Predictors of Human Milk Feeding and Direct Breastfeeding for Infants with Single Ventricle Congenital Heart Disease: Machine Learning Analysis of the National Pediatric Cardiology Quality Improvement Collaborative Registry

Elgersma, Kristin M.; Wolfson, Julian; Fulkerson, Jayne A.; Georgieff, Michael; Looman, Wendy S.; Spatz, Diane L.; Shah, Kavisha; Uzark, Karen; McKechnie, Anne Chevalier

doi:10.1016/j.jpeds.2023.113562

Cited by 3 publications

(3 citation statements)

References 66 publications

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“…This study included only data available through electronic health record chart review; therefore, nutrition data post-discharge were not collected. Covariates for model adjustment were based on previous literature, [14][15][16] and included prematurity, race, major genetic diagnosis, cardiac diagnosis, number of surgeries, and hospital length of stay. Additional covariate details can be found in Supplementary Table S1.…”

Section: Definitionsmentioning

confidence: 99%

“…For example, a previous national registry analysis used machine learning techniques to identify the strongest predictors of human milk and breastfeeding for infants with single ventricle CHD. 14 While clinical stability played a role, demographic/social variables and clinical site practices were more important in determining whether an infant would breastfeed. While samples across sites may vary, in the current study cohort breastfeeding rates at 1-year follow-up were often higher for infants with potential indicators of clinical complexity (e.g., prematurity, major genetic diagnosis, higher-risk cardiac diagnosis; see Supplementary Table S2), and previous national registry analysis demonstrates wide variation in direct breastfeeding prevalence, even among sites with large volumes of high-complexity single ventricle patients.…”

Section: Nutrition and Neurodevelopmentmentioning

confidence: 99%

“…While lactating parents of infants with critical CHD often initiate milk expression via pumping due to parent/infant separation after birth or infant physiologic instability, qualitative reports have underscored the burden of extended pumping. 30 Direct breastfeeding is associated with increased duration of human milk feeding for infants with critical CHD, 14,21 but support for direct breastfeeding in this population is inconsistent both between and within institutions, 30 and interventions to facilitate breastfeeding have not been tested in the context of critical CHD. Given that increased duration of human milk feeding has been associated with neurodevelopmental benefits throughout childhood for preterm infants, improved support for direct breastfeeding, as clinically indicated, could be an important intervention to support neurodevelopment.…”

Section: Nutrition Patternsmentioning

confidence: 99%

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Human milk, breastfeeding, and early neurodevelopmental outcomes for infants with critical CHD

Elgersma,

Engel,

Ramel

et al. 2024

Cardiol Young

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Human milk improves neurodevelopment for preterm infants, but relationships between human milk and neurodevelopment for infants with critical CHD are unknown. We aimed to (1) explore associations between human milk/direct breastfeeding and neurodevelopment at 1-year and 2-year follow-up and (2) describe patterns of human milk (maternal, donor) and commercial formula during hospitalisation in the first year of life. This retrospective cohort study included infants who underwent surgery for CHD < 6 months old. The primary outcome was neurodevelopment via Bayley Scales of Infant Development-IV. Analysis included adjusted linear regression for associations between exclusive human milk while inpatient during the first 6 months or any direct breastfeeding while inpatient during the first year of life and 1-year Bayley-IV scores. Models were adjusted for race, insurance type, genetic diagnosis, and length of stay. Of 98 eligible infants, 40% followed up at 1 year; 27% at 2 years. There were differences in follow-up related to demographics (race, ethnicity) and social determinants of health (insurance type, distance from clinic). In adjusted models, infants who directly breastfed had 13.18 points higher cognition (95% CI: 0.84–25.53, p = 0.037); 14.04 points higher language (2.55–25.53, p = 0.018); and 15.80 points higher motor scores (3.27–28.34, p = 0.015) at 1-year follow-up. Infants fed exclusive human milk had 12.64 points higher cognition scores (−0.53–25.82, p = 0.059). Future investigation into nutrition and neurodevelopment in the context of critical CHD is warranted. As neurodevelopmental follow-up becomes standard of care in this population, efforts are needed to mitigate disparities in access to this care.

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Section: Definitionsmentioning

confidence: 99%

Section: Nutrition and Neurodevelopmentmentioning

confidence: 99%

Section: Nutrition Patternsmentioning

confidence: 99%

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Human milk, breastfeeding, and early neurodevelopmental outcomes for infants with critical CHD

Elgersma,

Engel,

Ramel

et al. 2024

Cardiol Young

Self Cite

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Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling

Olapojoye,

Singh,

Nishi

et al. 2024

Journal of Engineering and Science in Medical Diagnostics and Therapy

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Breastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management.

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Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review

Agudelo-Pérez,

Botero-Rosas,

Rodríguez-Alvarado

et al. 2024

Int Breastfeed J

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Predictors of Human Milk Feeding and Direct Breastfeeding for Infants with Single Ventricle Congenital Heart Disease: Machine Learning Analysis of the National Pediatric Cardiology Quality Improvement Collaborative Registry

Cited by 3 publications

References 66 publications

Human milk, breastfeeding, and early neurodevelopmental outcomes for infants with critical CHD

Human milk, breastfeeding, and early neurodevelopmental outcomes for infants with critical CHD

Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling

Artificial intelligence applied to the study of human milk and breastfeeding: a scoping review

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