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英国一大群初级保健患者(临床实践研究数据链)中预定义哮喘表型的临床概况

Clinical profile of predefined asthma phenotypes in a large cohort of UK primary care patients (Clinical Practice Research Datalink).

作者信息

Nissen Francis, Douglas Ian J, Müllerová Hana, Pearce Neil, Bloom Chloe I, Smeeth Liam, Quint Jennifer K

机构信息

Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK,

RWD & Epidemiology, GSK R&D, Uxbridge, UK.

出版信息

J Asthma Allergy. 2019 Jan 8;12:7-19. doi: 10.2147/JAA.S182013. eCollection 2019.

Abstract

BACKGROUND

Distinct asthma phenotypes have previously been suggested, including benign asthma, atopic asthma and obese non-eosinophilic asthma. This study aims to establish if these phenotypes can be identified using data recorded in primary care clinical records and reports on patient characteristics and exacerbation frequency.

METHODS

A population-based cohort study identified 193,999 asthma patients in UK primary care from 2007 to 2017. We used linked primary and secondary care data from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics. Patients were classified into predefined phenotypes or included in an asthma "not otherwise specified" (NOS) group. We used negative binomial regression to calculate the exacerbation rates and adjusted rate ratios. Rate ratios were further stratified by asthma treatment step.

RESULTS

In our cohort, 3.9% of patients were categorized as benign asthma, 28.6% atopic asthma and 4.8% obese non-eosinophilic asthma. About 62.7% of patients were asthma NOS, including asthma NOS without treatment (10.4%), only on short-acting beta agonist (6.1%) and on maintenance treatment (46.2%). Crude severe exacerbation rates per 1,000 person-years were lowest for benign asthma (106.8 [95% CI: 101.2-112.3]) and highest for obese non-eosinophilic asthma (469.0 [451.7-486.2]). Incidence rate ratios for all phenotype groups decreased when stratified by treatment step but remained raised compared to benign asthma.

CONCLUSION

Established phenotypes can be identified in a general asthma population, although many patients did not fit into the specific phenotypes which we studied. Phenotyping patients and knowledge of asthma treatment step could help anticipate clinical course and therefore could aid clinical management but is only possible in a minority of primary care patients based on current phenotypes and electronic health records (EHRs).

摘要

背景

此前已提出不同的哮喘表型,包括良性哮喘、特应性哮喘和肥胖非嗜酸性粒细胞性哮喘。本研究旨在确定是否可以使用初级保健临床记录中记录的数据以及关于患者特征和加重频率的报告来识别这些表型。

方法

一项基于人群的队列研究在2007年至2017年期间在英国初级保健机构中识别出193,999名哮喘患者。我们使用了来自临床实践研究数据链、医院事件统计和国家统计局的关联初级和二级保健数据。患者被分类为预定义的表型或纳入哮喘“未另行规定”(NOS)组。我们使用负二项回归来计算加重率和调整后的率比。率比进一步按哮喘治疗步骤分层。

结果

在我们的队列中,3.9%的患者被归类为良性哮喘,28.6%为特应性哮喘,4.8%为肥胖非嗜酸性粒细胞性哮喘。约62.7%的患者为哮喘NOS,包括未接受治疗的哮喘NOS(10.4%)、仅使用短效β受体激动剂的患者(6.1%)和接受维持治疗的患者(46.2%)。每1000人年的粗严重加重率在良性哮喘中最低(106.8 [95% CI:101.2 - 112.3]),在肥胖非嗜酸性粒细胞性哮喘中最高(469.0 [451.7 - 486.2])。当按治疗步骤分层时,所有表型组的发病率比均下降,但与良性哮喘相比仍有所升高。

结论

在一般哮喘人群中可以识别出既定的表型,尽管许多患者并不符合我们所研究的特定表型。对患者进行表型分析以及了解哮喘治疗步骤有助于预测临床病程,因此有助于临床管理,但基于当前的表型和电子健康记录(EHRs),这仅在少数初级保健患者中可行。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/663b/6329349/2579adbf345e/jaa-12-007Fig1.jpg

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