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德国慢性阻塞性肺疾病患者未满足的需求:一项回顾性横断面研究。

Unmet needs of patients with COPD in Germany: a retrospective, cross-sectional study.

作者信息

Herth Felix J F, Vogelmeier Claus F, Trudzinski Franziska C, Watz Henrik, Skowasch Dirk, Beeh Kai-Michael, Compton Chris, Mohan Tharishini, Richter Hartmut, Claussen Jing, Bartel Sabine

机构信息

Thoraxklinik Heidelberg and Translational Lung Research Center Heidelberg, University of Heidelberg, Heidelberg, Germany.

Member of the German Center for Lung Research (DZL), Germany.

出版信息

ERJ Open Res. 2025 Jun 16;11(3). doi: 10.1183/23120541.00976-2024. eCollection 2025 May.

Abstract

BACKGROUND

Earlier diagnosis and treatment of COPD, particularly preventing exacerbations, are key to slowing disease progression and reducing mortality. This study focused on the identification of patients in Germany with unstable COPD due to suboptimal treatments.

METHODS

The IQVIA™ LRx database, capturing 80% of Statutory Health Insurance prescriptions was used to identify patients with COPD using a machine-learning model. Patients with unstable COPD were identified through high prescriptions of oral corticosteroid (OCS) and/or rescue inhalers between April 2022 and March 2023.

RESULTS

The machine-learning model identified around 2.6 million treated patients with COPD, with 77% precision. The mean age was 71 years, 48% were female and 86% were aged ≥60 years. About 14% patients (n=363k) exhibited unstable COPD due to high OCS prescriptions, while 10% patients (n=256k) had high rescue inhaler prescriptions. Among those with high OCS and high rescue inhaler prescriptions, respectively, 43% and 38% were on dual therapy, 17% and 21% were on single inhaler triple therapy, 14% and 16% were on multiple inhaler triple therapy, 11% and 9% were on monotherapy and 15% and 17% had no maintenance therapy.

CONCLUSIONS

A substantial number of unstable COPD patients were either on suboptimal maintenance therapy (monotherapy or inhaled corticosteroid-based dual therapy) or not receiving any maintenance therapy. The study highlights a substantial need in Germany for improved maintenance therapy, which could reduce disease burden, improve disease stability and reduce reliance on OCS and rescue therapies, thereby minimising side effects.

摘要

背景

慢性阻塞性肺疾病(COPD)的早期诊断和治疗,尤其是预防急性加重,是减缓疾病进展和降低死亡率的关键。本研究聚焦于识别德国因治疗欠佳而患有不稳定型COPD的患者。

方法

利用IQVIA™ LRx数据库(该数据库涵盖了80%的法定医疗保险处方),通过机器学习模型识别COPD患者。在2022年4月至2023年3月期间,通过口服糖皮质激素(OCS)和/或急救吸入器的高处方量来识别不稳定型COPD患者。

结果

机器学习模型识别出约260万接受治疗的COPD患者,精确率为77%。平均年龄为71岁,48%为女性,86%年龄≥60岁。约14%的患者(n = 36.3万)因OCS高处方量而表现为不稳定型COPD,而10%的患者(n = 25.6万)有急救吸入器高处方量。在分别有OCS高处方量和急救吸入器高处方量的患者中,43%和38%接受双联治疗,17%和21%接受单吸入器三联治疗,14%和16%接受多吸入器三联治疗,11%和9%接受单药治疗,15%和17%未接受维持治疗。

结论

大量不稳定型COPD患者要么接受的是欠佳的维持治疗(单药治疗或基于吸入性糖皮质激素的双联治疗),要么未接受任何维持治疗。该研究凸显了德国对改善维持治疗的迫切需求,这可能减轻疾病负担、提高疾病稳定性并减少对OCS和急救治疗的依赖,从而将副作用降至最低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6288/12168169/b1354cc26eb5/00976-2024.01.jpg

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