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Published on: January 16, 2015
Clinical Use of an Exposure, Symptom, and Spirometry Algorithm to Stratify Smokers into COPD Risk Phenotypes: A Case
Abraham Bohadana1, Ariel Rokach1, Pascal Wild2
1Respiratory Research Unit, Pulmonary Institute, Department of Medicine, Shaare Zedek Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
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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