Long-term smoking status of cardiac patients following symptom-specific cessation advice

Biomedical risk assessment as an aid for smoking cessation

BACKGROUND

A possible strategy for increasing smoking cessation rates could be to provide smokers with feedback on the current or potential future biomedical effects of smoking using, for example, measurement of exhaled carbon monoxide (CO), lung function, or genetic susceptibility to lung cancer or other diseases.

OBJECTIVES

The main objective was to determine the efficacy of providing smokers with feedback on their exhaled CO measurement, spirometry results, atherosclerotic plaque imaging, and genetic susceptibility to smoking-related diseases in helping them to quit smoking.

SEARCH METHODS

For the most recent update, we searched the Cochrane Tobacco Addiction Group Specialized Register in March 2018 and ClinicalTrials.gov and the WHO ICTRP in September 2018 for studies added since the last update in 2012.

SELECTION CRITERIA

Inclusion criteria for the review were: a randomised controlled trial design; participants being current smokers; interventions based on a biomedical test to increase smoking cessation rates; control groups receiving all other components of intervention; and an outcome of smoking cessation rate at least six months after the start of the intervention.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. We expressed results as a risk ratio (RR) for smoking cessation with 95% confidence intervals (CI). Where appropriate, we pooled studies using a Mantel-Haenszel random-effects method.

MAIN RESULTS

We included 20 trials using a variety of biomedical tests interventions; one trial included two interventions, for a total of 21 interventions. We included a total of 9262 participants, all of whom were adult smokers. All studies included both men and women adult smokers at different stages of change and motivation for smoking cessation. We judged all but three studies to be at high or unclear risk of bias in at least one domain. We pooled trials in three categories according to the type of biofeedback provided: feedback on risk exposure (five studies); feedback on smoking-related disease risk (five studies); and feedback on smoking-related harm (11 studies). There was no evidence of increased cessation rates from feedback on risk exposure, consisting mainly of feedback on CO measurement, in five pooled trials (RR 1.00, 95% CI 0.83 to 1.21; I² = 0%; n = 2368). Feedback on smoking-related disease risk, including four studies testing feedback on genetic markers for cancer risk and one study with feedback on genetic markers for risk of Crohn's disease, did not show a benefit in smoking cessation (RR 0.80, 95% CI 0.63 to 1.01; I² = 0%; n = 2064). Feedback on smoking-related harm, including nine studies testing spirometry with or without feedback on lung age and two studies on feedback on carotid ultrasound, also did not show a benefit (RR 1.26, 95% CI 0.99 to 1.61; I² = 34%; n = 3314). Only one study directly compared multiple forms of measurement with a single form of measurement, and did not detect a significant difference in effect between measurement of CO plus genetic susceptibility to lung cancer and measurement of CO only (RR 0.82, 95% CI 0.43 to 1.56; n = 189).

AUTHORS' CONCLUSIONS

There is little evidence about the effects of biomedical risk assessment as an aid for smoking cessation. The most promising results relate to spirometry and carotid ultrasound, where moderate-certainty evidence, limited by imprecision and risk of bias, did not detect a statistically significant benefit, but confidence intervals very narrowly missed one, and the point estimate favoured the intervention. A sensitivity analysis removing those studies at high risk of bias did detect a benefit. Moderate-certainty evidence limited by risk of bias did not detect an effect of feedback on smoking exposure by CO monitoring. Low-certainty evidence, limited by risk of bias and imprecision, did not detect a benefit from feedback on smoking-related risk by genetic marker testing. There is insufficient evidence with which to evaluate the hypothesis that multiple types of assessment are more effective than single forms of assessment.

Life Years Gained From Smoking-Cessation Counseling After Myocardial Infarction

INTRODUCTION

Hospitalization for acute myocardial infarction (AMI) is an opportune time to counsel smokers to quit. Studies have demonstrated lower short-term mortality for counseled versus non-counseled smokers; yet, little is known about the long-term survival benefits of post-AMI smoking-cessation counseling (SCC).

