Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections
Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections
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机器学习算法识别细菌感染脓毒症患者临床和代谢组学特征的病原体特异性生物标志物
DOI：
10.1155/2020/6950576
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发表时间：
2020-07
影响因子：
--
通讯作者：
Liang Huiying
中科院分区：
文献类型：
--
作者：
Zheng Lingling;Lin Fangqin;Zhu Changxi;Liu Guangjian;Wu Xiaohui;Wu Zhiyuan;Zheng Jianbin;Xia Huimin;Cai Yi;Liang Huiying
脓毒症是一种由细菌感染引起的高死亡率疾病，但病原体在个体患者中很难诊断。由微生物活动引发的代谢组学变化为我们提供了准确识别感染的可能性。我们采用机器学习的方法来训练不同的分类器与临床代谢组学数据库从脓毒症病例中识别脓毒症的病原体。在患者到达医院后，获得了每例患者的临床指标和代谢物浓度记录。在100名明确感染的患者和相应的29名对照中使用机器学习算法，以选择特定的生物特征来区分脓毒症患者中的微生物。入院时测定临床和代谢组学特征预测诊断结果的灵敏度、特异性和AUC值。我们的分析表明，通过机器学习算法选择的生物特征可以对感染患者和革兰氏阳性与革兰氏阴性的识别具有诊断价值;相关AUC值分别为0.94 ± 0.054和0.80 ± 0.085。感染患者中临床和代谢组学生物标志物的途径和血液疾病富集分析表明，脓毒症疾病伴随着氮代谢异常、细胞呼吸障碍和肾或肠衰竭。一组选定的临床和代谢组学特征可能是区分脓毒症患者的有力生物标志物。
Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes triggered by microbial activity provide us with the possibility of accurately identifying infection. We adopted machine learning methods for training different classifiers with a clinical-metabolomic database from sepsis cases to identify the pathogen of sepsis. Records of clinical indicators and concentration of metabolites were obtained for each patient upon their arrival at the hospital. Machine learning algorithms were used in 100 patients with clear infection and corresponding 29 controls to select specific biosignatures to discriminate microorganism in septic patients. The sensitivity, specificity, and AUC value of clinical and metabolomic characteristics in predicting diagnostic outcomes were determined at admission. Our analyses demonstrate that the biosignatures selected by machine learning algorithms could have diagnostic value on the identification of infected patients and Gram-positive from Gram-negative; related AUC values were 0.94 ± 0.054 and 0.80 ± 0.085, respectively. Pathway and blood disease enrichment analyses of clinical and metabolomic biomarkers among infected patients showed that sepsis disease was accompanied by abnormal nitrogen metabolism, cell respiratory disorder, and renal or intestinal failure. The panel of selected clinical and metabolomic characteristics might be powerful biomarkers to discriminate patients with sepsis.
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DOI：
10.1093/cid/cir051
发表时间：
2011-05
期刊：
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
影响因子：
--
作者：
L. Rice
通讯作者：
L. Rice
影响因子：
3.7
作者：
Horng S;Sontag DA;Halpern Y;Jernite Y;Shapiro NI;Nathanson LA
通讯作者：
Nathanson LA
代谢组学和转录组学的整合揭示了早期巨噬细胞感染期间结核分枝杆菌的复杂饮食。
影响因子：
6.4
作者：
Zimmermann M;Kogadeeva M;Gengenbacher M;McEwen G;Mollenkopf HJ;Zamboni N;Kaufmann SHE;Sauer U
通讯作者：
Sauer U
影响因子：
3.2
作者：
Desautels T;Calvert J;Hoffman J;Jay M;Kerem Y;Shieh L;Shimabukuro D;Chettipally U;Feldman MD;Barton C;Wales DJ;Das R
通讯作者：
Das R
影响因子：
1
作者：
Q. Tan;Hangjin Jiang;Yiming Ding
通讯作者：
Q. Tan;Hangjin Jiang;Yiming Ding