Smoking cessation in chronic obstructive pulmonary disease
ABSTRACT Chronic obstructive pulmonary disease (COPD) is increasing in prevalence, and is predicted to become the third leading cause of deaths worldwide by 2020. The precise prevalence of COPD is not known, as many individuals with the disease are left undiagnosed, despite the requirement of only simple spirometry testing for disease detection. The major risk factor for the development of COPD is cigarette smoking, with 90% of deaths from COPD directly attributable to smoking. Therefore smoking cessation is the most effective means of halting or slowing the progress of this disease. This review summarizes and compares the differential characteristics of smokers with COPD vs. those without COPD in relation to their smoking behavior and quitting attempts, and discusses the various strategies that can be used to help patients quit and improve their likelihood of long-term smoking cessation. Of the various behavioral interventions available that can increase the likelihood of smoking cessation, one of the simplest and most effective strategies that physicians can use is simply to advise their patients to quit, particularly if this advice is combined with informing the patients of their "lung age". We also discuss the pharmacologic therapies used to enhance the likelihood of quitting, including nicotine replacement, bupropion SR and varenicline, along with novel nicotine vaccines, which are currently undergoing clinical trials.
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ABSTRACT: AimsTo test internal consistency and factor structure of a brief instrument called Trying to Quit smoking.Background The most effective treatment for patients with chronic obstructive pulmonary disease is to quit smoking. Constant thoughts about quitting and repeated quit attempts can generate destructive feelings and make it more difficult to quit.DesignDevelopment and psychometric testing of the Trying to Quit smoking scale.Methods The Trying to Quit smoking, an instrument designed to assess pressure-filled states of mind and corresponding pressure-relief strategies, was tested among 63 Swedish patients with chronic obstructive pulmonary disease. Among these, the psychometric properties of the instrument were analysed by Exploratory Factor Analyses.ResultsFourteen items were included in the factor analyses, loading on three factors labelled: (1) development of pressure-filled mental states; (2) use of destructive pressure-relief strategies; and (3) ambivalent thoughts when trying to quit smoking. These three factors accounted for more than 80% of the variance, performed well on the Kaiser-Meyer-Olkin (KMO) test and had high internal consistency.12/2014; 1(1). DOI:10.1002/nop2.4
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ABSTRACT: Background. Published reference equations predicting estimated-lung-age (ELA) didn’t reliably predict chronological-lung-age (CLA) data in North African population. Aims. To develop and to validate novel reference equations for ELA from varied anthropometric data and FEV1. Methods. Applying multiple regression analysis, equations predicting ELA were invented using data from 540 never-smokers with normal spirometry (group I). Validation was made based on data from 41 never-smokers with normal spirometry (group II). Equations were further applied for 91 subjects with confirmed COPD. Results. Novel regression equations allowing prediction of reference value of ELA and normal limits of difference between ELA and CLA were elaborated in both sexes. In males, ELA (yrs) = 42.85 - 20.74 x FEV1 (L) + 47.41 x Body Surface Area (m2) - 0.62 x Body-Mass-Index (BMI, kg/m2). In females, ELA (yrs) = 64.64 - 8.00 x FEV1 (L) - 0.17 x BMI (kg/m2) + 8.82 x Height (m). Normal limits of difference between ELA and CLA were ±16.9 yrs in males and ±14.8 yrs in females. Established equations predicted ELA of group II with no significant difference between CLA and ELA in either sex (respectively, 42.9±16.6 vs. 40.3±13.7 yrs in males, 42.0±13.5 vs. 45.6±7.7 yrs in females) ELA was significantly older than CLA age only in COPD with grades III and IV ((ELA minus CLA) (yrs) averaged, respectively, +21.7, +26.4). Conclusion. North African reference equations enrich the World Bank of reference equations from which the physician should choose according to the patient’s ethnic background.04/2014; 63(2). DOI:10.1016/j.ejcdt.2014.01.003
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ABSTRACT: Although lung age calculated backward from regression formulas constructed for FEV(1) estimation is widely used, it possesses a couple of faults. We developed novel equations predicting lung age from varied spirometric parameters (spirometry-derived lung age (SDL-age)). Applying multiple regression analysis, equations predicting SDL-age were invented using data from 8015 never-smokers with normal spirometry (group I). Validation was made based on data from 6398 never-smokers with normal spirometry (group II). Equations were further applied for 446 subjects with airflow limitation. FEV(1), FEV(1)/FVC, FEF(50), and PEF were selected as explanatory variables for reference value of SDL-age. Normal limits of difference between SDL-age and chronological-age were ± 13.4 years in the male and ± 15.0 years in the female. Established equations predicted SDL-age of group II. SDL-age was older than chronological-age only in subjects with severe airflow limitation. Novel regression equations allowing prediction of reference value of SDL-age and normal limits of difference between SDL-age and chronological-age were elaborated in both genders.Respiratory Physiology & Neurobiology 06/2012; 183(2):108-14. DOI:10.1016/j.resp.2012.06.025 · 1.97 Impact Factor