Mou Yanling, Li Jinhao, Wang Jianjun, Yu Daiyue, Yang Huirong, Zhang Xi, Tan Rongying, Adam Mahamat Djibril, Wu Kai
Department of Pediatric Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
J Matern Fetal Neonatal Med. 2025 Dec;38(1):2521798. doi: 10.1080/14767058.2025.2521798. Epub 2025 Jun 23.
: Early identification and timely preventive interventions play an essential role for improving the prognosis of newborns with necrotizing enterocolitis (NEC). Thus, establishing a novel and simple prediction model is of great clinical significance. : The clinical data of 143 NEC neonates in the Zhujiang Hospital of Southern Medical University from October 2010 to October 2022 were collected, whereas 429 non-NEC patients in the same period were allocated to the control group by random sampling. Afterward, all participants were randomly divided into a training group (70%) and a testing group (30%). Then, five machine learning (ML) algorithms and classical logistic regression models were established, combining relevant clinical features and laboratory results. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of various models were compared to evaluate the performance of each model. Ten-folds cross-validation was used to find the best hyperparameters for each model. Decision curve analysis (DCA) was further used to evaluate the performance of the established models for clinical applications and create a column-line graph, ranking the feature importance in models by SHapely Additive exPlanation (SHAP). The column plots were calibrated using calibration curves. Additionally, the established model was validated in time series analysis and another medical center. : Six important features were included for modeling: days of age (odds ratio [OR] = 1.16; 95% confidence interval [CI]: 1.08-1.25; = 0.001), gestational age (OR = 0.77; 95% CI: 0.62-0.96; = 0.018), eosinophil count (EOS) (OR = 3.78; 95% CI: 1.74-8.19; < 0.001), hemoglobin (HB) (OR = 0.98; 95% CI: 0.97-1.00; = 0.008), platelet distribution width (PDW) (OR = 1.18; 95% CI: 1.05-1.33; = 0.004), and high-sensitivity C-reactive protein (HSCRP) (OR = 1.03; 95% CI: 1.01-1.06; = 0.013). While the logistic regression model achieved an AUC of 0.904, accuracy of 0.865, sensitivity of 0.786, F1-score of 0.742, and a Brier score of 0.1009 in the training group, the AUCs for the five ML models ranged from 0.806 to 0.960. Among these models, the LightGBM model performed the best, providing an AUC of 0.960, accuracy of 0.858, sensitivity of 0.970, F1-score of 0.775, and a Brier score of 0.071. : The LightGBM ML model can effectively identify neonatal patients at higher risk of NEC based on the day of age, gestational age, EOS, and HB, PDW, and HSCRP levels. Thus, this model is useful for assisting clinical decision-making.
早期识别和及时的预防性干预对于改善坏死性小肠结肠炎（NEC）新生儿的预后起着至关重要的作用。因此，建立一种新颖且简单的预测模型具有重大的临床意义。收集了南方医科大学珠江医院2010年10月至2022年10月期间143例NEC新生儿的临床资料，同时通过随机抽样将同期429例非NEC患者分配到对照组。之后，将所有参与者随机分为训练组（70%）和测试组（30%）。然后，结合相关临床特征和实验室结果，建立了五种机器学习（ML）算法和经典逻辑回归模型。比较各模型的受试者操作特征曲线下面积（AUC）、准确率、灵敏度和特异性，以评估每个模型的性能。采用十折交叉验证来寻找每个模型的最佳超参数。进一步使用决策曲线分析（DCA）来评估所建立模型在临床应用中的性能，并创建柱状线图，通过SHapely Additive exPlanation（SHAP）对模型中的特征重要性进行排名。使用校准曲线对柱状图进行校准。此外，在时间序列分析和另一个医疗中心对所建立的模型进行了验证。建模纳入了六个重要特征：日龄（比值比[OR]=1.16；95%置信区间[CI]：1.08 - 1.25；P = 0.001）、胎龄（OR = 0.77；95% CI：0.62 - 0.96；P = 0.018）、嗜酸性粒细胞计数（EOS）（OR = 3.78；95% CI：1.74 - 8.19；P < 0.001）、血红蛋白（HB）（OR = 0.98；95% CI：0.97 - 1.00；P = 0.008）、血小板分布宽度（PDW）（OR = 1.18；95% CI：1.05 - 1.33；P = 0.004）和高敏C反应蛋白（HSCRP）（OR = 1.03；95% CI：1.01 - 1.06；P = 0.013）。在训练组中，逻辑回归模型的AUC为0.904，准确率为0.865，灵敏度为0.786，F1值为0.742，布里尔分数为0.1009，而五个ML模型的AUC范围为0.806至0.960。在这些模型中，LightGBM模型表现最佳，AUC为0.960，准确率为0.858，灵敏度为0.970，F1值为0.775，布里尔分数为0.071。LightGBM ML模型可以根据日龄、胎龄、EOS以及HB、PDW和HSCRP水平有效地识别NEC风险较高的新生儿患者。因此，该模型有助于临床决策。
