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Suspension microarray-based comparison of oropharyngeal swab and bronchoalveolar lavage fluid for pathogen identification in young children hospitalized with respiratory tract infection.

基于悬浮芯片的口咽拭子和支气管肺泡灌洗液的比较,用于呼吸道感染住院幼儿的病原体鉴定。

  • 影响因子:2.79
  • DOI:10.1186/s12879-020-4900-8
  • 作者列表:"Ma ZY","Deng H","Hua LD","Lei W","Zhang CB","Dai QQ","Tao WJ","Zhang L
  • 发表时间:2020-02-22
Abstract

BACKGROUND:Respiratory tract infection (RTI) in young children is a leading cause of morbidity and hospitalization worldwide. There are few studies assessing the performance for bronchoalveolar lavage fluid (BALF) versus oropharyngeal swab (OPS) specimens in microbiological findings for children with RTI. The primary purpose of this study was to compare the detection rates of OPS and paired BALF in detecting key respiratory pathogens using suspension microarray. METHODS:We collected paired OPS and BALF specimens from 76 hospitalized children with respiratory illness. The samples were tested simultaneously for 8 respiratory viruses and 5 bacteria by suspension microarray. RESULTS:Of 76 paired specimens, 62 patients (81.6%) had at least one pathogen. BALF and OPS identified respiratory pathogen infections in 57 (75%) and 49 (64.5%) patients, respectively (P > 0.05). The etiology analysis revealed that viruses were responsible for 53.7% of the patients, whereas bacteria accounted for 32.9% and Mycoplasma pneumoniae for 13.4%. The leading 5 pathogens identified were respiratory syncytial virus, Streptococcus pneumoniaee, Haemophilus influenzae, Mycoplasma pneumoniae and adenovirus, and they accounted for 74.2% of etiological fraction. For detection of any pathogen, the overall detection rate of BALF (81%) was marginally higher than that (69%) of OPS (p = 0.046). The differences in the frequency distribution and sensitivity for most pathogens detected by two sampling methods were not statistically significant. CONCLUSIONS:In this study, BALF and OPS had similar microbiological yields. Our results indicated the clinical value of OPS testing in pediatric patients with respiratory illness.

摘要

背景: 幼儿呼吸道感染 (RTI) 是世界范围内发病和住院的主要原因。很少有研究评估支气管肺泡灌洗液 (BALF) 与口咽拭子 (OPS) 标本在 RTI 患儿微生物学结果中的表现。本研究的主要目的是使用悬浮芯片比较 OPS 和配对 BALF 检测关键呼吸道病原体的检出率。 方法: 收集 76 例呼吸系统疾病住院患儿的 OPS 和 BALF 标本。用悬浮芯片对样品同时进行 8 种呼吸道病毒和 5 种细菌的检测。 结果: 在 76 例配对标本中,62 例患者 (81.6%) 至少有一种病原体。支气管肺泡灌洗液 (BALF) 和 OPS 分别检出呼吸道感染 57 例 (75%) 和 49 例 (64.5%) (p> 0.05)。病原学分析发现,病毒占 53.7%,细菌占 32.9%,肺炎支原体占 13.4%。主要病原菌为呼吸道合胞病毒、肺炎链球菌、流感嗜血杆菌、肺炎支原体和腺病毒,占病原学的 74.2%。BALF 的总检出率 (81%) 略高于 OPS 的总检出率 (69%) (p = 0.046)。两种采样方法检测大多数病原体的频率分布和敏感性差异无统计学意义。 结论: 在本研究中,BALF 和 OPS 具有相似的微生物产量。我们的结果表明 OPS 检测在儿科呼吸系统疾病患者中的临床价值。

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DOI:10.1007/s00464-019-07334-4
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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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