Risk prediction of failed extubation of invasive mechanical ventilation in patients with acute exacerbation of chronic obstructive pulmonary disease
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摘要: 目的:探讨个体化预测慢性阻塞性肺疾病急性加重期(AECOPD)患者有创机械通气拔管失败的风险因素,并据此建立列线图模型预测拔管失败的发生风险。方法:选取2018年1月-2020年12月我院收治的AECOPD患者作为研究对象,收集患者的临床资料,使用单因素和多因素回归分析有创机械通气拔管失败的危险因素,并据此建立列线图风险模型。结果:共纳入159例AECOPD患者,拔管成功125例,失败34例,失败率为21.38%。高龄、Hb水平低、ALB水平低、MODS、主动咳嗽力量弱和通气时间长是AECOPD患者有创机械通气拔管失败的独立危险因素(P<0.05)。依此建立预测ICU内AECOPD患者有创机械通气拔管失败的列线图风险模型,模型验证结果显示,C-index为0.907,校正曲线趋近于理想曲线,AUC为0.916(95%CI:0.895~0.938),在2%~75%范围内,预测净获益值较高,表明该模型具有良好的预测能力。结论:年龄、Hb水平、ALB水平、MODS、主动咳嗽力量和通气时间与AECOPD患者有创机械通气拔管失败显著相关,据此构建的列线图模型能有效预测拔管失败的发生风险。
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关键词:
- 慢性阻塞性肺疾病急性加重期 /
- 有创机械通气 /
- 拔管失败 /
- 危险因素 /
- 列线图
Abstract: Objective:To explore the risk factors for the failure of extubation due to invasive mechanical ventilation in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD), and to establish an individualized predictive model.Methods:Select AECOPD patients admitted to our hospital from January 2018 to December 2020 as the research objects, collect the clinical data of the patients, analyze the risk factors of invasive mechanical ventilation extubation failure using single factor and multivariate regression, and establish a nomogram risk model based on this.Results:A total of 159 patients with AECOPD were included in this study. Extubation was successful in 125 cases and failed in 34 cases. The failure rate was 21.38%. Age, low Hb level, low ALB level, MODS, weak active cough strength and long ventilation time were independent risk factors for the failure of of extubation of AECOPD patients with invasive mechanical ventilation(P<0.05). Based on this, a nomogram risk model was established to predict the failure of AECOPD patients with invasive mechanical ventilation in the ICU. The model verification results showed that the C-index was 0.907, the calibration curve was close to the ideal curve, and the area under the ROC curve(AUC) was 0.916(95%CI:0.895-0.938). In the range of 2% to 75%, the nomogram predicts a higher net benefit value, indicating that the model has good predictive ability.Conclusion:Age, Hb level, ALB level, MODS, active cough strength, and ventilation time are significantly related to the failure of AECOPD patients with invasive mechanical ventilation. The nomogram model constructed based on this can effectively predict the risk of failure of extubation. -
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