Bax Carmen, Robbiani Stefano, Zannin Emanuela, Capelli Laura, Ratti Christian, Bonetti Simone, Novelli Luca, Raimondi Federico, Di Marco Fabiano, Dellacà Raffaele L
Department of Chemistry, Materials and Chemical Engineering "Giulio Natta" (DCMC), Politecnico di Milano, 20133 Milano, Italy.
TechRes Lab, Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milano, Italy.
Diagnostics (Basel). 2022 Mar 22;12(4):776. doi: 10.3390/diagnostics12040776.
Background: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. Methods: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. Results: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). Conclusions: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.
非侵入性床边诊断工具对于调整呼吸衰竭患者的治疗方案极为重要。使用电子鼻(ENs)进行呼出气分析有潜力为不同呼吸疾病的表型分析和改善诊断提供有用信息,但其在呼吸衰竭患者中的应用仍是一项挑战。我们开发了一种用于分析此类患者呼出气的新型测量装置。
该呼气采样装置利用医院医用空气和氧气管道系统来控制吸入氧分数,并防止呼出气体受到环境挥发性有机化合物(VOCs)的污染。其设计旨在最小化施加于患者的死腔和呼吸负荷。使用配备定制金属氧化物半导体(MOX)传感器的商用电子鼻评估呼气气味指纹。我们对33例新冠病毒(SARS-CoV-2)患者(25例呼吸衰竭患者和8例无症状患者)和22例对照进行了可行性研究,以收集关于耐受性的数据,并对敏感性和特异性进行初步评估。使用博鲁塔(Boruta)算法确定呼吸衰竭患者与对照的呼气气味指纹之间最显著的特征,然后将其应用于支持向量机(SVM)分类模型的开发。
所有患者对新型采样系统耐受性良好。支持向量机区分呼吸衰竭患者与对照的准确率为0.81(ROC曲线下面积),敏感性和特异性分别为0.920和0.682。所选特征在患有呼吸衰竭的新冠病毒患者与对照以及无症状新冠病毒患者之间存在显著差异(分别为p < 0.001和0.046)。
所开发的系统适用于收集呼吸衰竭患者的呼出气样本。我们的初步结果表明,呼气气味指纹可能是肺部疾病严重程度和病因的敏感标志物。