Jia Mingxuan, Hu Xiyan, Ji Lin, Lin Jiawen, Liu Jialin, Wang Yong
Middlebury College, Middlebury, VT, United States.
Stanford University, Stanford, CA, United States.
Front Pediatr. 2025 Jun 6;13:1583573. doi: 10.3389/fped.2025.1583573. eCollection 2025.
Pneumonia is globally recognized as a significant disease burden, particularly among pediatric patients in intensive care units (ICU), where its etiology is complex and prognosis often poor.
Data were extracted from a pediatric-specific intensive care (PIC) database, selecting 795 pediatric pneumonia patients in ICUs (2010-2018). After applying rigorous inclusion/exclusion criteria, 543 cases formed the study cohort. We analyzed patient baseline information and 70 laboratory indicators to identify 25 prognosis-associated biomarkers. For prognostic model construction, we used stepwise regression to filter 28 variables, then Spearman and Pearson correlation analyses to identify an intersection of 14 key indicators from the top 20 features. Twelve machine learning algorithms underwent parameter tuning and combination, forming 113 model combinations for survival outcome prediction.
The "Stepglm [both] + GBM" combination achieved the highest average accuracy (79.4%) in both training and testing sets. Twelve prognostic variables were identified: WBC Count, Glucose, Neutrophils Count, Cystatin C, Temperature (body), Sodium (Whole Blood), Cholesterol (Total), Absolute Lymphocyte Count, Urea, Lactate, and Bilirubin (Total).
These 12 variables provide a dependable basis and novel insights for prognostic evaluation, supporting clinical diagnosis, treatment, and early intervention.
肺炎在全球范围内被公认为是一种重大的疾病负担，尤其是在重症监护病房（ICU）的儿科患者中，其病因复杂且预后往往较差。
从儿科重症监护（PIC）数据库中提取数据，选取795例ICU中的儿科肺炎患者（2010 - 2018年）。在应用严格的纳入/排除标准后，543例病例组成了研究队列。我们分析了患者的基线信息和70项实验室指标，以确定25个与预后相关的生物标志物。为构建预后模型，我们使用逐步回归筛选28个变量，然后通过Spearman和Pearson相关性分析从排名前20的特征中确定14个关键指标的交集。对12种机器学习算法进行参数调整和组合，形成113种用于生存结局预测的模型组合。
“Stepglm [两者] + GBM”组合在训练集和测试集中均达到了最高平均准确率（79.4%）。确定了12个预后变量：白细胞计数、葡萄糖、中性粒细胞计数、胱抑素C、体温（身体）、钠（全血）、胆固醇（总）、绝对淋巴细胞计数、尿素、乳酸和胆红素（总）。
这12个变量为预后评估提供了可靠的依据和新的见解，支持临床诊断、治疗和早期干预。
