Dataset Information

Association of First-Week Nutrient Intake and Extrauterine Growth Restriction in Moderately Preterm Infants: A Regional Population-Based Study.

ABSTRACT: The purpose of this study was to determine the influence of first-week nutrition intake on neonatal growth in moderate preterm (MP) infants. Data on neonatal morbidity and nutrition intake on day of life 7 (DoL7) were prospectively collected from 735 MP infants (32^0/7-34^6/7 weeks gestational age (GA)). Multivariable regression was used to assess the factors associated with extrauterine growth restriction (EUGR) defined as a decrease of more than 1 standard deviation (SD) in the weight z-score during hospitalization. Mean (SD) gestational age and birth weight were 33.2 (0.8) weeks and 2005 (369) g. The mean change in the weight z-score during hospitalization was -0.64 SD. A total of 138 infants (18.8%) had EUGR. Compared to adequate growth infants, EUGR infants received 15% and 35% lower total energy and protein intake respectively (p < 0.001) at DoL7. At DoL7, each increase of 10 kcal/kg/d and 1 g/kg/d of protein was associated with reduced odds of EUGR with an odds ratio of 0.73 (95% CI, 0.66-0.82; p < 0.001) and 0.54 (0.44-0.67; p < 0.001), respectively. Insufficient energy and protein intakes on DoL7 negatively affected neonatal growth of MP infants. Nutritional support should be optimized from birth onwards to improve neonatal weight growth.

SUBMITTER: Baillat M

PROVIDER: S-EPMC7830065 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Association of First-Week Nutrient Intake and Extrauterine Growth Restriction in Moderately Preterm Infants: A Regional Population-Based Study.

Baillat Marine M Pauly Vanessa V Dagau Gina G Berbis Julie J Boubred Farid F Fayol Laurence L

Nutrients 20210114 1

The purpose of this study was to determine the influence of first-week nutrition intake on neonatal growth in moderate preterm (MP) infants. Data on neonatal morbidity and nutrition intake on day of life 7 (DoL7) were prospectively collected from 735 MP infants (320/7-346/7 weeks gestational age (GA)). Multivariable regression was used to assess the factors associated with extrauterine growth restriction (EUGR) defined as a decrease of more than 1 standard deviation (SD< ...[more]

PMID: 33466801

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Project description:IntroductionPreterm birth is a leading cause of infant mortality and morbidity. Despite the improvement in the overall mortality in premature infants, the intact survival of these infants remains a significant challenge. Screening the physical growth of infants is fundamental to potentially reducing the escalation of this disorder. Recently, machine learning models have been used to predict the growth restrictions of infants; however, they frequently rely on conventional risk factors and cross-sectional data and do not leverage the longitudinal database associated with medical data from laboratory tests.MethodsThis study aimed to present an automated interpretable ML-based approach for the prediction and classification of short-term growth outcomes in preterm infants. We prepared four datasets based on weight and length including weight baseline, length baseline, weight follow-up, and length follow-up. The CHA Bundang Medical Center Neonatal Intensive Care Unit dataset was classified using two well-known supervised machine learning algorithms, namely support vector machine (SVM) and logistic regression (LR). A five-fold cross-validation, and several performance measures, including accuracy, precision, recall and F1-score were used to compare classifier performances. We further illustrated the models' trustworthiness using calibration and cumulative curves. The visualized global interpretations using Shapley additive explanation (SHAP) is provided for analyzing variables' contribution to final prediction.ResultsBased on the experimental results with area under the curve, the discrimination ability of the SVM algorithm was found to better than that of the LR model on three of the four datasets with 81%, 76% and 72% in weight follow-up, length baseline and length follow-up dataset respectively. The LR classifier achieved a better ROC score only on the weight baseline dataset with 83%. The global interpretability results revealed that pregnancy-induced hypertension, gestational age, twin birth, birth weight, antenatal corticosteroid use, premature rupture of membranes, sex, and birth length were consistently ranked as important variables in both the baseline and follow-up datasets.DiscussionThe application of machine learning models to the early detection and automated classification of short-term growth outcomes in preterm infants achieved high accuracy and may provide an efficient framework for clinical decision systems enabling more effective monitoring and facilitating timely intervention.

Project description:BackgroundNutritional support of preterm infants remains a field of debate in the literature and clinical practice varies significantly. Adequate nutrition should promote growth and aim for optimal later neurodevelopment. However, it is often impaired by prematurity-associated morbidity and the physiologic immaturity of preterm infants. This study assessed the impact of energy and macronutrient provision on growth velocity and outcome and explored differences attributed to the heterogeneity of the preterm population.MethodsWe retrospectively collected clinical and nutritional data from neonates hospitalized in two separate Neonatal Intensive Care Units (NICUs). Estimated energy and protein balance were calculated based on the ESPGHAN guidelines and their association with the growth outcome was explored. Growth assessment was based on somatometry Delta (Δ) z-scores at discharge.ResultsIn total, 174 neonates were included in the study. By day 14, most preterm infants were exclusively enterally fed, whereas there were infants in the <28 and 28-31+6 subgroups fed exclusively parenterally. Energy balance was positive for all gestational age (GA) subgroups except for those born <28 weeks. Protein balance was consistently positive for extremely premature but negative for late preterms. Cumulative substrates provisions were strong predictors of a positive energy or protein balance in the <34 weeks GA preterms on days 14 (ROC analyses, p < 0.001) and 7 (p < 0.05). A higher GA (p = 0.013) and enteral nutrition (p = 0.005) were additional predictors of a positive energy balance. All GA subgroups had a negative Δ z-score of weight at discharge. In the <34 GA subcohorts, a positive protein balance on day 14 (p = 0.009) and a short time to regain birth weight (exp(B) 3.1 (p = 0.004)) were independently associated with a positive Δ z-score of weight at discharge.ConclusionsEarly achievement of a positive energy and protein balance, based on the ESPGHAN guidelines, is crucial to ensure optimal postnatal growth and prevent extrauterine growth restriction, a relatively common occurrence in preterm infants.

Dataset Information

Association of First-Week Nutrient Intake and Extrauterine Growth Restriction in Moderately Preterm Infants: A Regional Population-Based Study.

Publications

Association of First-Week Nutrient Intake and Extrauterine Growth Restriction in Moderately Preterm Infants: A Regional Population-Based Study.

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