| 陈园园,鲁杏,方长太,李晓倩,查佳安.基于ATⅢ、PC、SOFA评分联合、APACHEⅡ评分构建脓毒症预后模型.[J].中南医学科学杂志.,2025,(1):145-148. |
| 基于ATⅢ、PC、SOFA评分联合、APACHEⅡ评分构建脓毒症预后模型 |
| Construction of sepsis prognosis model based on antithrombin Ⅲ, plasma protein C,SOFA score combined with acute physiology and chronic health evaluation Ⅱ score |
| 投稿时间：2024-08-31 修订日期：2024-11-30 |
| DOI：10.15972/j.cnki.43-1509/r.2025.01.036 |
| 中文关键词: 脓毒症 预后 模型 抗凝血酶Ⅲ 血浆蛋白C [ |
| 英文关键词:sepsis prognosis model plasma antithrombin Ⅲ plasma protein C |
| 基金项目:安庆市科学技术局科研技术项目(2021Z2015)；皖南医学院临床医学科研基金项目(WK2023JXYY026) |
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| 中文摘要: |
| 目的基于血浆抗凝血酶Ⅲ(ATⅢ)、血浆蛋白C(PC)、序贯器官衰竭评估量表(SOFA)、急性生理功能和慢性健康状态(APACHEⅡ)评分水平,探讨脓毒症患者预后的影响因素,构建列线图预测模型并评价其效能。 方法回顾性分析241例脓毒症患者,根据患者入ICU后28天内预后情况将其分为存活组(142例)和死亡组(99例)。回顾性收集患者入ICU当天的血小板计数(PLT)、血浆凝血酶原时间(PT)、活化部分凝血活酶时间(APTT)、纤维蛋白原(FIB)、D-二聚体(D-D)、胆红素(TBIL)、谷丙转氨酶(GPT)、谷草转氨酶(GOT)、ATⅢ活性、PC活性以及患者入ICU当天的APACHEⅡ评分和SOFA评分等指标。采用多因素Logistic回归分析评估脓毒症患者预后的影响因素,并构建列线图预测模型,采用ROC曲线、校准图和决策曲线分析(DCA)评价预测模型的效能。 结果死亡组血浆PT、APTT、D-D、TBIL、GPT、PLT、ATⅢ、PC水平与存活组比较,差异均有统计学意义(P＜0.05)。成功构建预测模型,该模型AUC为0.813,拟合性较好(P=0.08),净受益高。 结论基于ATⅢ、PC、SOFA评分联合APACHEⅡ评分构建的模型可用于早期预测脓毒症患者的预后。 |
| 英文摘要: |
| AimTo explore the influencing factors of prognosis in patients with sepsis based on plasma antithrombin Ⅲ (ATⅢ), plasma protein C (PC), sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation (APACHEⅡ) scores, as well as to construct a nomogram prediction model and evaluate its efficacy. Methods241 sepsis patients were divided into survival group (n＝142) and death group (n＝99) according to the prognosis within 28 days after admission to ICU. The platelet count (PLT), plasma prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (D-D), bilirubin (TBIL), glutamic-pyruvic transaminase (GPT), glutamic-oxaloacetic transaminase (GOT), ATⅢ activity and PC activity on the day of ICU admission were retrospectively collected, and APACHEⅡ score and SOFA score on the day of ICU admission were also recorded. Multivariate logistic regression analysis was used to evaluate the influencing factors of the prognosis of patients with sepsis, and a nomogram prediction model was constructed. ROC curve, calibration chart and decision curve analysis (DCA) were used to evaluate the efficacy of the prediction model. ResultsThe plasma levels of PT, APTT, D-D, TBIL, GPT, PLT, ATⅢ and PC in the death group were significantly different from those in the survival group (P＜0.05). The prediction model was successfully constructed, and the AUC of the model was 0.813, with good fitting (P＝0.08) and high net benefit. ConclusionThe model based on ATⅢ, PC, SOFA score combined with APACHEⅡ score can be used to predict the prognosis of patients with sepsis. |
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