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Bronchodilator response assessed using the forced oscillation technique identifies poor asthma control with greater sensitivity than spirometry.

使用强迫振荡技术评估的支气管扩张剂反应识别出哮喘控制不佳,其灵敏度高于肺活量测定。

  • 影响因子:3.84
  • DOI:10.1016/j.chest.2019.12.035
  • 作者列表:"Cottee AM","Seccombe LM","Thamrin C","King GG","Peters MJ","Farah CS
  • 发表时间:2020-01-23
Abstract

BACKGROUND:Persistent bronchodilator response (BDR) following diagnosis of asthma is an under-recognized treatable trait, associated with worse lung function and asthma control. The forced oscillation technique (FOT) measures respiratory system impedance and BDR cutoffs have been proposed for healthy adults, however relevance in asthma is unknown. We compared BDR cutoffs using FOT and spirometry in asthma and the relationship with asthma control. METHODS:Data from patients with asthma who withheld bronchodilator medication for at least eight hours prior to a tertiary Airways clinic visit was reviewed. All subjects performed FOT and spirometry before and after salbutamol, and completed an asthma control test. FOT parameters examined included respiratory system resistance (R5) and reactance (X5) at 5Hz, and area under the reactance curve (AX). BDR was defined by standard recommendations for spirometry and based on the 95th percentile of BDR in healthy adults for FOT. RESULTS:52 subjects (18 male; mean age 53±18 years) were included. BDR was identified more frequently by FOT than spirometry (54% vs 27% subjects). BDR assessed by X5 and AX, but not R5, were associated with spirometric BDR (χ2 p<0.01) and correlated with asthma control (X5: rs=-0.36, p<0.01; AX: rs=0.34, p=0.01). BDR measured by reactance parameters identified more subjects with poor asthma control than spirometry (AX 69% vs spirometry 41%). Conclusion: BDR assessed using FOT can identify poor asthma control. Reactance parameters were more sensitive in identifying poor asthma control than spirometry, supporting the use of FOT to complement spirometry in the clinical management of asthma.

摘要

背景: 诊断为哮喘后的持续支气管扩张剂反应 (BDR) 是一种认识不足的可治疗特征,与较差的肺功能和哮喘控制相关。强迫振荡技术 (FOT) 测量呼吸系统阻抗和 BDR 截断值已被提出用于健康成人,但在哮喘中的相关性尚不清楚。我们比较了哮喘中使用 FOT 和肺活量测定法的 BDR 临界值以及与哮喘控制的关系。 方法: 回顾了在三级气道门诊就诊前至少 8 小时不服用支气管扩张剂的哮喘患者的数据。所有受试者在沙丁胺醇前后进行 FOT 和肺活量测定,并完成哮喘控制测试。检查的 FOT 参数包括 5Hz 时的呼吸系统电阻 (R5) 和电抗 (X5),以及电抗曲线下面积 (AX)。BDR 由肺活量测定法的标准建议定义,并基于健康成人 FOT 的 BDR 的第 95 百分位数。 结果: 纳入 52 例受试者 (18 例男性; 平均年龄 53 ± 18 岁)。FOT 比肺活量测定法更频繁地识别 BDR (54% vs 27% 例受试者)。X5 和 AX 评估的 BDR 与肺活量 BDR 相关 (χ 2 p<0.01),与哮喘控制相关 (X5: rs =-0.36,p<0.01; AX: rs = 0.34,p = 0.01)。通过电抗参数测量的 BDR 比肺活量测定法确定了更多哮喘控制不佳的受试者 (AX 69% vs 肺活量测定法 41%)。 结论: 使用 FOT 评估的 BDR 可以识别哮喘控制不佳。电抗参数较多。比肺活量测定法在识别哮喘控制不佳方面敏感,支持在哮喘的临床管理中使用 FOT 来补充肺活量测定法。

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影响因子:3.31
发表时间:2020-01-02
DOI:10.1007/s10916-019-1481-4
作者列表:["Matava, Clyde","Pankiv, Evelina","Raisbeck, Sam","Caldeira, Monica","Alam, Fahad"]

METHODS:Background The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinical procedures such as airway management. In this study, we sought to develop a machine learning algorithm that could classify vocal cords and tracheal airway anatomy real-time during video laryngoscopy or bronchoscopy as well as compare the performance of three novel convolutional networks for detecting vocal cords and tracheal rings. Methods Following institutional approval, a clinical dataset of 775 video laryngoscopy and bronchoscopy videos was used. The dataset was divided into two categories for use for training and testing. We used three convolutional neural networks (CNNs): ResNet, Inception and MobileNet. Backpropagation and a mean squared error loss function were used to assess accuracy as well as minimize bias and variance. Following training, we assessed transferability using the generalization error of the CNN, sensitivity and specificity, average confidence error, outliers, overall confidence percentage, and frames per second for live video feeds. After the training was complete, 22 models using 0 to 25,000 steps were generated and compared. Results The overall confidence of classification for the vocal cords and tracheal rings for ResNet, Inception and MobileNet CNNs were as follows: 0.84, 0.78, and 0.64 for vocal cords, respectively, and 0.69, 0.72, 0.54 for tracheal rings, respectively. Transfer learning following additional training resulted in improved accuracy of ResNet and Inception for identifying the vocal cords (with a confidence of 0.96 and 0.93 respectively). The two best performing CNNs, ResNet and Inception, achieved a specificity of 0.985 and 0.971, respectively, and a sensitivity of 0.865 and 0.892, respectively. Inception was able to process the live video feeds at 10 FPS while ResNet processed at 5 FPS. Both were able to pass a feasibility test of identifying vocal cords and tracheal rings in a video feed. Conclusions We report the development and evaluation of a CNN that can identify and classify airway anatomy in real time. This neural network demonstrates high performance. The availability of artificial intelligence may improve airway management and bronchoscopy by helping to identify key anatomy real time. Thus, potentially improving performance and outcomes during these procedures. Further, this technology may theoretically be extended to the settings of airway pathology or airway management in the hands of experienced providers. The researchers in this study are exploring the performance of this neural network in clinical trials.

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影响因子:3.84
发表时间:2020-01-01
来源期刊:Chest
DOI:10.1016/j.chest.2019.06.018
作者列表:["Dhooria S","Chaudhary S","Ram B","Sehgal IS","Muthu V","Prasad KT","Aggarwal AN","Agarwal R"]

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