Wheezing Diagnosis Using a Smartphone in Infants Referred for Bronchiolitis
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 600
- 试验地点
- 1
- 主要终点
- positive and negative predictive values of the algorithm for wheezing diagnosis
研究概览
简要总结
Abnormal respiratory sounds (wheezing and/or crackles) are diagnosis criteria of acute bronchiolitis. One third of these infants will develop recurrent episodes, leading to the diagnosis of infant asthma. Nowadays, no available treatment shortens the course of bronchiolitis or hastens the resolution of symptoms, thus, therapy is supportive. Our hypothesis is that the diagnosis of wheezing during bronchiolitis (~60% of infants) will help to select infants who will benefit from anti-asthma therapy. In this setting the diagnosis of wheezing is crucial, and an objective tool for recognition of wheezing is of clinical value. The investigators developed a wheezing recognition algorithm from recorded respiratory sounds with a Smartphone placed near the mouth (Bokov P, Comput Biol Med, 2016). The objectives of the present cross sectional, observational study are 1/ to further validate our approach in a larger sample of infants (1 to 24 months) admitted to hospital for a respiratory complaint during the period of viral bronchiolitis, and 2/ to use gold standard diagnosis of wheezing by respiratory sound recording (Littmann) and subsequent analysis by two experienced pediatricians.
详细描述
Infants (1 to 24 months old) are recruited in two emergency departments (Robert Debré; Antoine Béclère hospitals of Assistance publique - Hôpitaux de Paris) based on a respiratory complaint. Six characteristics are recorded (age, sex, SpO2, presence or absence of wheezing, other respiratory sound, initial diagnosis). Two recordings of respiratory sounds are obtained almost simultaneously: one with a Smartphone at the mouth (5 cm) and one with an electronic stetoscope (Littmann). Two expert pediatricians listen the recordings giving thee groups: with wheezing (agreement), without wheezing (agreement) and non agreement diagnosis. The recordings made with the Smartphone are subjected to the wheezing recognition algorithm as previously described. The sensitivity, specificity, PPV, NPP are then evaluated. The algorithm will further be improved if necessary using the true negative and true positive recordings (those with expert agreement).
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 1 Month 至 24 Months(Child)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •infant 1 to 24 months old
- •respiratory complaint in the emergency room
排除标准
- 未提供
结局指标
主要结局
positive and negative predictive values of the algorithm for wheezing diagnosis
时间窗: 8 months
次要结局
- sensibility and specificity of the algorithm in subgroups(8 months)
