Clinical Use of an Exposure, Symptom, and Spirometry Algorithm to Stratify Smokers into COPD Risk Phenotypes: A Case Finding Study Combined with Smoking Cessation Counseling.

IF 2.3 4区 医学 Q2 RESPIRATORY SYSTEM Chronic Obstructive Pulmonary Diseases-Journal of the Copd Foundation Pub Date : 2023-07-26 DOI:10.15326/jcopdf.2022.0368
Abraham Bohadana, Ariel Rokach, Pascal Wild, Ofir Kotek, Chen-Chen Shuali, Hava Azulai, Gabriel Izbicki
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Abstract

Background: Chronic obstructive pulmonary disease (COPD) case-finding aims to detect airflow obstruction in symptomatic smokers and ex-smokers. We used a clinical algorithm including smoking, symptoms, and spirometry to classify smokers into COPD risk phenotypes. In addition, we evaluated the acceptability and effectiveness of including smoking cessation advice in the case-finding intervention.

Methods: Smoking, symptoms, and spirometry abnormalities (airflow obstruction: forced expiratory volume in 1 second [FEV1] to forced vital capacity [FVC] <0.7 or preserved-ratio spirometry (FEV1<80% of predicted value and FEV1/FVC ratio ≥ 0.7)] were assessed in a group of 864 smokers aged ≥ 30 years. The combination of these parameters allowed the identification of 4 phenotypes: Phenotype A (no symptoms, normal spirometry; reference), Phenotype B (symptoms; normal spirometry; possible COPD), Phenotype C (no symptoms; abnormal spirometry; possible COPD), and Phenotype D (symptoms; abnormal spirometry; probable COPD). We assessed phenotype differences in clinical variables and modeled the trend from phenotype A to phenotype D. Smoking cessation advice based on spirometry was provided. Follow-up was done by telephone 3 months later.

Results: Using smokers without symptoms or abnormal spirometry (phenotype A; n=212 [24.5%]) as a reference, smokers were classified into possible COPD (phenotype B;n=332 [38.4%]; and C: n=81 [9.4%]) and probable COPD (phenotype D: n=239 [27.2%]). The trend from baseline phenotype A to probable COPD phenotype D was significant for the number of cigarettes/day and the number of years of smoking (p=0.0001). At follow-up, 58 (7.7%) of the respondents (n=749) reported that they had quit smoking.

Conclusions: Our clinical algorithm allowed us to classify smokers into COPD phenotypes whose manifestations were associated with smoking intensity and to significantly increase the number of smokers screened for COPD. Smoking cessation advice was well accepted, resulting in a low but clinically significant quit rate.

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临床应用暴露、症状和肺活量测定法将吸烟者分为COPD风险表型:一项结合戒烟咨询的病例发现研究
背景:慢性阻塞性肺疾病(COPD)病例调查旨在发现有症状的吸烟者和戒烟者的气流阻塞。我们使用包括吸烟、症状和肺活量测定在内的临床算法将吸烟者分为COPD风险表型。此外,我们评估了在病例发现干预中纳入戒烟建议的可接受性和有效性。方法:对864名年龄≥30岁的吸烟者进行吸烟、症状和肺活量测量异常(气流阻塞:1秒用力呼气量[FEV1]与用力肺活量[FVC] 11/FVC比值≥0.7)的评估。这些参数的组合允许鉴定4种表型:表型A(无症状,肺活量正常;B型(症状;正常的肺量测定法;可能为COPD),表型C型(无症状;不正常的肺量测定法;可能的COPD)和表型D(症状;不正常的肺量测定法;可能的COPD)。我们评估了临床变量的表现型差异,并建立了从表现型A到表现型d的趋势模型。3个月后电话随访。结果:使用无症状或肺量异常的吸烟者(表现型A;n=212[24.5%])作为参考,将吸烟者分为可能的COPD(表型B;n=332 [38.4%];C型:n=81[9.4%])和可能的COPD (D型:n=239[27.2%])。从基线表现型A到可能的COPD表现型D的趋势在每天吸烟数量和吸烟年数方面具有显著性(p=0.0001)。在随访中,58名(7.7%)受访者(n=749)报告他们已经戒烟。结论:我们的临床算法使我们能够将吸烟者分为COPD表型,其表现与吸烟强度相关,并显着增加了筛查COPD的吸烟者数量。戒烟建议被广泛接受,导致低但临床意义重大的戒烟率。
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CiteScore
3.70
自引率
8.30%
发文量
45
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