Bomers Marije K, Menke Frederik P, Savage Richard S, Vandenbroucke-Grauls Christina M J E, van Agtmael Michiel A, Covington James A, Smulders Yvo M
Department of Internal Medicine, VU University Medical Center, Amsterdam, The Netherlands.
1] Systems Biology Centre, University of Warwick, Coventry, UK [2] Warwick Medical School, University of Warwick, Coventry, UK.
Am J Gastroenterol. 2015 Apr;110(4):588-94. doi: 10.1038/ajg.2015.90. Epub 2015 Mar 31.
A rapid test to diagnose Clostridium difficile infection (CDI) on hospital wards could minimize common but critical diagnostic delay. Field asymmetric ion mobility spectrometry (FAIMS) is a portable mass spectrometry instrument that quickly analyses the chemical composition of gaseous mixtures (e.g., above a stool sample). Can FAIMS accurately distinguish C. difficile-positive from -negative stool samples?
We analyzed 213 stool samples with FAIMS, of which 71 were C. difficile positive by microbiological analysis. The samples were divided into training, test, and validation samples. We used the training and test samples (n=135) to identify which sample characteristics discriminate between positive and negative samples, and to build machine learning algorithms interpreting these characteristics. The best performing algorithm was then prospectively validated on new, blinded validation samples (n=78). The predicted probability of CDI (as calculated by the algorithm) was compared with the microbiological test results (direct toxin test and culture).
Using a Random Forest classification algorithm, FAIMS had a high discriminatory ability on the training and test samples (C-statistic 0.91 (95% confidence interval (CI): 0.86-0.97)). When applied to the blinded validation samples, the C-statistic was 0.86 (0.75-0.97). For samples analyzed ≤7 days of collection (n=76), diagnostic accuracy was even higher (C-statistic: 0.93 (0.85-1.00)). A cutoff value of 0.32 for predicted probability corresponded with a sensitivity of 92.3% (95% CI: 77.4-98.6%) and specificity of 86.0% (78.3-89.3%). For even fresher samples, discriminatory ability further increased.
FAIMS analysis of unprocessed stool samples can differentiate between C. difficile-positive and -negative samples with high diagnostic accuracy.
在医院病房进行快速检测以诊断艰难梭菌感染（CDI），可将常见但关键的诊断延迟降至最低。场不对称离子迁移谱（FAIMS）是一种便携式质谱仪器，可快速分析气体混合物（如粪便样本上方的气体）的化学成分。FAIMS能否准确区分艰难梭菌阳性和阴性粪便样本？
我们用FAIMS分析了213份粪便样本，其中71份经微生物分析为艰难梭菌阳性。样本被分为训练样本、测试样本和验证样本。我们使用训练样本和测试样本（n = 135）来确定哪些样本特征可区分阳性和阴性样本，并构建解释这些特征的机器学习算法。然后，对新的、盲法验证样本（n = 78）进行前瞻性验证，以验证表现最佳的算法。将算法计算出的CDI预测概率与微生物检测结果（直接毒素检测和培养）进行比较。
使用随机森林分类算法，FAIMS在训练样本和测试样本上具有较高的区分能力（C统计量为0.91（95%置信区间（CI）：0.86 - 0.97））。应用于盲法验证样本时，C统计量为0.86（0.75 - 0.97）。对于采集后≤7天分析的样本（n = 76），诊断准确性更高（C统计量：0.93（0.85 - 1.00））。预测概率的截断值为0.32时，敏感性为92.3%（95%CI：77.4 - 98.6%），特异性为86.0%（78.3 - 89.3%）。对于更新鲜的样本，区分能力进一步提高。
对未处理的粪便样本进行FAIMS分析，能够以较高的诊断准确性区分艰难梭菌阳性和阴性样本。
