Daunhawer Imant, Schumacher Kai, Badura Anna, Vogt Julia E, Michel Holger, Wellmann Sven
Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Department of Neonatology, Hospital St. Hedwig of the Order of St. John, University Children's Hospital Regensburg (KUNO), Regensburg, Germany.
Front Pediatr. 2023 Oct 9;11:1229462. doi: 10.3389/fped.2023.1229462. eCollection 2023.
Hyperbilirubinemia of the newborn infant is a common disease worldwide. However, recognized early and treated appropriately, it typically remains innocuous. We recently developed an early phototherapy prediction tool (EPPT) by means of machine learning (ML) utilizing just one bilirubin measurement and few clinical variables. The aim of this study is to test applicability and performance of the EPPT on a new patient cohort from a different population.
This work is a retrospective study of prospectively recorded neonatal data from infants born in 2018 in an academic hospital, Regensburg, Germany, meeting the following inclusion criteria: born with 34 completed weeks of gestation or more, at least two total serum bilirubin (TSB) measurement prior to phototherapy. First, the original EPPT-an ensemble of a logistic regression and a random forest-was used in its freely accessible version and evaluated in terms of the area under the receiver operating characteristic curve (AUROC). Second, a new version of the EPPT model was re-trained on the data from the new cohort. Third, the predictive performance, variable importance, sensitivity and specificity were analyzed and compared across the original and re-trained models.
In total, 1,109 neonates were included with a median (IQR) gestational age of 38.4 (36.6-39.9) and a total of 3,940 bilirubin measurements prior to any phototherapy treatment, which was required in 154 neonates (13.9%). For the phototherapy treatment prediction, the original EPPT achieved a predictive performance of 84.6% AUROC on the new cohort. After re-training the model on a subset of the new dataset, 88.8% AUROC was achieved as evaluated by cross validation. The same five variables as for the original model were found to be most important for the prediction on the new cohort, namely gestational age at birth, birth weight, bilirubin to weight ratio, hours since birth, bilirubin value.
The individual risk for treatment requirement in neonatal hyperbilirubinemia is robustly predictable in different patient cohorts with a previously developed ML tool (EPPT) demanding just one TSB value and only four clinical parameters. Further prospective validation studies are needed to develop an effective and safe clinical decision support system.
新生儿高胆红素血症是一种全球常见疾病。然而，若能早期识别并得到恰当治疗，通常并无大碍。我们最近通过机器学习（ML）开发了一种早期光疗预测工具（EPPT），该工具仅利用一次胆红素测量值和少量临床变量。本研究旨在测试EPPT在来自不同人群的新患者队列中的适用性和性能。
本研究是一项回顾性研究，对德国雷根斯堡一家学术医院2018年出生的婴儿的前瞻性记录的新生儿数据进行分析，这些婴儿符合以下纳入标准：孕周达到或超过34周，在光疗前至少进行过两次总血清胆红素（TSB）测量。首先，使用原始的EPPT（一种逻辑回归和随机森林的集成模型）的免费可用版本，并根据受试者操作特征曲线下面积（AUROC）进行评估。其次，在新队列的数据上重新训练EPPT模型的新版本。第三，分析并比较原始模型和重新训练模型的预测性能、变量重要性、敏感性和特异性。
总共纳入了1109名新生儿，中位（四分位间距）孕周为38.4（36.6 - 39.9），在任何光疗治疗前共进行了3940次胆红素测量，其中154名新生儿（13.9%）需要进行光疗。对于光疗治疗预测，原始EPPT在新队列中的预测性能为AUROC 84.6%。在新数据集的一个子集上重新训练模型后，通过交叉验证评估，AUROC达到了88.8%。发现与原始模型相同的五个变量对新队列的预测最为重要，即出生时的孕周、出生体重、胆红素与体重比、出生后的小时数、胆红素值。
使用先前开发的仅需一个TSB值和仅四个临床参数的ML工具（EPPT），在不同患者队列中可可靠地预测新生儿高胆红素血症的个体治疗需求风险。需要进一步的前瞻性验证研究来开发一个有效且安全的临床决策支持系统。
