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Montgomery T-tube for management of tracheomalacia: Impact on voice-related quality of life.

Montgomery t管治疗气管软化: 对语音相关生活质量的影响。

  • 影响因子:1.39
  • DOI:10.1111/crj.13098
  • 作者列表:"Fiorelli A","Natale G","Freda C","Cascone R","Carlucci A","Costanzo S","Ferrara V","Santini M
  • 发表时间:2020-01-01
Abstract

INTRODUCTION:Tracheostomy is a common procedure for management of tracheomalacia. However, the limitation to speak related to tracheostomy cannula could affect the quality of life. OBJECTIVES:we reported a new minimally invasive procedure to replace tracheostomy cannula with Montgomery T-tube to improve the ability of speaking. METHODS:This is a single center study including all consecutive patients undergoing the replacement of standard tracheostomy cannula with T-tube for management of tracheomalacia. The end-points were to evaluate (a) the changes in Voice-related quality of Life (V-RQOL) before and after T-tube placement; and (b) the complications related to T-tube. RESULTS:Eleven patients were included in the study. T-tube was placed using flexible bronchoscopy and laryngeal mask airway. A suture was inserted through the proximal end of T-tube. Once the stent was introduced with a clamp into the trachea, a traction was applied on the suture to facilitate the alignment of the upper end of the stent. The comparison of V-RQOL values before and after T-tube insertion showed a significant improvement in social/emotional (39.2 ± 6.1 vs 66.8 ± 1.9; P = .0001); physical functioning (21 ± 5.7 vs 56.4 ± 5.3; P = 0.0001) and total V-RQOL scores (33.9 + 5.4 vs 61.3 + 6.1; P = 0.0001). No complications were seen during the insertion of the stent. In two patients, T-tube was obstructed by mucus that resolved with aspiration using flexible bronchoscopy (mean follow-up: 18 ± 10 months). CONCLUSIONS:Our technique is simple and safe, not needing specific skills and/or cumbersome devices. The replacement of tracheostomy cannula with T-tube seems to improve the quality of voice without adding major complications.

摘要

简介: 气管切开术是治疗气管软化的常用方法。然而,与气管切开套管相关的说话限制可能会影响生活质量。 目的: 我们报道了一种新的微创手术,用蒙哥马利 t型管代替气管切开套管,以提高说话能力。 方法: 这是一项单中心研究,包括所有接受标准气管造口套管更换 t管治疗气管软化的连续患者。终点是评估 (a) t管放置前后语音相关生活质量 (V-RQOL) 的变化; 和 (b) 与 t管相关的并发症。 结果: 11 例患者纳入研究。使用可弯曲支气管镜和喉罩置入 t管。通过 t管的近端插入缝线。一旦用夹子将支架引入气管,就在缝线上施加牵引,以促进支架上端的对齐。T 管插入前后 V-RQOL 值的比较显示社会/情绪显著改善 (39.2 ± 6.1 vs 66.8 ± 1.9; P =。 0001); 身体功能 (21 ± 5.7 vs 56.4 ± 5.3; P = 0.0001) 和总 V-RQOL 评分 (33.9 + 5.4 vs 61.3 + 6.1; P = 0.0001)。支架置入过程中未见并发症。在 2 例患者中,t管被粘液阻塞,通过使用柔性支气管镜抽吸消退 (平均随访: 18 ± 10 个月)。 结论: 我们的技术简单安全,不需要特定的技能和/或繁琐的设备。用 t管更换气管造口套管似乎可以改善语音质量,而不会增加主要并发症。

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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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