Li Xiaoming, Liu Chao, Wang Xiaoli, Mao Zhi, Yi Hongyu, Zhou Feihu
Medical School of Chinese PLA, Beijing, People's Republic of China.
Department of Critical Care Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, People's Republic of China.
Int J Gen Med. 2022 Feb 2;15:1013-1022. doi: 10.2147/IJGM.S348797. eCollection 2022.
Sepsis is a systemic inflammatory response due to infection, resulting in organ dysfunction. Timely targeted interventions can improve prognosis. Inflammation plays a crucial role in the process of sepsis. To identify potential sepsis early, we developed and validated a nomogram model and a simple risk scoring model for predicting sepsis in critically ill patients.
The medical records of adult patients admitted to our intensive care unit (ICU) from August 2017 to December 2020 were analyzed. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%). A nomogram model was developed through multivariate logistic regression analysis. The continuous variables included in nomogram model were transformed into dichotomous variables. Then, a multivariable logistic regression analysis was performed based on these dichotomous variables, and the odds ratio (OR) for each variable was used to construct a simple risk scoring model. The receiver operating characteristic curves (ROC) were constructed, and the area under the curve (AUC) was calculated.
A total of 2074 patients were enrolled. Finally, white blood cell (WBC), C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and neutrophil-to-lymphocyte ratio (NLR) were included in our models. The AUC of the nomogram model and the simple risk scoring model were 0.854 and 0.842, respectively. The prediction performance of the two models on sepsis is comparable (p = 0.1298).
This study combining five commonly available inflammatory markers (WBC, CRP, IL-6, PCT and NLR) developed a nomogram model and a simple risk scoring model to predict sepsis in critically ill patients. Although the prediction performance of the two models is comparable, the simple risk scoring model may be simpler and more practical for clinicians to identify potential sepsis in critically ill patients at an early stage and strategize treatments.
脓毒症是由感染引起的全身炎症反应，可导致器官功能障碍。及时的针对性干预可改善预后。炎症在脓毒症过程中起关键作用。为了早期识别潜在的脓毒症，我们开发并验证了一种列线图模型和一种简单的风险评分模型，用于预测重症患者的脓毒症。
分析了2017年8月至2020年12月入住我们重症监护病房（ICU）的成年患者的病历。患者被随机分为训练队列（70%）和验证队列（30%）。通过多因素逻辑回归分析建立列线图模型。将列线图模型中包含的连续变量转换为二分变量。然后，基于这些二分变量进行多因素逻辑回归分析，并使用每个变量的比值比（OR）构建简单的风险评分模型。绘制受试者工作特征曲线（ROC），并计算曲线下面积（AUC）。
共纳入2074例患者。最终，我们的模型纳入了白细胞（WBC）、C反应蛋白（CRP）、白细胞介素-6（IL-6）、降钙素原（PCT）和中性粒细胞与淋巴细胞比值（NLR）。列线图模型和简单风险评分模型的AUC分别为0.854和0.842。两种模型对脓毒症的预测性能相当（p = 0.1298）。
本研究结合五种常用的炎症标志物（WBC、CRP、IL-6、PCT和NLR）开发了一种列线图模型和一种简单的风险评分模型，以预测重症患者的脓毒症。虽然两种模型的预测性能相当，但简单风险评分模型可能更简单、更实用，便于临床医生在早期识别重症患者潜在的脓毒症并制定治疗策略。
