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首页> 外文期刊>Journal of breath research >Use of a least absolute shrinkage and selection operator (LASSO) model to selected ion flow tube mass spectrometry (SIFT-MS) analysis of exhaled breath to predict the efficacy of dialysis: a pilot study
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Use of a least absolute shrinkage and selection operator (LASSO) model to selected ion flow tube mass spectrometry (SIFT-MS) analysis of exhaled breath to predict the efficacy of dialysis: a pilot study

机译：使用最小绝对收缩和选择算子（LASSO）模型来选择呼出气的离子流管质谱（SIFT-MS）分析以预测透析的有效性：一项先导研究

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Selected ion flow tube-mass spectrometry (SIFT-MS) provides rapid, non-invasive measurements of a full-mass scan of volatile compounds in exhaled breath. Although various studies have suggested that breath metabolites may be indicators of human disease status, many of these studies have included few breath samples and large numbers of compounds, limiting their power to detect significant metabolites. This study employed a least absolute shrinkage and selective operator (LASSO) approach to SIFT-MS data of breath samples to preliminarily evaluate the ability of exhaled breath findings to monitor the efficacy of dialysis in hemodialysis patients. A process of model building and validation showed that blood creatinine and urea concentrations could be accurately predicted by LASSO-selected masses. Using various precursors, the LASSO models were able to predict creatinine and urea concentrations with high adjusted R-square (>80%) values. The correlation between actual concentrations and concentrations predicted by the LASSO model (using precursor H3O+) was high (Pearson correlation coefficient = 0.96). Moreover, use of full mass scan data provided a better prediction than compounds from selected ion mode. These findings warrant further investigations in larger patient cohorts. By employing a more powerful statistical approach to predict disease outcomes, breath analysis using SIFT-MS technology could be applicable in future to daily medical diagnoses.
机译：选定的离子流管质谱仪（SIFT-MS）可对呼出气中的挥发性化合物进行全质量扫描，从而快速，无创地进行测量。尽管各种研究表明呼吸代谢物可能是人类疾病状况的指标，但许多此类研究仅包括少量呼吸样品和大量化合物，从而限制了它们检测重要代谢物的能力。这项研究对呼吸样本的SIFT-MS数据采用了最小绝对收缩和选择性算子（LASSO）方法，以初步评估呼出呼吸发现以监测血液透析患者的透析功效。建立模型和验证的过程表明，通过LASSO选择的质量可以准确预测血液中的肌酐和尿素浓度。通过使用各种前体，LASSO模型能够以较高的R平方值（> 80％）预测肌酐和尿素浓度。实际浓度与LASSO模型（使用前体H3O +）预测的浓度之间的相关性很高（Pearson相关系数= 0.96）。此外，与来自选定离子模式的化合物相比，使用全质量扫描数据可提供更好的预测。这些发现需要在更大的患者队列中进行进一步的研究。通过采用更强大的统计方法来预测疾病结果，使用SIFT-MS技术进行的呼吸分析可能会在将来应用于日常医学诊断。

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