慢性阻塞性肺病患者心力衰竭恶化的风险预测模型

邹新亮; 景涛; 崔海艳; 江敏

doi:10.16016/j.2097-0927.202308041

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慢性阻塞性肺病患者心力衰竭恶化的风险预测模型

临床医学 | 更新时间：2026-01-09

- 慢性阻塞性肺病患者心力衰竭恶化的风险预测模型
- Risk prediction model for deterioration of heart failure in patients with chronic obstructive pulmonary disease
- 陆军军医大学学报 2024年46卷第2期页码：162-169
- 作者机构：
  
  400038 重庆，陆军军医大学(第三军医大学)第一附属医院心血管内科
- 作者简介：
  
  [ 江敏，E-mail：1532650985@qq.com JIANG Min, E-mail: 1532650985@qq.com
- 基金信息：
  
  重庆市科卫联合医学科研项目(2022ZDXM005)
- DOI：10.16016/j.2097-0927.202308041
  中图分类号： R195.1;R541.61;R563.9
- 收稿：2023-08-11，
  
  修回：2023-09-06，
  
  纸质出版：2024-01-30
- 稿件说明：
移动端阅览
邹新亮, 景涛, 崔海艳, 等. 慢性阻塞性肺病患者心力衰竭恶化的风险预测模型[J]. 陆军军医大学学报, 2024,46(2):162-169.

Xinliang ZOU, Tao JING, Haiyan CUI, et al. Risk prediction model for deterioration of heart failure in patients with chronic obstructive pulmonary disease[J]. Journal of Army Medical University, 2024, 46(2): 162-169.
邹新亮, 景涛, 崔海艳, 等. 慢性阻塞性肺病患者心力衰竭恶化的风险预测模型[J]. 陆军军医大学学报, 2024,46(2):162-169. DOI： 10.16016/j.2097-0927.202308041.

Xinliang ZOU, Tao JING, Haiyan CUI, et al. Risk prediction model for deterioration of heart failure in patients with chronic obstructive pulmonary disease[J]. Journal of Army Medical University, 2024, 46(2): 162-169. DOI： 10.16016/j.2097-0927.202308041.

摘要

目的

构建慢性阻塞性肺疾病(chronic obstructive pulmonary disease，COPD)合并心力衰竭(heart failure，HF)恶化风险的预测模型，旨在对高风险人群做出合乎逻辑的预测评估和及时系统的干预反应。

方法

回顾性收集本院2016年1月至2022年5月首次因HF住院的481例COPD患者的临床资料。使用Lasso回归进行特征变量降维，拟合Cox比例风险模型。采用C-index、校准曲线、受试者工作特征曲线和决策曲线分析、评价模型的准确性和应用价值。根据列线图评分截断值划分为高、低风险组，采用Kaplan-Meier法、Log-rank检验分析组间未发生复合终点概率的差异。

结果

观察期间发生结局事件患者279例(58.0%)，其中死亡患者16例(3.3%)。模型保留4个预测变量：利尿剂使用情况、诊断肺部感染、中度或重度贫血和左室射血分数。在0.5、1、2年时间点对应的AUC分别为0.699、0.742和0.662，校准曲线和DCA曲线显示出良好的准确性和应用价值。高、低风险组之间的生存概率差异具有统计学意义(

0.001)。

结论

本研究构建的COPD患者发生HF恶化风险预测模型预测性能良好，且在临床上合理的风险阈值范围内应用模型可能产生正收益。

Abstract

Objective

To develop a prediction model for assessing the risk of worsening heart failure (HF) in patients with chronic obstructive pulmonary disease (COPD) in order to provide logical prediction assessment and timely systematic intervention for high-risk individuals.

Methods

A retrospective study was conducted on 481 COPD patients who were hospitalized for the first time due to congestive HF in our hospital from January 2016 to May 2022. Lasso regression was utilized to reduce the dimensionality of the variables

followed by fitting the Cox proportional hazards model. The accuracy and clinical utility of the model were evaluated using C-index

calibration curve

receiver operating characteristic (ROC) curve

and decision curve analysis (DCA). Based on the cutoff value determined by the nomogram model

these patients were divided into high-risk and low-risk groups

and Kaplan-Meier analysis and Log-rank test were used to analyze the difference in the probability of composite endpoints between the 2 groups.

Results

During the observation period

279 patients (58.0%) experienced the outcome events

with 16 (3.3%) of them dead. The model retained 4 predictive variables: diuretic use

diagnosis of pulmonary infection

moderate or severe anemia

and left ventricular ejection fraction (LVEF). The AUC value of the established model for predicting at 0.5

1 and 2 years were 0.699

0.742

and 0.662

respectively

indicating good predictive accuracy. The calibration curve and DCA demonstrated the model had sound accuracy and clinical applicability. There was a significant difference in survival probability between the high-risk and low-risk groups (

0.001).

Conclusion

Our developed prediction model has good predictive performance for the probability of HF exacerbation in COPD patients. When it is used within a clinically acceptable range of risk thresholds

beneficial outcomes could be obtained.

关键词

Keywords

references

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