Azurmendi Gil Leire, Krattinger-Turbatu Laura, Schweizer Juliane, Katan Mira, Sanchez Jean-Charles
Human Protein Sciences Department, University of Geneva, 1211 Geneva, Switzerland.
Department of Neurology and University of Zurich, University Hospital, 8057 Zürich, Switzerland.
Diagnostics (Basel). 2021 Jun 10;11(6):1070. doi: 10.3390/diagnostics11061070.
Accurate and early prediction of poststroke infections is important to improve antibiotic therapy guidance and/or to avoid unnecessary antibiotic treatment. We hypothesized that the combination of blood biomarkers with clinical parameters could help to optimize risk stratification during hospitalization. In this prospective observational study, blood samples of 283 ischemic stroke patients were collected at hospital admission within 72 h from symptom onset. Among the 283 included patients, 60 developed an infection during the first five days of hospitalization. Performance predictions of blood biomarkers (Serum Amyloid-A (SAA), C-reactive protein, procalcitonin (CRP), white blood cells (WBC), creatinine) and clinical parameters (National Institutes of Health Stroke Scale (NIHSS), age, temperature) for the detection of poststroke infection were evaluated individually using receiver operating characteristics curves. Three machine learning techniques were used for creating panels: Associative Rules Mining, Decision Trees and an internal iterative-threshold based method called PanelomiX. The PanelomiX algorithm showed stable performance when applied to two representative subgroups obtained as splits of the main subgroup. The panel including SAA, WBC and NIHSS had a sensitivity of 97% and a specificity of 45% to identify patients who did not develop an infection. Therefore, it could be used at hospital admission to avoid unnecessary antibiotic (AB) treatment in around half of the patients, and consequently, to reduce AB resistance.
准确且早期预测卒中后感染对于改善抗生素治疗指导和/或避免不必要的抗生素治疗至关重要。我们假设血液生物标志物与临床参数相结合有助于优化住院期间的风险分层。在这项前瞻性观察研究中，对283例缺血性卒中患者在症状发作后72小时内入院时采集血样。在纳入的283例患者中，60例在住院的前五天内发生了感染。使用受试者工作特征曲线分别评估血液生物标志物（血清淀粉样蛋白A（SAA）、C反应蛋白、降钙素原（CRP）、白细胞（WBC）、肌酐）和临床参数（美国国立卫生研究院卒中量表（NIHSS）、年龄、体温）对卒中后感染检测的性能预测。使用三种机器学习技术创建组合：关联规则挖掘、决策树和一种基于内部迭代阈值的方法PanelomiX。当将PanelomiX算法应用于作为主要亚组拆分得到的两个代表性亚组时，显示出稳定的性能。包含SAA、WBC和NIHSS的组合在识别未发生感染的患者时，敏感性为97%，特异性为45%。因此，它可在入院时用于避免约一半患者接受不必要的抗生素（AB）治疗，从而降低AB耐药性。
