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Abstract：
Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine learning (ML) algorithm to predict sNEC using perinatal factors obtained from the national cohort registry of very low birth weight (VLBW) infants. Data were collected from the medical records of 16,385 VLBW infants registered in the Korean Neonatal Network (KNN). Infants who underwent surgical intervention were identified with sNEC, and infants who received medical treatment, with medical NEC (mNEC). We used 38 variables, including maternal, prenatal, and postnatal factors that were obtained within 1 week of birth, for training. A total of 1085 patients had NEC (654 with sNEC and 431 with mNEC). VLBW infants showed a higher incidence of sNEC at a lower gestational age (GA) (p < 0.001). Our proposed ensemble model showed an area under the receiver operating characteristic curve of 0.721 for sNEC prediction. Conclusion: Proposed ensemble model may help predict which infants with NEC are likely to develop sNEC. Through early prediction and prompt intervention, prognosis of sNEC may be improved. What is Known: • Machine learning (ML)-based techniques have been employed in NEC research for prediction, diagnosis, and prognosis, with promising outcomes. • While most studies have utilized abdominal radiographs and clinical manifestations of NEC as data sources, and have demonstrated their usefulness, they may prove weak in terms of early prediction. What is New: • We analyzed the perinatal factors of VLBW infants acquired within 7 days of birth and used ML-based analysis to identify which infants with NEC are vulnerable to clinical deterioration and at high risk for surgical intervention using nationwide cohort data.
摘要：
早期预测早产儿的外科坏死性小肠结肠炎（sNEC）很重要。然而,由于疾病的复杂性，很难确定NEC患儿的手术干预风险较高.我们开发了一种机器学习（ML）算法，使用从极低出生体重（VLBW）婴儿的国家队列注册获得的围产期因素来预测sNEC。数据是从在韩国新生儿网络（KNN）注册的16,385名VLBW婴儿的医疗记录中收集的。接受手术干预的婴儿被确定为sNEC，和接受治疗的婴儿，医疗NEC（mNEC）。我们使用了38个变量，包括产妇,产前,以及出生后1周内获得的出生后因素，用于训练。共有1085名患者患有NEC（654名患者患有sNEC，431名患者患有mNEC）。VLBW婴儿在较低的胎龄（GA）时显示出更高的sNEC发生率（p<0.001）。我们提出的集成模型显示，用于sNEC预测的接收器工作特性曲线下的面积为0.721。结论：提出的集成模型可能有助于预测哪些NEC婴儿可能发生sNEC。通过早期预测和及时干预，sNEC的预后可能得到改善。已知：•基于机器学习（ML）的技术已在NEC研究中用于预测，诊断,和预后，有希望的结果。•虽然大多数研究都利用腹部X光片和NEC的临床表现作为数据来源，并证明了它们的有用性，就早期预测而言，它们可能会很弱。我们分析了出生后7天内获得的VLBW婴儿的围产期因素，并使用基于ML的分析来确定哪些NEC婴儿容易发生临床恶化，并使用全国队列数据进行手术干预。
