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An Exploratory Bayesian Network Analysis of Weight and Feeding Outcomes in 6-Month-Old Infants With Complex
Amy L Delaney1, Mary Taylor1, Julie Lavoie2
1Neurodevelopmental Feeding and Swallowing Lab, Department of Speech Pathology and Audiology, Marquette University, Milwaukee, WI.
Insights
Bayesian network analysis reveals oral-motor skills are key to feeding and growth in infants with complex congenital heart disease (CCHD). Clinical stability may hide feeding issues, highlighting the need for targeted interventions and closer monitoring.
Area of Science:
- Pediatric Cardiology
- Infant Nutrition
- Developmental Pediatrics
Background:
- Infants with complex congenital heart disease (CCHD) often face feeding challenges.
- Understanding the interplay of factors influencing feeding outcomes is crucial for clinical management.
Purpose of the Study:
- To explore associations between feeding-related factors in infants with CCHD using Bayesian network analysis.
- To identify key predictors influencing feeding and growth outcomes in this population.
- To generate hypotheses for future research on optimizing feeding interventions.
Main Methods:
- Descriptive study of 19 infants with CCHD, collecting data on illness severity, oral-motor (OM) and swallowing skills, feeding patterns, and growth (weight-for-age z-score at 2 and 6 months).
- Bayesian network analysis to model conditional probabilities and predict feeding outcomes (volume consumed) and 6-month weight-for-age z-score.
- Simulation of clinical scenarios by manipulating predictor variable probabilities to assess network effects on outcomes.
Main Results:
- Feeding and growth patterns align with existing literature.
- Bayesian network modeling identified three primary themes: feeding profiles for risk stratification, OM skill development as a foundational predictor, and clinical stability potentially masking feeding vulnerabilities.
- Oral-motor skill development significantly impacts feeding and growth outcomes.
Conclusions:
- Bayesian network analysis offers valuable insights into conditional relationships, supporting clinical decision-making for infants with CCHD.
- Further research with larger, diverse samples is warranted to confirm findings and explore the benefits of closer monitoring for infants with less severe CCHD.
Purpose:
We aimed to explore with a Bayesian network how clinically important factors related to feeding infants with complex congenital heart disease (CCHD) are associated with each other and influence feeding outcomes. Our goal was to raise questions for further study.
Method:
This descriptive study included data from 19 infants on severity of neonatal illness, early oral-motor (OM) and swallowing skills, feeding patterns, liquid and solid intake, and weight-for-age at 2 and 6 months. Bayesian network analysis was used to estimate the conditional probabilities of these variables in relation to each other and predict the 6-month weight-for-age z score and feeding outcomes (volume of liquid and solid food consumed) in the context of the other variables in the model. In clinically oriented scenarios, we manipulated the probability of specific predictor variables to 100% to examine the network effect on outcome variables.
Results:
Descriptive analyses revealed feeding and growth patterns consistent with prior literature. Bayesian network modeling identified three key themes: (a) feeding profiles may support risk stratification and guide targeted intervention, (b) OM skill development emerged as a foundational predictor of feeding and growth outcomes, and (c) clinical stability may obscure underlying feeding vulnerabilities.
Conclusions:
Bayesian network analysis provided insights into the conditional relationships among multiple factors, showing a method that could support clinical decision making. Further study with a larger, more diverse sample is needed to explore whether closer monitoring of intake and growth would promote better feeding outcomes, particularly for infants with less severe CCHD.
Supplemental Material:
https://doi.org/10.23641/asha.31856314.
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