Department of Physiology and Cellular Development, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.
Department of Neonatal Intensive Care, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.
Int J Environ Res Public Health. 2018 Nov 9;15(11):2509. doi: 10.3390/ijerph15112509.
Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were ² > 0.97. Critical variables for IP prediction were identified: neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO₂ and HCO₃); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.
肠穿孔（IP）与坏死性小肠结肠炎（NEC）相关，是早产儿死亡的主要原因之一，还会导致严重的营养和神经发育后遗症。由于预测哪些早产儿会发生穿孔仍然具有挑战性，因此临床医生可能会从早期诊断工具和关键因素的识别中受益良多。本研究旨在预测与 NEC 相关的 IP，并基于基于机器学习的技术研究变量的预测质量。使用从 NICU 病历中构建的数据集，将反向传播神经网络用于训练和测试模型，该数据集包含出生和住院时的母婴和新生儿临床、喂养和实验室参数等输入变量。模型的结果是诊断：（1）与 NEC 相关的 IP；（2）NEC；或（3）对照（既无 IP 也无 NEC）。模型能够准确估计 IP，具有良好的性能；实验数据和预测数据之间的回归系数²>0.97。用于 IP 预测的关键变量有：新生儿血小板和中性粒细胞、经口气管插管、出生体重、性别、动脉血气参数（pCO₂和 HCO₃）、胎龄、使用强化剂、动脉导管未闭、母亲年龄和母亲发病率。这些模型可能会提高医疗实践的质量。
