Archives of disease in childhood · 2022
Neurodevelopment assessment of small for gestational age children in a community-based cohort from Pakistan.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Naz Sabahat, Hoodbhoy Zahra, Jaffar Ali, Kaleem Sidra, Hasan Babar S, Chowdhury Devyani, Gladstone Melissa
Аннотация
BACKGROUND: Children born small for gestational age (SGA) may experience more long-term neurodevelopmental issues than those born appropriate for gestational age (AGA). This study aimed to assess differences in the neurodevelopment of children born SGA or AGA within a periurban community in Pakistan.
METHODS: This was a prospective cohort study in which study participants were followed from the pilot Doppler cohort study conducted in 2018. This pilot study aimed to develop a pregnancy risk stratification model using machine learning on fetal Dopplers. This project identified 119 newborns who were born SGA (2.4±0.4 kg) based on International Fetal and Newborn Growth Consortium standards. We assessed 180 children (90 SGA and 90 AGA) between 2 and 4 years of age (76% of follow-up rate) using the Malawi Developmental Assessment Tool (MDAT).
FINDINGS: Multivariable linear regression analysis comparing the absolute scores of MDAT showed significantly lower fine motor scores (β: -0.98; 95% CI -1.90 to -0.06) among SGAs, whereas comparing the z-scores using multivariable logistic regression, SGA children had three times higher odds of overall z-scores ≤-2 (OR: 3.78; 95% CI 1.20 to 11.89) as compared with AGA children.
INTERPRETATION: SGA exposure is associated with poor performance on overall MDAT, mainly due to changes in the fine motor domain in young children. The scores on the other domains (gross motor, language and social) were also lower among SGAs; however, none of these reached statistical significance. There is a need to design follow-up studies to assess the impact of SGA on child's neurodevelopmental trajectory and school performance.
METHODS: This was a prospective cohort study in which study participants were followed from the pilot Doppler cohort study conducted in 2018. This pilot study aimed to develop a pregnancy risk stratification model using machine learning on fetal Dopplers. This project identified 119 newborns who were born SGA (2.4±0.4 kg) based on International Fetal and Newborn Growth Consortium standards. We assessed 180 children (90 SGA and 90 AGA) between 2 and 4 years of age (76% of follow-up rate) using the Malawi Developmental Assessment Tool (MDAT).
FINDINGS: Multivariable linear regression analysis comparing the absolute scores of MDAT showed significantly lower fine motor scores (β: -0.98; 95% CI -1.90 to -0.06) among SGAs, whereas comparing the z-scores using multivariable logistic regression, SGA children had three times higher odds of overall z-scores ≤-2 (OR: 3.78; 95% CI 1.20 to 11.89) as compared with AGA children.
INTERPRETATION: SGA exposure is associated with poor performance on overall MDAT, mainly due to changes in the fine motor domain in young children. The scores on the other domains (gross motor, language and social) were also lower among SGAs; however, none of these reached statistical significance. There is a need to design follow-up studies to assess the impact of SGA on child's neurodevelopmental trajectory and school performance.