基于集成模型预测食物过敏儿童学龄期哮喘风险

文献信息

中文摘要

背景： 生命早期食物过敏（FA）是儿童哮喘的主要风险因素，但针对0-2岁高风险婴儿的前瞻性筛查工具仍缺乏，延误了早期干预。
目的： 开发一种具有成本敏感优化的集成学习模型，用于FA婴儿（0-2岁）的早期哮喘风险筛查，并提供定量临床决策支持。
方法： 本前瞻性队列纳入447名确诊FA的婴儿（0-2岁），收集7个类别的135项基线预测因子。在中位4.5年随访至学龄期（≥6岁）后，我们生成3个临床可解释特征，并构建两阶段加权集成模型（随机森林、XGBoost、LightGBM、深度残差网络），采用成本敏感学习（漏诊与误报比=5:1）以优化阈值。
结果： 在独立测试集上，AUC=0.886，召回率=1.00（零漏诊）。决策曲线分析显示在广泛阈值范围内具有更优的净获益，包括最佳截断值0.09。最重要的预测因子：RSV感染、喘息频率、夜间睡眠、抗生素暴露和鼻炎持续时间。敏感性分析证实，在成本比≥5:1时稳定保持零漏诊，表明5:1并非关键驱动因素。
结论： 该模型初步预测FA婴儿的学龄期哮喘风险。其高召回设计避免了漏诊，并为2岁前的早期干预和个体化随访提供定量参考。
影响： 核心发现：一种性能加权集成模型仅使用临床数据即可预测食物过敏婴儿（0-2岁）的学龄期哮喘，AUC为0.886，召回率为1.00。贡献：一种针对该高风险人群定制的成本敏感、特征工程化模型；确定RSV、喘息分级和抗生素负荷为最重要的预测因子。
意义： 可在2岁前实现早期、无创筛查，避免遗漏高风险儿童，并支持初级保健中的针对性预防干预。

英文摘要

BACKGROUND: Early-life food allergy (FA) is a major childhood asthma risk, but prospective screening tools for high-risk infants aged 0-2 are lacking, delaying early intervention.
OBJECTIVE: To develop an ensemble-learning model with cost-sensitive optimization for early asthma risk screening in FA infants (0-2 years) and provide quantitative clinical decision support.
METHODS: This prospective cohort enrolled 447 FA-confirmed infants (0-2 years), collecting 135 baseline predictors across 7 categories. After median 4.5-year follow-up to school age (≥6 years), we generated 3 clinically interpretable features and built a two-stage weighted ensemble (Random Forest, XGBoost, LightGBM, Deep Residual Networks) with cost-sensitive learning (missed-to-false-alarm ratio = 5:1) to optimize thresholds.
RESULTS: On the independent test set, AUC = 0.886, recall = 1.00 (zero missed). Decision-curve analysis showed superior net benefit across broad thresholds, including optimal cutoff 0.09. Top predictors: RSV infection, wheeze frequency, nocturnal sleep, antibiotic exposure, and rhinitis duration. Sensitivity analysis confirmed stable zero missed at cost ratios ≥5:1, indicating 5:1 was not the key driver.
CONCLUSION: The model preliminarily predicts school-age asthma risk in FA infants. Its high-recall design avoids missed diagnoses and offers quantitative references for early intervention and individualized follow-up before age 2.
IMPACT: Core finding: A performance-weighted ensemble model predicts school-age asthma in food-allergic infants (0-2 years) with an AUC of 0.886 and recall 1.00 using only clinical data. Contribution: A cost-sensitive, feature-engineered model tailored to this high-risk population; identifies RSV, wheeze grading, and antibiotic load as top predictors.
SIGNIFICANCE: Enables early, non-invasive screening before age 2, avoids missed high-risk children, and supports targeted preventive interventions in primary care.