Gong Weihua, Gao Kaijie, Ni Jiajia, Shi Ying, Shan Zhiming, Sun Hongqi, Wang Shanshan, Xu Jiangtao, Yang Junmei
Department of Clinical Laboratory, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children's Infection and Immunity, Zhengzhou, Henan, China.
Department of Detection and Diagnosis Technology Research, Guangzhou National Laboratory, Guangzhou, China.
Front Med (Lausanne). 2025 Jun 2;12:1536705. doi: 10.3389/fmed.2025.1536705. eCollection 2025.
To construct and validate a risk factor prediction model for neonatal severe pneumonia.
This study collected data from newborns diagnosed with pneumonia in Children's Hospital Affiliated to Zhengzhou University. A total of 652 newborns were included. Risk factors were identified using Least Absolute Selection and Shrinkage Operator (LASSO) regression and logistic regression analysis. The nomogram was used to construct a prediction model. The effectiveness of the model was evaluated using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).
Out of 652 newborns, 186 (29%) were diagnosed with severe pneumonia. The patients were randomly divided into a training set ( = 554) and a testing set ( = 98) in a ratio of 85:15. A total of 30 indicators were analyzed. Respiratory rate (OR = 1.058, 95% CI: 1.035-1.081), weight (OR = 0.483, 95% CI: 0.340-0.686), C-reactive protein (CRP) (OR = 1.142, 95% CI: 1.028-1.268), neutrophil (NEU) (OR = 1.384, 95% CI: 1.232-1.555), hemoglobin (HGB) (OR = 0.989, 95% CI: 0.979-0.999), uric acid (UA) (OR = 1.006, 95% CI: 1.002-1.010), and blood urea nitrogen (BUN) (OR = 1.230, 95% CI: 1.058-1.431) were identified as independent risk factors for neonatal severe pneumonia. The calibration curve showed significant agreement. The area under the ROC curve (AUC) was 0.884 (95% CI: 0.852-0.916) for the training set, and 0.835 (95% CI: 0.747-0.922) for the testing set. DCA demonstrated good predictive properties.
The prediction model based on respiratory rate, weight, CRP, NEU, HGB, UA, and BUN has shown promising predictive value in distinguishing between mild to moderate pneumonia and severe pneumonia in neonates.
构建并验证新生儿重症肺炎的危险因素预测模型。
本研究收集了郑州大学附属儿童医院诊断为肺炎的新生儿的数据。共纳入652例新生儿。使用最小绝对收缩选择算子（LASSO）回归和逻辑回归分析确定危险因素。使用列线图构建预测模型。使用校准曲线、受试者工作特征（ROC）曲线和决策曲线分析（DCA）评估模型的有效性。
652例新生儿中，186例（29%）被诊断为重症肺炎。患者按85:15的比例随机分为训练集（=554）和测试集（=98）。共分析了30项指标。呼吸频率（OR=1.058，95%CI：1.035-1.081）、体重（OR=0.483，95%CI：0.340-0.686）、C反应蛋白（CRP）（OR=1.142，95%CI：1.028-1.268）、中性粒细胞（NEU）（OR=1.384，95%CI：1.232-1.555）、血红蛋白（HGB）（OR=0.989，95%CI：0.979-0.999）、尿酸（UA）（OR=1.006，95%CI：1.002-1.010）和血尿素氮（BUN）（OR=1.230，95%CI：1.058-1.431）被确定为新生儿重症肺炎的独立危险因素。校准曲线显示出显著的一致性。训练集的ROC曲线下面积（AUC）为0.884（95%CI：0.852-0.916），测试集为0.835（95%CI：0.747-0.922）。DCA显示出良好的预测性能。
基于呼吸频率、体重、CRP、NEU、HGB、UA和BUN的预测模型在区分新生儿轻度至中度肺炎和重症肺炎方面显示出有前景的预测价值。
