Improving the Diagnosis of Phenylketonuria by Using a Machine Learning–Based Screening Model of Neonatal MRM Data - Zhu, Zhixing; Gu, Jianlei; Genchev, Georgi Z.; Cai, Xiaoshu; Wang, Yangmin; Guo, Jing; Tian, Guoli; Lu, Hui (Frontiers in Molecular Biosciences, 2020)
Phenylketonuria (PKU) is a common genetic metabolic disorder that affects infant's nerve development and manifests as abnormal behavior and developmental delay as the child grows. Currently, tandem mass spectrometry (MS/MS) is common high-accuracy clinical PKU screening method. However, there is high false positive rate associated with this modality and its reduction can provide a diagnostic and economic benefit to both pediatric patients and health providers. Machine learning methods have the advantage of utilizing high-dimensional and complex features, which can be obtained from the patient's metabolic patterns and interrogated for clinically relevant knowledge. In this study, using MS/MS screening data of more than 600 000 patients collected at the Newborn Screening Center of Shanghai Children's Hospital, we derive a dataset containing 256 PKUsuspected cases. We then develop a machine learning logistic regression analysis model with the aim to minimize false positive rates in the results of the initial PKU test. The model attained a 95%~100% sensitivity, the specificity was improved 63.61%, PPV increased from 19.14% to 37.68%. The present study shows that machine learning models may be used as a pediatric diagnosis aid tool to reduce the number of suspected cases and to help eliminate patient recall. Our study can serve as a future reference for the selection and evaluation of computational screening methods.