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Predicting fetal alcohol spectrum disorders in preschool-aged children from early life factors. | LitMetric

AI Article Synopsis

  • Early life factors such as parental demographics, pregnancy exposure, and infant development are linked to fetal alcohol spectrum disorders (FASD), prompting the study of a classifier model to diagnose FASD in preschoolers based on these characteristics.
  • The researchers analyzed data from a pregnancy cohort in Western Ukraine, utilizing factors like prenatal alcohol use and infant features to predict FASD, and employed various classifier models to assess their accuracy and sensitivity.
  • The random forest model showed the best results with a sensitivity of 54% and an accuracy of 86%, indicating that while some models identified FASD cases effectively, many still struggled with higher sensitivity and specificity.

Article Abstract

Background: Early life factors, including parental sociodemographic characteristics, pregnancy exposures, and physical and neurodevelopmental features measured in infancy are associated with fetal alcohol spectrum disorders (FASD). The objective of this study was to evaluate the performance of a classifier model for diagnosing FASD in preschool-aged children from pregnancy and infancy-related characteristics.

Methods: We analyzed a prospective pregnancy cohort in Western Ukraine enrolled between 2008 and 2014. Maternal and paternal sociodemographic factors, maternal prenatal alcohol use and smoking behaviors, reproductive characteristics, birth outcomes, infant alcohol-related dysmorphic and physical features, and infant neurodevelopmental outcomes were used to predict FASD. Data were split into separate training (80%: n = 245) and test (20%: n = 58; 11 FASD, 47 no FASD) datasets. Training data were balanced using data augmentation through a synthetic minority oversampling technique. Four classifier models (random forest, extreme gradient boosting [XGBoost], logistic regression [full model] and backward stepwise logistic regression) were evaluated for accuracy, sensitivity, and specificity in the hold-out sample.

Results: Of 306 children evaluated for FASD, 61 had a diagnosis. Random forest models had the highest sensitivity (0.54), with accuracy of 0.86 (95% CI: 0.74, 0.94) in hold-out data. Boosted gradient models performed similarly, however, sensitivity was less than 50%. The full logistic regression model performed poorly (sensitivity = 0.18 and accuracy = 0.65), while stepwise logistic regression performed similarly to the boosted gradient model but with lower specificity. In a hold-out sample, the best performing algorithm correctly classified six of 11 children with FASD, and 44 of 47 children without FASD.

Conclusions: As early identification and treatment optimize outcomes of children with FASD, classifier models from early life characteristics show promise in predicting FASD. Models may be improved through the inclusion of physiologic markers of prenatal alcohol exposure and should be tested in different samples.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10786333PMC
http://dx.doi.org/10.1111/acer.15233DOI Listing

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