METHODS

Data from the Cooperative Cardiovascular Project, a prospective cohort study of elderly patients with AMI between 1994 and 1996 with >17 years of follow-up, were used to evaluate the association of SCC with short- and long-term mortality in smokers with AMI. Life expectancy and years of potential life gained were used to quantify the long-term survival benefits of SCC. Cox proportional hazards models with exponential extrapolation were used to estimate life expectancy.

RESULTS

The analysis included 13,815 smokers, of whom 5,695 (41.2%) received SCC. Non-counseled smokers had higher crude mortality than counseled smokers over all 17 years of follow-up. After adjustment for patient and hospital characteristics, SCC was associated with a 22.6% lower 30-day mortality and a 7.5% lower mortality over 17 years. These survival differences produced higher life expectancy estimates for counseled smokers than non-counseled smokers at all ages, which resulted in average gains in life years of 0.13 (95% CI=-0.31, 0.56) to 0.58 (95% CI=0.25, 0.91) years, with the largest gains observed in older smokers.

CONCLUSIONS

SCC is associated with longer life expectancy and gains in life years in elderly smokers with AMI, supporting the importance of post-AMI counseling efforts.

Biomedical risk assessment as an aid for smoking cessation

BACKGROUND

A possible strategy for increasing smoking cessation rates could be to provide smokers who have contact with healthcare systems with feedback on the biomedical or potential future effects of smoking, e.g. measurement of exhaled carbon monoxide (CO), lung function, or genetic susceptibility to lung cancer.

OBJECTIVES

To determine the efficacy of biomedical risk assessment provided in addition to various levels of counselling, as a contributing aid to smoking cessation.

SEARCH METHODS

For the most recent update, we searched the Cochrane Collaboration Tobacco Addiction Group Specialized Register in July 2012 for studies added since the last update in 2009.

SELECTION CRITERIA

Inclusion criteria were: a randomized controlled trial design; subjects participating in smoking cessation interventions; interventions based on a biomedical test to increase motivation to quit; control groups receiving all other components of intervention; an outcome of smoking cessation rate at least six months after the start of the intervention.

DATA COLLECTION AND ANALYSIS

Two assessors independently conducted data extraction on each paper, with disagreements resolved by consensus. Results were expressed as a relative risk (RR) for smoking cessation with 95% confidence intervals (CI). Where appropriate, a pooled effect was estimated using a Mantel-Haenszel fixed-effect method.

MAIN RESULTS

We included 15 trials using a variety of biomedical tests. Two pairs of trials had sufficiently similar recruitment, setting and interventions to calculate a pooled effect; there was no evidence that carbon monoxide (CO) measurement in primary care (RR 1.06, 95% CI 0.85 to 1.32) or spirometry in primary care (RR 1.18, 95% CI 0.77 to 1.81) increased cessation rates. We did not pool the other 11 trials due to the presence of substantial clinical heterogeneity. Of the remaining 11 trials, two trials detected statistically significant benefits: one trial in primary care detected a significant benefit of lung age feedback after spirometry (RR 2.12, 95% CI 1.24 to 3.62) and one trial that used ultrasonography of carotid and femoral arteries and photographs of plaques detected a benefit (RR 2.77, 95% CI 1.04 to 7.41) but enrolled a population of light smokers and was judged to be at unclear risk of bias in two domains. Nine further trials did not detect significant effects. One of these tested CO feedback alone and CO combined with genetic susceptibility as two different interventions; none of the three possible comparisons detected significant effects. One trial used CO measurement, one used ultrasonography of carotid arteries and two tested for genetic markers. The four remaining trials used a combination of CO and spirometry feedback in different settings.

AUTHORS' CONCLUSIONS

There is little evidence about the effects of most types of biomedical tests for risk assessment on smoking cessation. Of the fifteen included studies, only two detected a significant effect of the intervention. Spirometry combined with an interpretation of the results in terms of 'lung age' had a significant effect in a single good quality trial but the evidence is not optimal. A trial of carotid plaque screening using ultrasound also detected a significant effect, but a second larger study of a similar feedback mechanism did not detect evidence of an effect. Only two pairs of studies were similar enough in terms of recruitment, setting, and intervention to allow meta-analyses; neither of these found evidence of an effect. Mixed quality evidence does not support the hypothesis that other types of biomedical risk assessment increase smoking cessation in comparison to standard treatment. There is insufficient evidence with which to evaluate the hypothesis that multiple types of assessment are more effective than single forms of assessment.

