高级检索
    孙敏捷, 罗兵, 李振兴, 霍星星, 王云. 预测ICU住院病人死亡的Nomogram模型[J]. 蚌埠医科大学学报, 2022, 47(12): 1733-1736. DOI: 10.13898/j.cnki.issn.1000-2200.2022.12.026
    引用本文: 孙敏捷, 罗兵, 李振兴, 霍星星, 王云. 预测ICU住院病人死亡的Nomogram模型[J]. 蚌埠医科大学学报, 2022, 47(12): 1733-1736. DOI: 10.13898/j.cnki.issn.1000-2200.2022.12.026
    shu
    SUN Min-jie, LUO Bing, LI Zhen-xing, HUO Xing-xing, WANG Yun. Nomogram model for predicting death of hospitalized ICU patients[J]. Journal of Bengbu Medical University, 2022, 47(12): 1733-1736. DOI: 10.13898/j.cnki.issn.1000-2200.2022.12.026
    Citation: SUN Min-jie, LUO Bing, LI Zhen-xing, HUO Xing-xing, WANG Yun. Nomogram model for predicting death of hospitalized ICU patients[J]. Journal of Bengbu Medical University, 2022, 47(12): 1733-1736. DOI: 10.13898/j.cnki.issn.1000-2200.2022.12.026
    shu

    预测ICU住院病人死亡的Nomogram模型

    Nomogram model for predicting death of hospitalized ICU patients

      摘要
    • 摘要:
      目的构建重症监护病房（ICU）住院病人死亡Nomogram预测模型，为降低ICU住院病人死亡率提供指导。
      方法回顾性分析ICU 1 133例住院病人临床资料，采用logistic回归分析筛选ICU住院病人死亡危险因素，并构建可视化Nomogram预测模型，采用决策曲线分析（DCA）对简单评价模型和复杂评价模型进行比较。
      结果多因素logistic回归分析显示医院感染（OR=1.876，95%CI：1.037~3.043）、高血压（OR=1.133，95%CI：1.090~1.177）、糖尿病（OR=1.141，95%CI：1.064~1.209）、输血（OR=1.357，95%CI：1.102~3.421）、低GCS评分（OR=0.953，95%CI：0.917~0.991）、APACHEⅡ评分（OR=2.638，95%CI：0.794~8.692）、气管切开（OR=3.973，95%CI：2.386~6.615）、气管插管（OR=1.562，95%CI：1.163~2.266）、动静脉插管（OR=1.365，95%CI：1.067~3.172）、动静脉插管时间（OR=1.825，95%CI：1.224~2.979）、导尿管插管（OR=2.016，95%CI：1.050~3.870）、导尿管插管时间（OR=2.689，95%CI：1.724~4.195）为ICU住院病人死亡独立危险因素（P < 0.05）。根据多因素logistic回归结果建立Nomogram模型，经验证预测模型一致性良好（C-index=0.748，P < 0.05）。DCA显示阈值概率在（0.09~0.49）范围内，复杂模型的净利润高于简单模型，阈值概率在（0.49~0.87）范围内，简单模型的净利润高于复杂模型。
      结论成功建立预测ICU住院病人死亡的Nomogram预测模型。

       

      Abstract:
      ObjectiveTo establish a Nomogram prediction model for inpatient mortality in intensive care units(ICU), and to provide guidance for reducing inpatient mortality in ICU.
      MethodsClinical data of 1 133 inpatients in ICU were analyzed retrospectively.Death risk factors of inpatients in ICU were screened by logistic regression analysis.Nomogram prediction model was constructed.Decision curve analysis(DCA) was used to compare simple and complex evaluation model.
      ResultsMultivariate logistic regression analysis showed that hospital infection(OR=1.876, 95% CI: 1.037-3.043), hypertension(OR=1.133, 95%CI: 1.090-1.177), diabetes meillitus (OR=1.141, 95% CI: 1.064-1.209), blood transfusion(OR=1.357, 95%CI: 1.102-3.421), low GCS score(OR=0.953, 95%CI: 0.917-0.991), APACHE Ⅱ score(OR=2.638, 95%CI: 0.794-8.692), tracheotomy(OR=3.973, 95%CI: 2.386-6.615), endotracheal intubation(OR=1.562, 95%CI: 1.163-2.266), arteriovenous cannulation(OR=1.365, 95%CI: 1.067-3.172), days of arteriovenous cannulation(OR=1.825, 95%CI: 1.224-2.979), urinary catheter intubation(OR=2.016, 95%CI: 1.050-3.870), and days of urinary catheter intubation(OR=2.689, 95%CI: 1.724-4.195) were independent risk factors for death of hospitalized ICU patients(P < 0.05).Nomogram model was established according to multivariate logistic regression results, and it was verified that the prediction model had good consistency(C-index=0.748, P < 0.05).DCA showed that the threshold probability was within the range of(0.09-0.49), and the net benefit of the complex model was higher than that of the simple model.The threshold probability was within the range of(0.49-0.87), the net benefit of the simple model was higher than that of the complex model.
      ConclusionsThe Nomogram model is successfully established to predict the death of ICU inpatients.

       

      • HTML全文
      • 参考文献(14)
      • 相关文章
      • 施引文献
    • 资源附件(0)

    /

    返回文章
    返回