PDF(Pubmed)
Abstract：
UNASSIGNED: We attempted to establish a model for predicting the mortality risk of sepsis patients during hospitalization.
UNASSIGNED: Data on patients with sepsis were collected from a clinical record mining database, who were hospitalized at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2013 and August 2022. These included patients were divided into modeling and validation groups. In the modeling group, the independent risk factors of death during hospitalization were determined using univariate and multi-variate regression analyses. After stepwise regression analysis (both directions), a nomogram was drawn. The discrimination ability of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the GiViTI calibration chart assessed the model calibration. The Decline Curve Analysis (DCA) was performed to evaluate the clinical effectiveness of the prediction model. Among the validation group, the logistic regression model was compared to the models established by the SOFA scoring system, random forest method, and stacking method.
UNASSIGNED: A total of 1740 subjects were included in this study, 1218 in the modeling population and 522 in the validation population. The results revealed that serum cholinesterase, total bilirubin, respiratory failure, lactic acid, creatinine, and pro-brain natriuretic peptide were the independent risk factors of death. The AUC values in the modeling group and validation group were 0.847 and 0.826. The P values of calibration charts in the two population sets were 0.838 and 0.771. The DCA curves were above the two extreme curves. Moreover, the AUC values of the models established by the SOFA scoring system, random forest method, and stacking method in the validation group were 0.777, 0.827, and 0.832, respectively.
UNASSIGNED: The nomogram model established by combining multiple risk factors could effectively predict the mortality risk of sepsis patients during hospitalization.
UNASSIGNED: Data on patients with sepsis were collected from a clinical record mining database, who were hospitalized at the Affiliated Dongyang Hospital of Wenzhou Medical University between January 2013 and August 2022. These included patients were divided into modeling and validation groups. In the modeling group, the independent risk factors of death during hospitalization were determined using univariate and multi-variate regression analyses. After stepwise regression analysis (both directions), a nomogram was drawn. The discrimination ability of the model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and the GiViTI calibration chart assessed the model calibration. The Decline Curve Analysis (DCA) was performed to evaluate the clinical effectiveness of the prediction model. Among the validation group, the logistic regression model was compared to the models established by the SOFA scoring system, random forest method, and stacking method.
UNASSIGNED: A total of 1740 subjects were included in this study, 1218 in the modeling population and 522 in the validation population. The results revealed that serum cholinesterase, total bilirubin, respiratory failure, lactic acid, creatinine, and pro-brain natriuretic peptide were the independent risk factors of death. The AUC values in the modeling group and validation group were 0.847 and 0.826. The P values of calibration charts in the two population sets were 0.838 and 0.771. The DCA curves were above the two extreme curves. Moreover, the AUC values of the models established by the SOFA scoring system, random forest method, and stacking method in the validation group were 0.777, 0.827, and 0.832, respectively.
UNASSIGNED: The nomogram model established by combining multiple risk factors could effectively predict the mortality risk of sepsis patients during hospitalization.
摘要：
■我们试图建立一个预测脓毒症患者住院期间死亡风险的模型。
■从临床记录挖掘数据库收集脓毒症患者的数据，2013年1月至2022年8月在温州医科大学附属东阳医院住院。将这些纳入的患者分为建模组和验证组。在建模组中，使用单变量和多变量回归分析确定住院期间死亡的独立危险因素.经过逐步回归分析(两个方向)，画了一个列线图。用受试者工作特征(ROC)曲线的曲线下面积(AUC)评价模型的辨别能力,和GiViTI校准图表评估模型校准。进行下降曲线分析（DCA）以评估预测模型的临床有效性。在验证组中，将逻辑回归模型与SOFA评分系统建立的模型进行比较，随机森林方法，和堆叠方法。
■本研究共纳入1740名受试者，1218在建模群体中，522在验证群体中。结果表明，血清胆碱酯酶，总胆红素,呼吸衰竭,乳酸,肌酐,脑钠肽前体是死亡的独立危险因素。模型组和验证组的AUC值分别为0.847和0.826。两组人群中校准图的P值分别为0.838和0.771。DCA曲线高于两个极端曲线。此外，SOFA评分系统建立的模型的AUC值，随机森林方法，验证组中的堆叠法和堆叠法分别为0.777、0.827和0.832。
■结合多个危险因素建立的列线图模型可以有效预测脓毒症患者住院期间的死亡风险。
■从临床记录挖掘数据库收集脓毒症患者的数据，2013年1月至2022年8月在温州医科大学附属东阳医院住院。将这些纳入的患者分为建模组和验证组。在建模组中，使用单变量和多变量回归分析确定住院期间死亡的独立危险因素.经过逐步回归分析(两个方向)，画了一个列线图。用受试者工作特征(ROC)曲线的曲线下面积(AUC)评价模型的辨别能力,和GiViTI校准图表评估模型校准。进行下降曲线分析（DCA）以评估预测模型的临床有效性。在验证组中，将逻辑回归模型与SOFA评分系统建立的模型进行比较，随机森林方法，和堆叠方法。
■本研究共纳入1740名受试者，1218在建模群体中，522在验证群体中。结果表明，血清胆碱酯酶，总胆红素,呼吸衰竭,乳酸,肌酐,脑钠肽前体是死亡的独立危险因素。模型组和验证组的AUC值分别为0.847和0.826。两组人群中校准图的P值分别为0.838和0.771。DCA曲线高于两个极端曲线。此外，SOFA评分系统建立的模型的AUC值，随机森林方法，验证组中的堆叠法和堆叠法分别为0.777、0.827和0.832。
■结合多个危险因素建立的列线图模型可以有效预测脓毒症患者住院期间的死亡风险。
