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电子鼻呼吸印记分析用于慢性肝病的分类和监测:一项概念验证研究。

Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study.

作者信息

De Vincentis Antonio, Pennazza Giorgio, Santonico Marco, Vespasiani-Gentilucci Umberto, Galati Giovanni, Gallo Paolo, Vernile Chiara, Pedone Claudio, Antonelli Incalzi Raffaele, Picardi Antonio

机构信息

Clinical Medicine and Hepatology Department, Campus Bio-Medico University, via Alvaro del Portillo 200, 00128 Rome, Italy.

Center for Integrated Research - CIR, Unit of Electronics for Sensor Systems, Campus Bio-Medico University, via Alvaro del Portillo 200, 00128 Rome, Italy.

出版信息

Sci Rep. 2016 May 5;6:25337. doi: 10.1038/srep25337.

Abstract

Since the liver plays a key metabolic role, volatile organic compounds in the exhaled breath might change with type and severity of chronic liver disease (CLD). In this study we analysed breath-prints (BPs) of 65 patients with liver cirrhosis (LC), 39 with non-cirrhotic CLD (NC-CLD) and 56 healthy controls by the e-nose. Distinctive BPs characterized LC, NC-CLD and healthy controls, and, among LC patients, the different Child-Pugh classes (sensitivity 86.2% and specificity 98.2% for CLD vs healthy controls, and 87.5% and 69.2% for LC vs NC-CLD). Moreover, the area under the BP profile, derived from radar-plot representation of BPs, showed an area under the ROC curve of 0.84 (95% CI 0.76-0.91) for CLD, of 0.76 (95% CI 0.66-0.85) for LC, and of 0.70 (95% CI 0.55-0.81) for decompensated LC. By applying the cut-off values of 862 and 812, LC and decompensated LC could be predicted with high accuracy (PPV 96.6% and 88.5%, respectively). These results are proof-of-concept that the e-nose could be a valid non-invasive instrument for characterizing CLD and monitoring hepatic function over time. The observed classificatory properties might be further improved by refining stage-specific breath-prints and considering the impact of comorbidities in a larger series of patients.

摘要

由于肝脏发挥着关键的代谢作用,呼出气体中的挥发性有机化合物可能会随着慢性肝病(CLD)的类型和严重程度而发生变化。在本研究中,我们通过电子鼻分析了65例肝硬化(LC)患者、39例非肝硬化慢性肝病(NC-CLD)患者和56例健康对照者的呼吸指纹(BP)。独特的BP可区分LC、NC-CLD和健康对照者,并且在LC患者中,不同的Child-Pugh分级也有区别(CLD与健康对照者相比,敏感性为86.2%,特异性为98.2%;LC与NC-CLD相比,敏感性为87.5%,特异性为69.2%)。此外,从BP的雷达图表示中得出的BP曲线下面积显示,CLD的ROC曲线下面积为0.84(95%CI 0.76-0.91),LC为0.76(95%CI 0.66-0.85),失代偿期LC为0.70(95%CI 0.55-0.81)。通过应用862和812的临界值,可以高精度地预测LC和失代偿期LC(PPV分别为96.6%和88.5%)。这些结果证明了电子鼻可能是一种有效的非侵入性工具,可用于表征CLD并随时间监测肝功能。通过完善特定阶段的呼吸指纹并考虑更多患者中合并症的影响,观察到的分类特性可能会进一步改善。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0897/4857073/63596d27ad86/srep25337-f1.jpg

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