Values of serum PCT, suPAR combined with severity scores for evaluating prognosis of septic shock patients

Author:

Wang Wenxin1,Jie Yingxin1,Zhou Jia2

Affiliation:

1. Department of Emergency , Tianjin Hospital, Tianjin University , China

2. Department of General Internal Medicine, Tianjin Hospital , Tianjin University , China

Abstract

Abstract Background: To explore the values of serum procalcitonin (PCT), soluble urokinase-type plasminogen activator receptor (suPAR) combined with APACHE II and SOFA scores for evaluating the prognosis of septic shock patients. Materials and Methods: A total of 118 eligible patients admitted from August 2017 to January 2021 were divided into survival and death groups. Serum PCT and suPAR levels were detected. APACHE II and SOFA scores were evaluated. A combination predictor pre1 was constructed. The predictive efficacy of the indicator alone or in combination was compared using receiver operating characteristic curve. Risk factors leading to death were analyzed, and a predictive model was established. Results: Serum PCT and suPAR levels as well as APACHE II and SOFA scores of death group significantly exceeded those of the survival group (P<0.05). PCT, suPAR, SOFA and APACHE II scores were valuable for predicting death. The area under curve (AUC) constructed by predictor pre1 for predicting death was largest. PCT, suPAR, APACHE II, and SOFA scores were independent risk factors for death. The model had AUC of 0.828, with the sensitivity of 86.54%, specificity of 89.03%, and accuracy of 82.47%. The death risk predicted by the model had a high concurrence with the actual one. Conclusion: PCT, suPAR, APACHE II, and SOFA scores are closely related to the prognosis of septic shock patients. The combined predictor pre1 is more effective than a single index for predicting prognosis. The combined prediction model of septic shock based on PCT, suPAR, APACHE II, and SOFA scores has higher predictive efficiency.

Publisher

Walter de Gruyter GmbH

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis;BMC Medical Informatics and Decision Making;2023-12-11

2. Correction;Revista Romana de Medicina de Laborator;2022-01-01

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