Bohadana Abraham, Rokach Ariel, Wild Pascal, Kotek Ofir, Shuali Chen-Chen, Azulai Hava, Izbicki Gabriel
Respiratory Research Unit, Pulmonary Institute, Department of Medicine, Shaare Zedek Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
PW Statistical Consulting, Laxou, France.
Chronic Obstr Pulm Dis. 2023 Jul 26;10(3):248-258. doi: 10.15326/jcopdf.2022.0368.
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.
Smoking, symptoms, and spirometry abnormalities (airflow obstruction: forced expiratory volume in 1 second [FEV] to forced vital capacity [FVC] <0.7 or preserved-ratio spirometry (FEV<80% of predicted value and FEV/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.
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 (=0.0001). At follow-up, 58 (7.7%) of the respondents (n=749) reported that they had quit smoking.
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.
慢性阻塞性肺疾病（COPD）病例发现旨在检测有症状的吸烟者和已戒烟者中的气流阻塞情况。我们使用了一种包括吸烟情况、症状和肺功能测定的临床算法，将吸烟者分类为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。
对一组864名年龄≥30岁的吸烟者评估吸烟情况、症状和肺功能测定异常（气流阻塞：第1秒用力呼气容积[FEV]与用力肺活量[FVC]之比<0.7或肺功能比值正常[FEV<预测值的80%且FEV/FVC比值≥0.7]）。这些参数的组合可识别出4种表型：表型A（无症状，肺功能正常；参照）、表型B（有症状；肺功能正常；可能为COPD）、表型C（无症状；肺功能异常；可能为COPD）和表型D（有症状；肺功能异常；很可能为COPD）。我们评估了临床变量中的表型差异，并模拟了从表型A到表型D的趋势。提供了基于肺功能测定的戒烟建议。3个月后通过电话进行随访。
以无症状或肺功能正常的吸烟者（表型A；n = 212[24.5%]）为参照，吸烟者被分类为可能患有COPD（表型B；n = 332[38.4%]；以及表型C：n = 81[9.4%]）和很可能患有COPD（表型D：n = 239[27.2%]）。从基线表型A到很可能患有COPD的表型D，每天吸烟支数和吸烟年数的变化趋势具有显著性（P = 0.0001）。在随访时，749名受访者中有58名（7.7%）报告称已戒烟。
我们的临床算法使我们能够将吸烟者分类为COPD表型，其表现与吸烟强度相关，并显著增加了接受COPD筛查的吸烟者数量。戒烟建议得到了很好的接受，戒烟率虽低但具有临床意义。
