Jiang Yu Huan, Zhao Rui, Bai Yun Xue, Li Hui Ming, Liu Jun, Wang Shi Xuan, Xie Xing, Liu Yang, Chen Qiang
Department of Clinical Laboratory, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
Eur J Med Res. 2025 May 20;30(1):404. doi: 10.1186/s40001-025-02617-0.
To identify the risk factors of bacterial blood stream infection (BSI) and construct a nomogram to predict the occurrence of bacterial BSI.
Blood stream infection is characterized by a systemic infection patient with positive blood culture and has one or more clinical symptoms, such as fever (body temperature > 38 °C) or hypothermia (body temperature < 36 °C), chills, hypotension, oliguria, or high lactic acid levels. The study dataset was randomly divided into a 70% training set and a 30% validation set. Univariate logistic analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and random forest algorithms were utilized to identify the potential risk factors for BSI. Independent risk factors identified by multivariate logistic analysis were used to construct a nomogram. The discriminative ability, calibrating ability, and clinical practicality of the nomogram were evaluated using the receiver operating characteristic curve, calibration curve, and decision curve analysis.
A total of 195 bacterial BSI patients were enrolled. gender, Acute Physiology and Chronic Health Evaluation-II (APACHEII) score, nCD64 index, erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), Interleukin-6 (IL-6), lymphocyte count, T-cell count, B-cell count, NK-cell count, Interleukin-8 (IL-8), Interleukin-10 (IL-10) and Interleukin-17A(IL-17A) were independent risk factors for BSI. The nomogram model exhibited excellent discrimination with an area under the curve (AUC) of 0.836 (95% CI 0.653-0.874) in the training set and 0.871 (95% CI 0.793-0.861) in the validation set. The calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting bacterial BSI.
Overall, this study successfully identified five risk factors for BSI patients and developed a nomogram, offering individualized diagnosis and risk assessment to predict bacterial BSI in infected patients.
识别细菌性血流感染（BSI）的危险因素，并构建列线图以预测细菌性BSI的发生。
血流感染的特征是血培养阳性的全身感染患者，并有一种或多种临床症状，如发热（体温>38°C）或体温过低（体温<36°C）、寒战、低血压、少尿或高乳酸水平。研究数据集被随机分为70%的训练集和30%的验证集。采用单因素逻辑回归分析、最小绝对收缩和选择算子（LASSO）回归分析以及随机森林算法来识别BSI的潜在危险因素。通过多因素逻辑回归分析确定的独立危险因素用于构建列线图。使用受试者工作特征曲线、校准曲线和决策曲线分析来评估列线图的判别能力、校准能力和临床实用性。
共纳入195例细菌性BSI患者。性别、急性生理与慢性健康状况评分系统II（APACHEII）评分、nCD64指数、红细胞沉降率（ESR）、降钙素原（PCT）、C反应蛋白（CRP）、白细胞介素-6（IL-6）、淋巴细胞计数、T细胞计数、B细胞计数、自然杀伤细胞计数、白细胞介素-8（IL-8）、白细胞介素-10（IL-10）和白细胞介素-17A（IL-17A）是BSI的独立危险因素。列线图模型在训练集中的曲线下面积（AUC）为0.836（95%CI 0.653 - 0.874），在验证集中为0.871（95%CI 0.79 _ 0.861），显示出优异的判别能力。校准曲线表明预测模型具有令人满意的校准能力。决策曲线分析显示列线图模型在预测细菌性BSI方面具有良好的临床实用性。
总体而言，本研究成功识别了BSI患者的五个危险因素并开发了列线图，为感染患者预测细菌性BSI提供了个体化诊断和风险评估。
