Makrinioti Heidi, Maggina Paraskevi, Lakoumentas John, Xepapadaki Paraskevi, Taka Stella, Megremis Spyridon, Manioudaki Maria, Johnston Sebastian L, Tsolia Maria, Papaevangelou Vassiliki, Papadopoulos Nikolaos G
West Middlesex University Hospital, Chelsea and Westminster Foundation Trust, Isleworth, United Kingdom.
Centre for Paediatrics and Child Health, Imperial College London, London, United Kingdom.
Front Allergy. 2021 Nov 2;2:728389. doi: 10.3389/falgy.2021.728389. eCollection 2021.
Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, infants with hospitalized bronchiolitis are more likely to develop wheeze exacerbations during the first years of life. The objective of this prospective cohort study was to develop machine learning models to predict incidence and persistence of wheeze exacerbations following the first hospitalized episode of acute bronchiolitis. One hundred thirty-one otherwise healthy term infants hospitalized with the first episode of bronchiolitis at a tertiary pediatric hospital in Athens, Greece, and 73 age-matched controls were recruited. All patients/controls were followed up for 3 years with 6-monthly telephone reviews. Through principal component analysis (PCA), a cluster model was used to describe main outcomes. Associations between virus type and the clusters and between virus type and other clinical characteristics and demographic data were identified. Through random forest classification, a prediction model with smallest classification error was identified. Primary outcomes included the incidence and the number of caregiver-reported wheeze exacerbations. PCA identified 2 clusters of the outcome measures (Cluster 1 and Cluster 2) that were significantly associated with the number of recurrent wheeze episodes over 3-years of follow-up (Chi-Squared, < 0.001). Cluster 1 included infants who presented higher number of wheeze exacerbations over follow-up time. Rhinovirus (RV) detection was more common in Cluster 1 and was more strongly associated with clinical severity on admission ( < 0.01). A prediction model based on virus type and clinical severity could predict Cluster 1 with an overall error 0.1145 (sensitivity 75.56% and specificity 91.86%). A prediction model based on virus type and clinical severity of first hospitalized episode of bronchiolitis could predict sensitively the incidence and persistence of wheeze exacerbations during a 3-year follow-up. Virus type (RV) was the strongest predictor.
急性细支气管炎是婴儿期最常见的呼吸道感染之一。虽然大多数患细支气管炎的婴儿不住院，但住院的细支气管炎婴儿在生命的头几年更有可能出现喘息加重。这项前瞻性队列研究的目的是开发机器学习模型，以预测急性细支气管炎首次住院发作后喘息加重的发生率和持续时间。在希腊雅典的一家三级儿科医院，招募了131名因首次细支气管炎发作而住院的健康足月儿和73名年龄匹配的对照者。所有患者/对照者均接受了3年的随访，每6个月进行一次电话复查。通过主成分分析（PCA），使用聚类模型来描述主要结果。确定了病毒类型与聚类之间以及病毒类型与其他临床特征和人口统计学数据之间的关联。通过随机森林分类，确定了分类误差最小的预测模型。主要结果包括照顾者报告的喘息加重的发生率和次数。PCA确定了2个结果测量聚类（聚类1和聚类2），它们与3年随访期间反复喘息发作的次数显著相关（卡方检验，<0.001）。聚类1包括在随访期间出现喘息加重次数较多的婴儿。鼻病毒（RV）检测在聚类1中更常见，并且与入院时的临床严重程度更密切相关（<0.01）。基于病毒类型和临床严重程度的预测模型可以预测聚类1，总体误差为0.1145（敏感性75.56%，特异性91.86%）。基于细支气管炎首次住院发作的病毒类型和临床严重程度的预测模型可以敏感地预测3年随访期间喘息加重的发生率和持续时间。病毒类型（RV）是最强的预测因子。
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