Maternal and Child Nutrition· 2024Q1
Infant growth by INTERGROWTH‐21st and Fenton Growth Charts: Predicting 1‐year anthropometry in South African preterm infants
- 2citations
- Q1SCImago
- 2024year
Short summary
Growth change (ΔWZ) from birth to 50 weeks PMA, calculated using either Fenton (FGC) or INTERGROWTH-21st (IG-PPGS) charts, showed low correlation (r < 0.45) with 1-year anthropometry in 321 South African preterm infants, with FGC slightly outperforming IG-PPGS in predicting underweight and overweight.
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Key points
- Mean weight-for-postmenstrual age z-score at 50 weeks PMA was higher on IG-PPGS (-0.56 ± 1.52) than FGC (-0.90 ± 1.52).
- Change in weight-for-postmenstrual age z-score (ΔWZ) from birth to 50 weeks PMA showed similar trends between FGC (-0.11 ± 1.14) and IG-PPGS (-0.26 ± 1.23).
- Correlation coefficients of ΔWZ with 1-year anthropometric z-scores were low (r < 0.45) for both charts, with FGC showing slightly higher values.
- ΔWZ < -1 on FGC predicted higher percentages of underweight (42% vs. 36%) and wasting (43% vs. 39%) compared to IG-PPGS.
- ΔWZ > +1 on FGC predicted higher percentages of overweight (57% vs. 38%) compared to IG-PPGS.
AI-generated from the title and abstract; the full text is not read.
Abstract
Post-natal growth influences short- and long-term preterm infant outcomes. Different growth charts, such as the Fenton Growth Chart (FGC) and INTERGROWTH-21st Preterm Post-natal Growth Standards (IG-PPGS), describe different growth curves and targets. This study compares FGC- and IG-PPGS-derived weight-for-postmenstrual age z-score (WZ) up to 50 weeks postmenstrual age (PMA50) for predicting 1-year anthropometry in 321 South African preterm infants. The change in WZ from birth to PMA50 (ΔWZ, calculated using FGC and IG-PPGS) was correlated to age-corrected 1-year anthropometric z-scores for weight-for-age (WAZ), length-for-age (LAZ), weight-for-length (WLZ) and BMI-for-age (BMIZ), and categorically compared with rates of underweight (WAZ < -2), stunting (LAZ < -2), wasting (WLZ < -2) and overweight (BMIZ > + 2). Multivariable analyses explored the effects of other early-life exposures on malnutrition risk. At PMA50, mean WZ was significantly higher on IG-PPGS (-0.56 ± 1.52) than FGC (-0.90 ± 1.52; p < 0.001), but ΔWZ was similar (IG-PPGS -0.26 ± 1.23, FGC -0.11 ± 1.14; p = 0.153). Statistically significant ΔWZ differences emerged among small-for-gestational age infants (FGC -0.38 ± 1.22 vs. IG-PPGS -0.01 ± 1.30; p < 0.001) and appropriate-for-gestational age infants (FGC + 0.02 ± 1.08, IG-PPGS -0.39 ± 1.18; p < 0.001). Correlation coefficients of ΔWZ with WAZ, LAZ, WLZ and BMIZ were low (r < 0.45), though higher for FGC than IG-PPGS. Compared with IG-PPGS, ΔWZ < -1 on FGC predicted larger percentages of underweight (42% vs. 36%) and wasting (43% vs. 39%) and equal percentages of stunting (33%), while ΔWZ > + 1 predicted larger percentages overweight (57% vs. 38%). Both charts performed similarly in multivariable analysis. Differences between FGC and IG-PPGS are less apparent when considering ΔWZ, highlighting the importance of assessing growth as change over time, irrespective of growth chart.
The authors' abstract, as published at the source. Maternal and Child Nutrition, 2024 · DOI ↗
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Field: Nutrition and Dietetics
Nutrition and DieteticsNursing