Center for Drug Evaluation and Safety, University of Florida, Gainesville, FL, USA; Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada; Faculty of Pharmacy, Université de Montréal, Montréal, Québec, Canada; Department of Social and Preventive Medicine, Université de Montréal, Canada.
Respir Med. 2022 Jul;198:106866. doi: 10.1016/j.rmed.2022.106866. Epub 2022 May 9.
Tools capable of predicting the risk of asthma exacerbations can facilitate asthma management in clinical practice. However, existing tools require additional data from patients beyond electronic medical records.
To predict asthma exacerbation in an upcoming year using electronically accessible data conditional on past adherence to asthma medications.
This retrospective cohort study included patients with ≥1 hospitalization or ≥2 medical claims for asthma within 2 consecutive years between 2002 and 2015 in Quebec administrative databases. Cohort entry (CE) was defined as the date of the first asthma-related ambulatory visit on or after meeting the operational definition of asthma. Adherence to each controller medication and use of each rescue medication was measured in the year prior to CE. Elastic-net regularized logistic regression was applied.
Among 98,823 patients, the mean age was 55.9 years and 36.2% were men. The area under the curve for prediction was 0.708. In the model, the use of long-acting anticholinergic or long-acting β-agonists in the year prior to CE increased the odds of exacerbation by 24% and 21%, respectively. Among patients who received rescue medication, low and high adherence to controller medications increased the odds by 2%-5% compared with patients with medium adherence. Patients with a predicted risk of ≥0.20 were more likely to develop future exacerbation.
This risk prediction indicated that asthma-related medication use increased the risk of asthma exacerbation. A potential U-shaped relationship between adherence to controller medications and the risk of exacerbation was identified among users of rescue medications.
能够预测哮喘恶化风险的工具可以促进临床实践中的哮喘管理。然而，现有的工具需要患者提供电子病历之外的额外数据。
利用过去对哮喘药物的依从性，利用可从电子病历获取的数据预测未来一年内的哮喘恶化情况。
本回顾性队列研究纳入了 2002 年至 2015 年期间，在魁北克行政数据库中连续两年至少有 1 次因哮喘住院或至少有 2 次因哮喘就诊的患者。队列纳入（CE）定义为满足哮喘操作定义后的首次哮喘相关门诊就诊日期或之后的日期。在 CE 前 1 年测量每种控制药物的依从性和每种急救药物的使用情况。应用弹性网络正则化逻辑回归。
在 98823 例患者中，平均年龄为 55.9 岁，36.2%为男性。预测的曲线下面积为 0.708。在该模型中，CE 前一年使用长效抗胆碱能药物或长效β-激动剂会使恶化的可能性分别增加 24%和 21%。在使用急救药物的患者中，与中等依从性的患者相比，低和高依从性的控制药物会使恶化的可能性增加 2%-5%。预测风险≥0.20 的患者更有可能发生未来的恶化。
该风险预测表明，与哮喘相关的药物使用会增加哮喘恶化的风险。在使用急救药物的患者中，发现控制药物的依从性与恶化风险之间存在潜在的 U 型关系。
