使用电子鼻检测肺移植受者的细菌定植情况
Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose.
作者信息
Wijbenga Nynke, de Jong Nadine L A, Hoek Rogier A S, Mathot Bas J, Seghers Leonard, Aerts Joachim G J V, Bos Daniel, Manintveld Olivier C, Hellemons Merel E
机构信息
Department of Respiratory Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
Erasmus Medical Center Transplant Institute, Erasmus University Medical Center Rotterdam, Rotterdam, the Netherlands.
出版信息
Transplant Direct. 2023 Sep 20;9(10):e1533. doi: 10.1097/TXD.0000000000001533. eCollection 2023 Oct.
BACKGROUND
Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs.
METHODS
We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis.
RESULTS
In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set.
CONCLUSIONS
Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC.
背景
下呼吸道细菌定植（BC）在肺移植受者（LTRs）中很常见，并增加了慢性肺移植功能障碍的风险。诊断通常需要支气管镜检查。使用电子鼻（eNose）技术进行呼出气分析可能能够无创检测LTRs中的BC。因此，我们旨在评估eNose检测LTRs中BC的诊断准确性。
方法
我们在一项前瞻性单中心队列研究中进行了横断面分析，评估使用eNose技术检测LTRs中BC的诊断准确性。在门诊诊所，收集了连续的LTRs的eNose测量值。我们评估并分类了eNose测量值中是否存在BC。使用监督机器学习，在随机训练和验证集中评估eNose对BC的诊断准确性。使用受试者工作特征分析评估模型性能。
结果
总共纳入了161名LTRs，由于各种原因排除了80名。在其余81名患者中，16名（20%）被分类为BC，65名（80%）为非BC。基于eNose对有和无BC患者的分类在训练集中曲线下面积为0.82，在验证集中为0.97。
结论
使用eNose技术进行呼出气分析有潜力无创检测BC。
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