Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models

利用Fenton 2013和INTERGROWTH-21st生长曲线结合逻辑回归和机器学习模型评估早产儿出生后生长发育迟缓的患病率

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Abstract

Background/Objectives: Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term health complications. Methods: This retrospective study at a level III NICU of a tertiary hospital analyzed 650 preterm neonates born before 33 weeks. Postnatal growth was evaluated using the Fenton2013 and INTERGROWTH-21st growth charts, with changes in weight z-scores from birth to discharge classified as normal growth (ΔZ ≥ -1), non-severe PGF (-2 ≤ ΔZ < -1), and severe PGF (ΔZ < -2). Results: Mean gestational and postmenstrual age at discharge was 30 weeks (SD 1.9) and 37.1 weeks (SD 2.7), respectively. Fenton2013 growth curves revealed a higher prevalence of non-severe and severe PGF (43% and 14.6%) compared to INTERGROWTH-21st (24.5% and 10.3%). A more rapid establishment of full enteral feeds was strongly associated with reduced severe PGF prevalence in both growth charts (p < 0.001), as was shorter hospitalization. Late-onset sepsis was associated with an increased risk of severe PGF, while being small for gestational age (SGA) was protective against severe PGF across both growth charts (p < 0.001). A trend of decreasing PGF prevalence was noted over the study years, most probably attributed to the implementation of updated nutritional guidelines. Interestingly, when machine learning classification models were evaluated in our Greek cohort, a notable decline in predictive accuracy depending on the growth standard applied was observed. Conclusions: Our study highlights the need for standardizing PGF definition in an attempt to enhance nutritional management and further investigate the long-term impacts of nutritional interventions on growth, neurodevelopment, and overall health outcomes.

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