Department of Critical Care Medicine, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China.
Eur Rev Med Pharmacol Sci. 2024 Mar;28(6):2409-2418. doi: 10.26355/eurrev_202403_35748.
This study analyzed the clinical data of 200 sepsis patients, exploring the risk factors that affect patient prognosis and providing the basis for clinically targeted intervention to improve patient prognosis.
200 septic patients were admitted to Yulin Second Hospital, and they were divided into a survival group of 151 patients and a death group of 49 patients, according to their clinical outcomes on admission. The relevant clinical parameters within 24 h of admission were collected, and the independent risk factors affecting the prognosis of septic patients were analyzed by multivariate Logistic regression. R language 4.21 software was used to construct a nomogram prediction model. The receiver operating characteristic curve was used to evaluate the discrimination of the nomogram model, and decline curve analysis was drawn to evaluate the effectiveness of the model.
In the nomogram prediction model, age, the Acute Physiology and Chronic Health Scoring System Domain (APACHE II) score, the Sequential Organ Failure Assessment (SOFA) score, C-reactive protein (CRP), total bilirubin, albumin (Alb), urea nitrogen, creatinine, and lactate (Lac) were independent risk factors for death in septic patients. The area under the receiver operating characteristic (ROC) curve for predicting the prognosis of septic patients was 0.597-1.000, and the calibration curve tends to be the ideal curve. The model had good discrimination and calibration and had high accuracy in evaluating septic patients. The modeling curves in the decline curve analysis (DCA) were all above the two extreme curves, which had good clinical value.
Nine clinical variables have been found to be independent risk factors for death in septic patients. The prediction model established based on this has good accuracy, discrimination, and consistency in predicting the prognosis of sepsis patients.
本研究分析了 200 例脓毒症患者的临床资料,探讨影响患者预后的危险因素,为临床上有针对性地进行干预,改善患者预后提供依据。
选取榆林市第二医院收治的 200 例脓毒症患者,根据患者入院时的临床转归分为生存组 151 例和死亡组 49 例。收集患者入院后 24 h 内的相关临床参数,采用多因素 Logistic 回归分析影响脓毒症患者预后的独立危险因素。采用 R 语言 4.21 软件构建列线图预测模型,采用受试者工作特征曲线(ROC 曲线)评价列线图模型的区分度,绘制下降曲线分析评价模型的效能。
在列线图预测模型中,年龄、急性生理学与慢性健康状况评分系统Ⅱ(APACHEⅡ)评分、序贯器官衰竭估计(SOFA)评分、C 反应蛋白(CRP)、总胆红素、白蛋白(Alb)、尿素氮、肌酐、乳酸(Lac)是脓毒症患者死亡的独立危险因素。预测脓毒症患者预后的受试者工作特征(ROC)曲线下面积为 0.597~1.000,校准曲线趋于理想曲线。模型具有良好的区分度和校准度,对脓毒症患者的评估准确性较高。在下降曲线分析(DCA)中,模型的构建曲线均在两条极值曲线之上,具有良好的临床价值。
本研究发现 9 个临床变量是脓毒症患者死亡的独立危险因素,基于此建立的预测模型在预测脓毒症患者预后方面具有较好的准确性、区分度和一致性。