ArticleJournal of the American Medical Informatics Association : JAMIA2017
Automated identification of implausible values in growth data from pediatric electronic health records.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 82 papers, 1 of them a synthesis that pooled it.
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Who cites it
82 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Loss of Access to Universal Free School Meals and Body Mass Index in Students.JAMA network open · 2026Article
- Capturing infant and child growth dynamics with P-splines mixed effects models.International journal of obesity (2005) · 2026Article
- An EHR-based framework for modeling growth curves and constructing growth centile charts for genetic disorders.NPJ genomic medicine · 2026Article
- Birth Weight Percentiles and Infant and Child Growth Dynamics.JAMA network open · 2026Article
- Adverse Childhood Experiences and Growth Outcomes in Childhood: A Longitudinal EHR-Based Study.medRxiv : the preprint server for health sciences · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
- Disease-specific growth charts capture characteristic growth patterns in children with PMM2 - CDG.Molecular genetics and metabolism · 2026Article
- Preconception metabolic-bariatric surgery and child health outcomes: Identification and cohort profile of the POSIT study protocol.medRxiv : the preprint server for health sciences · 2026Article
- The Importance of Disease-specific Growth Charts for Children with Congenital Adrenal Hyperplasia.The Journal of clinical endocrinology and metabolism · 2026Article
- Emulation of a Target Trial of Antihypertensive Medications on Weight Change.Journal of general internal medicine · 2026Observational
- Distinct genetic profiles influence body mass index between infancy and adolescence.Nature communications · 2026Article
- Examining Biomarkers for Dyslipidemia, Diabetes, and NAFLD by CDC's 2022 Extended BMI Percentiles in US Youth Aged 6-17 Years.Obesity (Silver Spring, Md.) · 2026Article
- Neuroimaging markers of cognition in late childhood associated with moderate to late preterm birth.Pediatric research · 2026Article
- Modeling dynamic velocity-based gestational weight gain trajectories in advanced maternal age: association with perinatal outcomes.Frontiers in medicine · 2026Article
- Role of Sleep Duration and Timing on Paediatric BMI Across Childhood and Adolescence: Do Both Matter?Pediatric obesity · 2026Article
- The Contribution of BMI to a Young Child's Risk of Islet Autoimmunity Is Dependent on HLA-DR4-DQ8 Without HLA-DR3-DQ2.Diabetes care · 2025Article
- Regional Implications of the COVID-19 Pandemic on Childhood Obesity Prevalence, Denver, Colorado, 2019-2022.Childhood obesity (Print) · 2025Article
- Article
- Prenatal and postnatal environmental influences on child growth trajectories across a rural-urban gradient: an analysis of the ECoMiD longitudinal birth cohort.medRxiv : the preprint server for health sciences · 2025Article
22 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveLarge electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data. MATERIALS AND
methodsUsing deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors.
resultsOf 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors. DISCUSSION AND
conclusionThis automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.
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