Usemann Jakob, Xu Binbin, Delgado-Eckert Edgar, Korten Insa, Anagnostopoulou Pinelopi, Gorlanova Olga, Kuehni Claudia, Röösli Martin, Latzin Philipp, Frey Urs
University Children's Hospital Basel (UKBB), Basel, Switzerland.
Paediatric Respiratory Medicine, Dept of Paediatrics, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
ERJ Open Res. 2018 Nov 20;4(4). doi: 10.1183/23120541.00037-2018. eCollection 2018 Oct.
Children with frequent respiratory symptoms in infancy have an increased risk for later wheezing, but the association with symptom dynamics is unknown. We developed an observer-independent method to characterise symptom dynamics and tested their association with subsequent respiratory morbidity. In this birth-cohort of healthy neonates, we prospectively assessed weekly respiratory symptoms during infancy, resulting in a time series of 52 symptom scores. For each infant, we calculated the transition probability between two consecutive symptom scores. We used these transition probabilities to construct a Markov matrix, which characterised symptom dynamics quantitatively using an entropy parameter. Using this parameter, we determined phenotypes by hierarchical clustering. We then studied the association between phenotypes and wheezing at 6 years. In 322 children with complete data for symptom scores during infancy (16 864 observations), we identified three dynamic phenotypes. Compared to the low-risk phenotype, the high-risk phenotype, defined by the highest entropy parameter, was associated with an increased risk of wheezing (odds ratio (OR) 3.01, 95% CI 1.15-7.88) at 6 years. In this phenotype, infants were more often male (64%) and had been exposed to environmental tobacco smoke (31%). In addition, more infants had siblings (67%) and attended childcare (38%). We describe a novel method to objectively characterise dynamics of respiratory symptoms in infancy, which helps identify abnormal clinical susceptibility and recovery patterns of infant airways associated with persistent wheezing.
婴儿期频繁出现呼吸道症状的儿童日后发生喘息的风险增加，但与症状动态变化的关联尚不清楚。我们开发了一种独立于观察者的方法来描述症状动态变化，并测试了它们与随后呼吸道疾病的关联。在这个健康新生儿出生队列中，我们前瞻性地评估了婴儿期每周的呼吸道症状，得出了一个包含52个症状评分的时间序列。对于每个婴儿，我们计算了两个连续症状评分之间的转移概率。我们使用这些转移概率构建了一个马尔可夫矩阵，该矩阵使用熵参数定量描述症状动态变化。利用这个参数，我们通过层次聚类确定了表型。然后，我们研究了这些表型与6岁时喘息之间的关联。在322名有婴儿期症状评分完整数据的儿童（16864次观察）中，我们确定了三种动态表型。与低风险表型相比，由最高熵参数定义的高风险表型与6岁时喘息风险增加相关（优势比（OR）3.01，95%可信区间1.15-7.88）。在这个表型中，婴儿男性比例更高（64%），并且接触过环境烟草烟雾（31%）。此外，更多婴儿有兄弟姐妹（67%）并参加过托儿服务（38%）。我们描述了一种客观描述婴儿期呼吸道症状动态变化的新方法，这有助于识别与持续性喘息相关的婴儿气道异常临床易感性和恢复模式。
