Vander Elst Zoë, Laenen Annouschka, Deberdt Jana, Delemarre Lotte, Vermeersch Pieter, Frans Glynis, Naulaers Gunnar, Gijsen Matthias, Dreesen Erwin, Spriet Isabel, Allegaert Karel, Smits Anne
Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
Neonatal Intensive Care Unit, University Hospitals Leuven, Leuven, Belgium.
Pediatr Res. 2024 Oct 11. doi: 10.1038/s41390-024-03634-1.
Human serum albumin (HSA) concentrations may alter HSA-bound drug distribution. This study aims to describe longitudinal real-world HSA trends, and to develop a prediction model for HSA concentrations using a large neonatal cohort.
Patients admitted to the neonatal intensive care unit of the University Hospitals Leuven (postnatal age (PNA) ≤28days) were retrospectively included. Using linear mixed models, covariate effects on HSA were explored. A multivariable prediction model was developed (backward model selection procedure, 1% significance level).
In total, 848 neonates were included [median(interquartile range) gestational age (GA) 35(32-38)weeks, birth weight (BW) 2400(1640-3130)grams]. Median HSA concentration was 32.3(28.7-35.6)g/L. Longitudinal analyses demonstrated increasing HSA concentrations with PNA and GA for most GA groups. Univariable analyses revealed significant associations of HSA with PNA, GA, BW, current weight, total and direct bilirubin, total plasma proteins, respiratory support, mechanical ventilation, sepsis, ibuprofen use, and C-reactive protein (p-values < 0.05). A high-performance (R = 76.3%) multivariable HSA prediction model was developed, and PNA- and GA-dependent HSA centiles were provided.
Population-specific HSA centiles and an accurate neonatal HSA prediction model were developed, incorporating both maturational and non-maturational covariates. These results can enhance future clinical care and pharmacokinetic analyses to improve pharmacotherapy of HSA-bound drugs in neonates, respectively.
To improve future pharmacokinetic modeling initiatives, a high-performance human serum albumin (HSA) prediction model was developed for (pre)term neonates, using a large, single-center cohort of real-world data. This prediction model integrates both maturational and non-maturational covariates, resulting in accurate HSA predictions in neonates. Additionally, HSA centiles based on postnatal and gestational age were developed, which can be easily applied in clinical practice when interpreting HSA concentrations of neonates. In general, unbound drug fractions are higher in neonates compared to older populations. To improve pharmacotherapy of HSA-bound drugs in neonates, the obtained results can be integrated in future pharmacokinetic-pharmacodynamic analyses.
人血清白蛋白（HSA）浓度可能会改变与HSA结合的药物分布。本研究旨在描述HSA在现实世界中的纵向变化趋势，并利用一个大型新生儿队列建立HSA浓度的预测模型。
回顾性纳入鲁汶大学医院新生儿重症监护病房收治的患者（出生后年龄（PNA）≤28天）。使用线性混合模型探讨协变量对HSA的影响。建立了一个多变量预测模型（向后模型选择程序，显著性水平为1%）。
共纳入848例新生儿[中位（四分位间距）胎龄（GA）35（32 - 38）周，出生体重（BW）2400（1640 - 3130）克]。HSA浓度中位数为32.3（28.7 - 35.6）g/L。纵向分析表明，大多数GA组中，HSA浓度随PNA和GA增加。单变量分析显示，HSA与PNA、GA、BW、当前体重、总胆红素和直接胆红素、总血浆蛋白、呼吸支持、机械通气、败血症、布洛芬使用以及C反应蛋白存在显著关联（p值<0.05）。建立了一个高性能（R = 76.3%）的多变量HSA预测模型，并提供了依赖于PNA和GA的HSA百分位数。
建立了针对特定人群的HSA百分位数和准确的新生儿HSA预测模型，纳入了成熟和非成熟协变量。这些结果可分别加强未来的临床护理和药代动力学分析，以改善新生儿中与HSA结合药物的药物治疗。
为改进未来的药代动力学建模工作，利用一个大型单中心真实世界数据队列，为（早产）足月儿建立了一个高性能的人血清白蛋白（HSA）预测模型。该预测模型整合了成熟和非成熟协变量，能够准确预测新生儿的HSA水平。此外，还建立了基于出生后年龄和胎龄的HSA百分位数，在解释新生儿HSA浓度时可轻松应用于临床实践。一般来说，与年长人群相比，新生儿中游离药物分数更高。为改善新生儿中与HSA结合药物的药物治疗，可将所得结果纳入未来的药代动力学 - 药效学分析中。
