Li Xiaojun, Cai Donghao, Mei Chuangchuang, Huang Xinghui
Department of Nosocomial Infection, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, 510095, People's Republic of China.
Department of Laboratory Medicine, Guangdong Provincial Second Hospital of Traditional Chinese Medicine, Guangzhou, Guangdong, 510095, People's Republic of China.
Infect Drug Resist. 2024 Nov 26;17:5247-5260. doi: 10.2147/IDR.S491537. eCollection 2024.
To develop and validate a predictive model for the risk of death in patients with () bloodstream infection (BSI) for clinical decision-making and patient management.
In this study, we included demographic and clinical data from 206 patients with BSI in China between January 2013 and December 2023. Variables were screened by least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression, and prognostic models and nomograms were constructed. The models were evaluated using the area under curve (AUC) of Receiver Operating Characteristic (ROC), decision curve analysis (DCA), and standard curves to evaluate the model.
Comorbid septic shock, an elevated neutrophil/lymphocyte ratio (NLR), low hemoglobin (HGB) levels, and low platelet counts (PLT) were found to be independent risk factors for death in patients with BSI. With the models constructed from these four variables, the AUCs of the ROC curves of the test and validation cohorts for the prognostic scenarios at 7, 14, and 28 days were not less than 0.850, and the AUCs of the ROC curves of the risk-of-death prediction model were the highest for both groups at 7 days, at 0.907 and 0.886, respectively. The two sets of calibration curves show that the calibration curves oscillate around a 45° diagonal line at 7, 14, and 28 days, and there is a good correlation between the actual risk and the predicted risk, with a high degree of calibration.The clinical decision curve shows that the model has a strong discriminatory ability when the probability is between 10% and 70%.
Septic shock status, NLR, HGB and PLT are independent risk factors for 28-day mortality in patients with BSI. These variables are conveniently and readily available, and in patients with BSI these indicators can be closely monitored in clinical practice and timely interventions can be made to improve prognosis.
建立并验证一种用于（）血流感染（BSI）患者死亡风险的预测模型，以用于临床决策和患者管理。
在本研究中，我们纳入了2013年1月至2023年12月期间中国206例（）BSI患者的人口统计学和临床数据。通过最小绝对收缩和选择算子（LASSO）回归及多变量Cox回归筛选变量，并构建预后模型和列线图。使用受试者操作特征（ROC）曲线下面积（AUC）、决策曲线分析（DCA）和标准曲线对模型进行评估。
发现合并感染性休克、中性粒细胞/淋巴细胞比值（NLR）升高、血红蛋白（HGB）水平低和血小板计数（PLT）低是（）BSI患者死亡的独立危险因素。利用由这四个变量构建的模型，测试队列和验证队列在7天、14天和28天预后情况下的ROC曲线AUC均不低于0.850，死亡风险预测模型的ROC曲线AUC在两组7天时最高，分别为0.907和0.886。两组校准曲线显示，校准曲线在7天、14天和28天时围绕45°对角线波动，实际风险与预测风险之间具有良好的相关性，校准度高。临床决策曲线显示，当概率在10%至70%之间时，该模型具有较强的鉴别能力。
感染性休克状态、NLR、HGB和PLT是（）BSI患者28天死亡率的独立危险因素。这些变量方便且易于获得，在（）BSI患者中，临床实践中可密切监测这些指标并及时进行干预以改善预后。
