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使用观察到的肺功能结果预测峰值吸气流量的生理指标(POROS):慢性阻塞性肺疾病急性加重住院患者出院时的评估

Physiological predictors Of peak inspiRatory flow using Observed lung function resultS (POROS): evaluation at discharge among patients hospitalized for a COPD exacerbation.

作者信息

Price David B, Yang Sen, Ming Simon Wan Yau, Hardjojo Antony, Cabrera Claudia, Papaioannou Andriana I, Loukides Stelios, Kritikos Vicky, Bosnic-Anticevich Sinthia Z, Carter Victoria, Dorinsky Paul M

机构信息

Observational and Pragmatic Research Institute Pte Ltd, Singapore, Singapore,

Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK,

出版信息

Int J Chron Obstruct Pulmon Dis. 2018 Dec 13;13:3937-3946. doi: 10.2147/COPD.S174371. eCollection 2018.

Abstract

BACKGROUND

Peak inspiratory flow (PIF) as generated through the resistance of a dry powder inhaler (DPI) device is a critical patient-dependent maneuver impacting the success of DPI medication delivery. Despite its importance, it is not routinely measured in clinical practice. Little is currently known about the relationship, if any, between PIF through DPI devices, routine spirometry and disease outcomes.

AIM

The aim of this study was to identify potential predictors of PIF for different DPIs from spirometric parameters and patient characteristics and explore the association between PIF and follow-up events.

PATIENTS AND METHODS

A retrospective observational study at discharge among patients hospitalized for a COPD exacerbation at Attikon hospital, Athens, Greece. Spirometry was performed using an Easy on-PC™ spirometer. PIF was measured through four DPI resistances using the In-Check™ DIAL. Regression analyses were used to investigate the association between PIF through resistances and spirometric parameters obtained at discharge, comorbidities and demographic parameters.

RESULTS

Forty-seven COPD patients (mean [±SD], age 71 [±9] years, 72% males, 51% current smokers) were included in this study. Overall, 85% and 15% were classified as GOLD (2017) groups D and C, respectively. Most prevalent comorbidities were hypertension (70%) and cardiovascular disease (53%). In the final regression model, higher PIF was significantly associated with the following: higher FEV and % predicted peak expiratory flow (PEF) for Turbohaler (-squared value 0.374); higher FEV and diagnosis of gastroesophageal reflux disease (GERD) for Aerolizer (-squared value 0.209) and higher FEV, younger age and diagnosis of ischemic heart disease (IHD) for Diskus (-squared value 0.350). However, -squared values for all three devices were weak (<0.4).

CONCLUSION

The study did not provide evidence to support the use of surrogate measurements for PIF through device resistance, which could assist in determining the appropriateness of inhaler device type. Although PIF measurement is feasible in patients at discharge and could be a valuable addition to the standard of care in COPD management, it needs to be measured directly.

摘要

背景

通过干粉吸入器(DPI)装置阻力产生的吸气峰流速(PIF)是一项关键的依赖患者的操作,会影响DPI药物递送的成功率。尽管其很重要,但在临床实践中并未常规测量。目前对于通过DPI装置的PIF、常规肺功能测定与疾病转归之间的关系(如果存在的话)知之甚少。

目的

本研究的目的是从肺功能参数和患者特征中识别不同DPI的PIF潜在预测因素,并探讨PIF与随访事件之间的关联。

患者与方法

在希腊雅典阿提卡医院对因慢性阻塞性肺疾病(COPD)加重而住院的患者出院时进行一项回顾性观察研究。使用Easy on-PC™肺功能仪进行肺功能测定。使用In-Check™ DIAL通过四种DPI阻力测量PIF。采用回归分析研究通过阻力的PIF与出院时获得的肺功能参数、合并症和人口统计学参数之间的关联。

结果

本研究纳入了47例COPD患者(平均[±标准差],年龄71[±9]岁,72%为男性,51%为当前吸烟者)。总体而言,85%和15%分别被归类为GOLD(2017)D组和C组。最常见的合并症是高血压(70%)和心血管疾病(53%)。在最终回归模型中,较高的PIF与以下因素显著相关:对于都保,较高的第1秒用力呼气容积(FEV)和预测的呼气峰流速(PEF)百分比(决定系数值0.374);对于准纳器,较高的FEV和胃食管反流病(GERD)诊断(决定系数值0.209);对于思力华,较高的FEV、较年轻的年龄和缺血性心脏病(IHD)诊断(决定系数值0.350)。然而,所有三种装置的决定系数值都较弱(<0.4)。

结论

该研究未提供证据支持通过装置阻力对PIF进行替代测量以帮助确定吸入器装置类型的适用性。尽管PIF测量在出院患者中是可行的,并且可能是COPD管理标准护理中的一项有价值的补充,但仍需要直接测量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7223/6296178/61be989c3249/copd-13-3937Fig1.jpg

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