Koch Gilbert, Wilbaux Melanie, Kasser Severin, Schumacher Kai, Steffens Britta, Wellmann Sven, Pfister Marc
Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel (UKBB), University of Basel, Basel, Switzerland.
NeoPrediX AG, Basel, Switzerland.
Front Pharmacol. 2022 Aug 11;13:842548. doi: 10.3389/fphar.2022.842548. eCollection 2022.
The field of medicine is undergoing a fundamental change, transforming towards a modern data-driven patient-oriented approach. This paradigm shift also affects perinatal medicine as predictive algorithms and artificial intelligence are applied to enhance and individualize maternal, neonatal and perinatal care. Here, we introduce a pharmacometrics-based mathematical-statistical computer program (PMX-based algorithm) focusing on hyperbilirubinemia, a medical condition affecting half of all newborns. Independent datasets from two different centers consisting of total serum bilirubin measurements were utilized for model development (342 neonates, 1,478 bilirubin measurements) and validation (1,101 neonates, 3,081 bilirubin measurements), respectively. The mathematical-statistical structure of the PMX-based algorithm is a differential equation in the context of non-linear mixed effects modeling, together with Empirical Bayesian Estimation to predict bilirubin kinetics for a new patient. Several clinically relevant prediction scenarios were validated, i.e., prediction up to 24 h based on one bilirubin measurement, and prediction up to 48 h based on two bilirubin measurements. The PMX-based algorithm can be applied in two different clinical scenarios. First, bilirubin kinetics can be predicted up to 24 h based on one single bilirubin measurement with a median relative (absolute) prediction difference of 8.5% (median absolute prediction difference 17.4 μmol/l), and sensitivity and specificity of 95.7 and 96.3%, respectively. Second, bilirubin kinetics can be predicted up to 48 h based on two bilirubin measurements with a median relative (absolute) prediction difference of 9.2% (median absolute prediction difference 21.5 μmol/l), and sensitivity and specificity of 93.0 and 92.1%, respectively. In contrast to currently available nomogram-based static bilirubin stratification, the PMX-based algorithm presented here is a dynamic approach predicting individual bilirubin kinetics up to 48 h, an intelligent, predictive algorithm that can be incorporated in a clinical decision support tool. Such clinical decision support tools have the potential to benefit perinatal medicine facilitating personalized care of mothers and their born and unborn infants.
医学领域正在经历一场根本性变革，正朝着以现代数据驱动的、以患者为导向的方法转变。这种范式转变也影响着围产期医学，因为预测算法和人工智能被应用于加强和个性化孕产妇、新生儿及围产期护理。在此，我们介绍一种基于药代动力学的数理统计计算机程序（基于PMX的算法），其聚焦于高胆红素血症，这是一种影响半数新生儿的病症。来自两个不同中心的独立数据集，分别包含总血清胆红素测量值，被用于模型开发（342例新生儿，1478次胆红素测量）和验证（1101例新生儿，3081次胆红素测量）。基于PMX的算法的数理统计结构是在非线性混合效应建模背景下的一个微分方程，连同经验贝叶斯估计来预测新患者的胆红素动力学。几种临床相关的预测方案得到了验证，即基于一次胆红素测量预测长达24小时的情况，以及基于两次胆红素测量预测长达48小时的情况。基于PMX的算法可应用于两种不同的临床场景。首先，基于单次胆红素测量可预测长达24小时的胆红素动力学，中位相对（绝对）预测差异为8.5%（中位绝对预测差异17.4μmol/l），敏感性和特异性分别为95.7%和96.3%。其次，基于两次胆红素测量可预测长达48小时的胆红素动力学，中位相对（绝对）预测差异为9.2%（中位绝对预测差异21.5μmol/l），敏感性和特异性分别为93.0%和92.1%。与目前可用的基于列线图的静态胆红素分层不同，此处呈现的基于PMX的算法是一种动态方法，可预测长达48小时的个体胆红素动力学，是一种可纳入临床决策支持工具的智能预测算法。此类临床决策支持工具有可能使围产期医学受益，促进对母亲及其已出生和未出生婴儿的个性化护理。
