Related Experiment Video
Updated: Jan 31, 2026

Standard Operating Procedure for Lyssavirus Surveillance of the Bat Population in Taiwan
Published on: August 27, 2019
From population reference to national standard: new and improved birthweight charts
Liset Hoftiezer1, Michel H P Hof2, Joyce Dijs-Elsinga3
1Department of Neonatology, Princess Amalia Department of Pediatrics, Isala, Zwolle, The Netherlands; Department of Neonatology, Amalia Children's Hospital, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Insights
New prescriptive birthweight charts, excluding infants with risk factors for abnormal fetal growth, better identify small-for-gestational-age infants. This approach improves the discrimination between normal and abnormal birthweight, aiding clinical practice.
Area of Science:
- Obstetrics and Gynecology
- Neonatology
- Perinatology
Background:
- Accurate detection of intrauterine growth restriction (IUGR) before birth is a significant clinical challenge.
- Postnatal diagnosis of IUGR relies on birthweight charts, but chart selection impacts infant classification.
- Existing charts face controversy regarding the exclusion of infants with pathological risk factors.
Purpose of the Study:
- To identify and quantify pathological risk factors affecting fetal growth.
- To develop prescriptive birthweight charts for the Dutch population based on these findings.
- To enhance the accurate classification of infants' growth status.
Main Methods:
- Retrospective cross-sectional study of 2,712,301 infants born in the Netherlands (2000-2014).
- Risk factors for abnormal fetal growth were identified and categorized.
- Prescriptive charts derived from a low-risk population using Box-Cox-t distribution for sex-specific percentiles.
Main Results:
- Over 37% of infants were excluded due to identified risk factors for abnormal fetal growth.
- Significant differences in mean birthweights were observed between infants with and without risk factors.
- New charts' 10th percentiles approximated fetal-weight charts, exceeding existing birthweight charts.
Conclusions:
- Excluding infants with risk factors yields prescriptive charts similar to fetal-weight charts.
- This method improves the discrimination between normal and abnormal birthweight.
- The developed approach serves as a proof of concept applicable to other populations.
Background:
Antenatal detection of intrauterine growth restriction remains a major obstetrical challenge, with the majority of cases not detected before birth. In these infants with undetected intrauterine growth restriction, the diagnosis must be made after birth. Clinicians use birthweight charts to identify infants as small-for-gestational-age if their birthweights are below a predefined threshold for gestational age. The choice of birthweight chart strongly affects the classification of small-for-gestational-age infants and has an impact on both research findings and clinical practice. Despite extensive literature on pathological risk factors associated with small-for-gestational-age, controversy exists regarding the exclusion of affected infants from a reference population.
Objective:
This study aims to identify pathological risk factors for abnormal fetal growth, to quantify their effects, and to use these findings to calculate prescriptive birthweight charts for the Dutch population.
Materials And Methods:
We performed a retrospective cross-sectional study, using routinely collected data of 2,712,301 infants born in The Netherlands between 2000 and 2014. Risk factors for abnormal fetal growth were identified and categorized in 7 groups: multiple gestation, hypertensive disorders, diabetes, other pre-existing maternal medical conditions, maternal substance (ab)use, medical conditions related to the pregnancy, and congenital malformations. The effects of these risk factors on mean birthweight were assessed using linear regression. Prescriptive birthweight charts were derived from live-born singleton infants, born to ostensibly healthy mothers after uncomplicated pregnancies and spontaneous onset of labor. The Box-Cox-t distribution was used to model birthweight and to calculate sex-specific percentiles. The new charts were compared to various existing birthweight and fetal-weight charts.
Results:
We excluded 111,621 infants because of missing data on birthweight, gestational age or sex, stillbirth, or a gestational age not between 23 and 42 weeks. Of the 2,599,640 potentially eligible infants, 969,552 (37.3%) had 1 or more risk factors for abnormal fetal growth and were subsequently excluded. Large absolute differences were observed between the mean birthweights of infants with and without these risk factors, with different patterns for term and preterm infants. The final low-risk population consisted of 1,629,776 live-born singleton infants (50.9% male), from which sex-specific percentiles were calculated. Median and 10th percentiles closely approximated fetal-weight charts but consistently exceeded existing birthweight charts.
Conclusion:
Excluding risk factors that cause lower birthweights results in prescriptive birthweight charts that are more akin to fetal-weight charts, enabling proper discrimination between normal and abnormal birthweight. This proof of concept can be applied to other populations.
More Related Videos
10:16Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines
Published on: October 8, 2015
06:55Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Related Concept Videos
Estimating Population Standard Deviation
Estimating Population Mean with Known Standard Deviation
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
Estimating Population Mean with Unknown Standard Deviation
William S. Gosset (1876–1937) of the...
Run Charts
The R Chart
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
Pareto Chart
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...