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单增卓嘎, 赵志峰, 陈茂. SCAI心源性休克分类法对CICU心源性休克患者死亡风险的预测价值[J]. 四川大学学报(医学版), 2021, 52(3): 503-509. DOI: 10.12182/20210560104
引用本文: 单增卓嘎, 赵志峰, 陈茂. SCAI心源性休克分类法对CICU心源性休克患者死亡风险的预测价值[J]. 四川大学学报(医学版), 2021, 52(3): 503-509. DOI: 10.12182/20210560104
DANZENGZHUOGA, ZHAO Zhi-feng, CHEN Mao. The Value of Using SCAI Cardiogenic Shock Stages in Predicting Mortality in CICU Patients[J]. Journal of Sichuan University (Medical Sciences), 2021, 52(3): 503-509. DOI: 10.12182/20210560104
Citation: DANZENGZHUOGA, ZHAO Zhi-feng, CHEN Mao. The Value of Using SCAI Cardiogenic Shock Stages in Predicting Mortality in CICU Patients[J]. Journal of Sichuan University (Medical Sciences), 2021, 52(3): 503-509. DOI: 10.12182/20210560104

SCAI心源性休克分类法对CICU心源性休克患者死亡风险的预测价值

The Value of Using SCAI Cardiogenic Shock Stages in Predicting Mortality in CICU Patients

  • 摘要:
      目的  探讨采用美国心血管造影和介入学会(Society of Cardiovascular Imaging and Intervention,SCAI)心源性休克分类法预测心脏重症监护病房(cardiac intensive care unit,CICU)心源性休克(cardiogenic shock,CS)患者死亡率的价值。
      方法  回顾性收集2011年1月−2018年1月四川大学华西医院CICU的连续住院患者资料,并对CS患者进行分析。根据SCAI心源性休克分类法将患者分为C组、D组和E组,主要结局指标为院内死亡率。采用logistic回归确定SCAI 分期与院内死亡率之间的关系,并进行多变量校正。受试者操作特征曲线用于评价SCAI心源性休克分类法对院内死亡率的预测价值。
      结果  最终对符合纳入标准的839例CS患者进行研究。SCAI分期C期(经典期)、D期(恶化期)、E期(终末期)各组占比分别为43.3%(363例)、38.7%(325例)和18.0%(151例)。未校正院内死亡率分别为22.9%(83例)、44.0%(143例)、53.6%(81例)(P<0.001);SCAI分期预测CICU心源性休克患者院内死亡的曲线下面积(area under the curve,AUC)为0.640,进行多变量校正后,AUC提高到0.776(P<0.001)。急性冠脉综合征患者中,全球冠脉事件登记研究评分模型(Global Registry of Acute Coronary Events,GRACE)预测院内死亡率AUC为0.644,当SCAI 分期联合GRACE评分,AUC提高到0.702(P<0.001)。
      结论  CICU心源性休克患者中,SCAI心源性休克分类法可以作为入院时快速评价疾病风险的分层方法。急性冠脉综合征合并CS的患者中,SCAI分期联合GRACE评分可提高对死亡风险的预测能力。

     

    Abstract:
      Objective  To study the value of using the cardiogenic shock (CS) stages developed by the Society of Cardiovascular Imaging and Intervention (SCAI) in predicting the mortality of CS patients in cardiac intensive care unit (CICU).
      Methods  We retrospectively collected (Jan., 2011−Jan., 2018) the information of inpatients who were admitted to the CICU of West China Hospital of Sichuan University on consecutive days, and conducted analysis on those with CS. The patients were divided into groups C, D and E, according to the corresponding SCAI stages, and the primary outcome indicator was in-hospital mortality. Logistic regression was done to determine the association between SCAI staging and in-hospital mortality before and after multivariate adjustment. The receiver operating characteristic curve was used to assess the value of SCAI stages of CS in predicting in-hospital mortality.
      Results  We studies 839 CS patients who met our inclusion criteria. The proportions of patients of SCAI stages C (Classic), D (Deteriorating), and E (Extremis) were 43.3% (363 cases), 38.7% (325 cases) and 18.0% (151 cases), respectively. The unadjusted in-hospital mortality rates were 22.9% (83 cases), 44.0% (143 cases) and 53.6% (81 cases), respectively (P<0.001). The SCAI stages had an AUC (area under the curve) of 0.640 for predicting in-hospital mortality among CS patients in CICU. After multivariate adjustment, the AUC increased to 0.776 (P<0.001). In patients with acute coronary syndrome, the Global Registry of Acute Coronary Events (GRACE) scores had an AUC of 0.644 for predicting in-hospital mortality, while a combination of the GRACE score with SCAI staging yielded an increased AUC of 0.702 (P<0.001).
      Conclusion  In CICU patients with CS, the SCAI stages of CS can be used as a stratified method for rapid assessment of disease risks upon admission. In patients with acute coronary syndrome and CS, SCAI stages combined with GRACE scores improved the ability to predict risks of death.

     

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