Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device
Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device
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DOI：
10.1007/s00216-019-02024-5
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发表时间：
2019-09-01
影响因子：
4.3
通讯作者：
Fan, Xudong
中科院分区：
文献类型：
--
作者：
Zhou, Menglian;Sharma, Ruchi;Fan, Xudong
急性呼吸窘迫综合征（ARDS）是急性肺损伤的最严重形式，造成高死亡率和长期发病率。作为一种具有多种病因的动态综合征，其及时诊断和跟踪综合征的过程是困难的。因此，对ARDS的早期、快速检测和诊断以及临床轨迹监测存在显著需求。在这里，我们报告了我们的工作，使用人类呼吸区分ARDS和非ARDS呼吸衰竭的原因。设计了一种全自动便携式二维气相色谱装置，该装置具有高峰容量（分辨率为1时> 200）、高灵敏度（亚ppb）和快速分析能力（类似于30 min），并在内部制造，用于现场分析患者的呼吸。共收集了48例ARDS患者和对照组的85个呼吸样本。97个洗脱峰在13分钟内分离和检测。基于机器学习，主成分分析（PCA）和线性判别分析（LDA）的算法开发。与医生根据柏林标准进行的裁定相比，我们的设备和算法实现了87.1%的总体准确性，阳性预测值为94.1%，阴性预测值为82.4%。整体准确率高，阳性预测值高，提示呼吸分析法能准确诊断ARDS。连续且无创地监测呼出气以用于ARDS的早期诊断、疾病轨迹跟踪和结果预测监测的能力可能对改变实践和改善患者结果具有显著影响。
Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (similar to 30 min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13 min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes.