Construction of risk prediction model for non-compliance with inhalation medication in COPD patients

YU Xiaojie; ZHAO Yanmin; HU Ailing; YANG Wenming; WANG Na

doi:10.6039/j.issn.1001-0408.2024.11.19

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Construction of risk prediction model for non-compliance with inhalation medication in COPD patients

更新时间：2024-06-11

- Construction of risk prediction model for non-compliance with inhalation medication in COPD patients
- ZHONGGUO YAOFANG Vol. 35, Issue 11, Pages: 1391-1395(2024)
- 作者机构：
  
  秦皇岛市第一医院药学部，河北秦皇岛　066000
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2024.11.19
  CLC： R974
- Published：15 June 2024，
  
  Received：24 October 2023，
  
  Revised：19 March 2024，
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于小杰,赵艳敏,胡爱玲等.COPD患者吸入制剂用药不依从风险预测模型构建 ^Δ[J].中国药房,2024,35(11):1391-1395.

YU Xiaojie,ZHAO Yanmin,HU Ailing,et al.Construction of risk prediction model for non-compliance with inhalation medication in COPD patients[J].ZHONGGUO YAOFANG,2024,35(11):1391-1395.
于小杰,赵艳敏,胡爱玲等.COPD患者吸入制剂用药不依从风险预测模型构建 ^Δ[J].中国药房,2024,35(11):1391-1395. DOI： 10.6039/j.issn.1001-0408.2024.11.19.

YU Xiaojie,ZHAO Yanmin,HU Ailing,et al.Construction of risk prediction model for non-compliance with inhalation medication in COPD patients[J].ZHONGGUO YAOFANG,2024,35(11):1391-1395. DOI： 10.6039/j.issn.1001-0408.2024.11.19.

摘要

目的

构建慢性阻塞性肺疾病患者吸入制剂用药不依从风险预测模型。

方法

回顾性分析秦皇岛市第一医院咳喘药学服务门诊2021年10月－2023年10月收治的365例慢性阻塞性肺疾病患者信息，将2021年10月－2023年6月收治的患者作为模型组（

＝303），2023年7－10月收治的患者作为验证组（

＝62），模型组分为依从亚组（

＝126）和不依从亚组（

＝177），通过单因素分析结合多因素Logistic回归分析患者使用吸入制剂不依从的危险因素。根据回归分析结果建立风险预测模型，并以验证组患者为对象，评价所建模型预测的准确性。

结果

多因素Logistic回归分析显示，同时使用2个吸入制剂（OR＝3.730，95%CI为1.996～6.971，

＜0.001）、一年内急性加重次数≥2次（OR＝2.509，95%CI为1.509～4.173，

＜0.001）、吸烟（OR＝2.167，95%CI为1.309～3.588，

＝0.003）、合并焦虑/抑郁（OR＝2.112，95%CI为1.257～3.499，

＝0.004）、改良版英国医学研究委员会呼吸困难问卷评级≥2级（OR＝1.701，95%CI为1.014～2.853，

＝0.044）是患者使用吸入制剂不依从的危险因素。基于此建立风险预测模型，绘制受试者工作特征曲线，可得模型组、验证组的曲线下面积分别为0.836、0.928，模型预测的总体准确率为88.71%。

结论

基于同时使用2个吸入制剂、一年内急性加重次数≥2次、吸烟、合并焦虑/抑郁、mMRC评级≥2级建立的风险预测模型对COPD患者使用吸入制剂不依从风险具有一定的预测价值。

Abstract

OBJECTIVE

To construct a risk prediction model for non-compliance with inhaled medication in patients with chronic obstructive pulmonary disease （COPD）.

METHODS

A retrospective analysis was conducted on 365 COPD patients admitted to the cough and wheeze pharmaceutical care clinic of the First Hospital of Qinhuangdao from October 2021 to October 2023. The patients admitted from October 2021 to June 2023 were selected as the model group （

＝303）， and the patients admitted from July to October 2023 were selected as the validation group （

＝62）. The model group was divided into compliance subgroup （

＝126） and non-compliance subgroup （

＝177）. Univariate analysis combined with multivariate Logistic regression analysis were used to analyze the risk factors for non-compliance with inhaled formulations in patients； the risk prediction model was established through regression analysis， and the accuracy of the model prediction was evaluated based on the validation group of patients.

RESULTS

Multivariate Logistic regression analysis showed that simultaneous use of 2 inhaled formulations （OR＝3.730， 95%CI 1.996-6.971，

＜0.001）， the number of acute exacerbations within one year ≥2 （OR＝2.509， 95%CI 1.509-4.173，

＜0.001）， smoking （OR＝2.167， 95%CI 1.309-3.588，

＝0.003）， complicated with anxiety/depression （OR＝2.112， 95%CI 1.257-3.499，

＝0.004） and mMRC grading≥2 levels （OR＝1.701， 95%CI 1.014-2.853，

＝0.044） were risk factors for non-compliance with inhaled preparations. Based on this， a risk prediction model was established and the ROC curve was drawn. The areas under the curve of the model group and validation group were 0.836 and 0.928， and the overall accuracy of the model’s prediction was 88.71%.

CONCLUSIONS

The predictive model based on the simultaneous use of 2 inhaled formulations， the number of acute exacerbations within one year ≥2， smoking， complicated with anxiety/depression， mMRC grading ≥2 levels has certain predictive value for the risk of non-compliance with inhaled formulations for COPD patients.

关键词

慢性阻塞性肺疾病吸入制剂依从性风险预测模型危险因素

Keywords

inhaled formulationscompliancerisk prediction modelrisk factors

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