(s): | Date Created: 20201002 Date Completed: 20220111 Latest Revision: 20220111 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC8186334 |
| DOI: | 10.4274/jcrpe.galenos.2020.2020.0206 |
| PMID: | 33006554 |
| Database: | MEDLINE |
Journal Article; Review