Automated Monitoring Spots Growth Disorders in Children

Children's growth disorders can be detected earlier and more efficiently with the help of new electronic health record (EHR) monitoring tools, according to a new study.

Developed by Antti Saari, MD, of the University of Eastern Finland (Joensuu, Finland), the tools include up-to-date growth reference curves, evidence-based screening cut-off values for abnormal growth, and automated growth monitoring, all based on EHRs. The study defined new growth, height, and BMI reference curves for Finnish children by making use of auxological [auxanological] data from approximately 72,000 children over a period of 60 years. The revised growth reference curves helped enhance the detection of growth disorders causing growth failure.

The study was used to determine evidence-based cut-off limits for attained height, weight, and growth rate, and validated these against two target conditions: Turner syndrome and Celiac disease. The findings showed that the screening precision was excellent for Turner syndrome, and good for Celiac disease. The new monitoring methods could also help in the early detection of growth disorders by automating growth monitoring methods developed in the study, using EHRs and growth monitoring software.

Among the findings was that healthy children born between 1983 and 2008 were growing taller than children in the former Finnish growth reference, which consisted of charts for children born between 1956 and 1973. The mean adult height of Finnish boys has increased from 178.9 cm to 180.7 cm (+1.8 cm), and the mean adult height of Finnish girls from 165.6 cm to 167.5 cm (+1.9 cm). The study also suggested that if Finland was to use the multiethnic World Health Organization (WHO; Geneva, Switzerland) growth charts instead of the updated national ones, many disorders affecting growth could go undetected.

“The study showed that computer-assisted growth monitoring clearly enhanced monitoring precision in primary health care when combined with automated growth consultation services used in special health care,” concluded Dr. Saari, who presented the study as his doctoral thesis. “The automated strategy improved the detection precision by approximately six-fold and often also allowed for a considerably earlier detection of disorders affecting growth than the traditional manual method.”

Related Links:
University of Eastern Finland
World Health Organization