Suppr超能文献

使用药物潜在类别分析确定的共病模式可独立于其他已知风险因素预测全因死亡率:慢性阻塞性肺疾病基因研究

Co-Morbidity Patterns Identified Using Latent Class Analysis of Medications Predict All-Cause Mortality Independent of Other Known Risk Factors: The COPDGene Study.

作者信息

Li Yisha, Ragland Margaret, Austin Erin, Young Kendra, Pratte Katherine, Hokanson John E, Beaty Terri H, Regan Elizabeth A, Rennard Stephen I, Wern Christina, Jacobs Michael R, Tal-Singer Ruth, Make Barry J, Kinney Gregory L

机构信息

Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA.

出版信息

Clin Epidemiol. 2020 Oct 27;12:1171-1181. doi: 10.2147/CLEP.S279075. eCollection 2020.

Abstract

PURPOSE

Medication patterns include all medications in an individual's clinical profile. We aimed to identify chronic co-morbidity treatment patterns through medication use among COPDGene participants and determine whether these patterns were associated with mortality, acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and quality of life.

MATERIALS AND METHODS

Participants analyzed here completed Phase 1 (P1) and/or Phase 2 (P2) of COPDGene. Latent class analysis (LCA) was used to identify medication patterns and assign individuals into unobserved LCA classes. Mortality, AECOPD, and the St. George's Respiratory Questionnaire (SGRQ) health status were compared in different LCA classes through survival analysis, logistic regression, and Kruskal-Wallis test, respectively.

RESULTS

LCA identified 8 medication patterns from 32 classes of chronic comorbid medications. A total of 8110 out of 10,127 participants with complete covariate information were included. Survival analysis adjusted for covariates showed, compared to a low medication use class, mortality was highest in participants with hypertension+diabetes+statin+antiplatelet medication group. Participants in hypertension+SSRI+statin medication group had the highest odds of AECOPD and the highest SGRQ score at both P1 and P2.

CONCLUSION

Medication pattern can serve as a good indicator of an individual's comorbidities profile and improves models predicting clinical outcomes.

摘要

目的

用药模式包括个体临床资料中的所有药物。我们旨在通过慢性阻塞性肺疾病基因研究(COPDGene)参与者的用药情况来确定慢性共病的治疗模式,并确定这些模式是否与死亡率、慢性阻塞性肺疾病急性加重(AECOPD)和生活质量相关。

材料与方法

此处分析的参与者完成了COPDGene的第1阶段(P1)和/或第2阶段(P2)。采用潜在类别分析(LCA)来识别用药模式,并将个体分配到未观察到的LCA类别中。通过生存分析、逻辑回归和Kruskal-Wallis检验,分别比较不同LCA类别中的死亡率、AECOPD和圣乔治呼吸问卷(SGRQ)健康状况。

结果

LCA从32类慢性共病药物中识别出8种用药模式。在10127名具有完整协变量信息的参与者中,共有8110名被纳入研究。经协变量调整的生存分析显示,与低用药量组相比,高血压+糖尿病+他汀类药物+抗血小板药物组的参与者死亡率最高。高血压+选择性5-羟色胺再摄取抑制剂(SSRI)+他汀类药物组的参与者在P1和P2时发生AECOPD的几率最高,SGRQ评分也最高。

结论

用药模式可作为个体共病情况的良好指标,并能改善预测临床结局的模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d2d2/7602898/597c4d5845f5/CLEP-12-1171-g0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验