Biomedical risk assessment as an aid for smoking cessation

BACKGROUND

A possible strategy for increasing smoking cessation rates could be to provide smokers who have contact with healthcare systems with feedback on the biomedical or potential future effects of smoking, e.g. measurement of exhaled carbon monoxide (CO), lung function, or genetic susceptibility to lung cancer.

OBJECTIVES

To determine the efficacy of biomedical risk assessment provided in addition to various levels of counselling, as a contributing aid to smoking cessation.

SEARCH STRATEGY

We systematically searched the Cochrane Collaboration Tobacco Addiction Group Specialized Register, Cochrane Central Register of Controlled Trials 2008 Issue 4, MEDLINE (1966 to January 2009), and EMBASE (1980 to January 2009). We combined methodological terms with terms related to smoking cessation counselling and biomedical measurements.

SELECTION CRITERIA

Inclusion criteria were: a randomized controlled trial design; subjects participating in smoking cessation interventions; interventions based on a biomedical test to increase motivation to quit; control groups receiving all other components of intervention; an outcome of smoking cessation rate at least six months after the start of the intervention.

DATA COLLECTION AND ANALYSIS

Two assessors independently conducted data extraction on each paper, with disagreements resolved by consensus. Results were expressed as a relative risk (RR) for smoking cessation with 95% confidence intervals (CI). Where appropriate a pooled effect was estimated using a Mantel-Haenszel fixed effect method.

MAIN RESULTS

We included eleven trials using a variety of biomedical tests. Two pairs of trials had sufficiently similar recruitment, setting and interventions to calculate a pooled effect; there was no evidence that CO measurement in primary care (RR 1.06, 95% CI 0.85 to 1.32) or spirometry in primary care (RR 1.18, 95% CI 0.77 to 1.81) increased cessation rates. We did not pool the other seven trials. One trial in primary care detected a significant benefit of lung age feedback after spirometry (RR 2.12; 95% CI 1.24 to 3.62). One trial that used ultrasonography of carotid and femoral arteries and photographs of plaques detected a benefit (RR 2.77; 95% CI 1.04 to 7.41) but enrolled a population of light smokers. Five trials failed to detect evidence of a significant effect. One of these tested CO feedback alone and CO + genetic susceptibility as two different intervention; none of the three possible comparisons detected significant effects. Three others used a combination of CO and spirometry feedback in different settings, and one tested for a genetic marker.

AUTHORS' CONCLUSIONS

There is little evidence about the effects of most types of biomedical tests for risk assessment. Spirometry combined with an interpretation of the results in terms of 'lung age' had a significant effect in a single good quality trial. Mixed quality evidence does not support the hypothesis that other types of biomedical risk assessment increase smoking cessation in comparison to standard treatment. Only two pairs of studies were similar enough in term of recruitment, setting, and intervention to allow meta-analysis.

Effectiveness of biomedical risk assessment as an aid for smoking cessation: a systematic review

OBJECTIVE

To determine the efficacy of biomedical risk assessment (eg, exhaled carbon monoxide (CO), or genetic susceptibility to lung cancer) as an aid for smoking cessation.

DATA SOURCES

Cochrane Tobacco Addiction Group Specialized Register, Cochrane Central Register of Controlled Trials, Medline (1966-2004) and EMBASE (1980-2004).

STUDY SELECTION

Randomised controlled smoking cessation interventions using biomedical tests with at least 6 months follow-up.

DATA EXTRACTION

Two reviewers independently screened all search results (titles and abstracts) for possible inclusion. Each reviewer then extracted data from the selected studies, and assessed their methodological quality based on the CONSORT (Consolidated Standards of Reporting Trials) statement criteria.

DATA SYNTHESIS

Of 4049 retrieved references, eight trials were retained for data extraction and analysis. Three trials isolated the effect of exhaled CO on smoking cessation rates resulting in the following ORs and 95% CIs: 0.73 (0.38 to 1.39), 0.93 (0.62 to 1.41) and 1.18 (0.84 to 1.64). Measurement of exhaled CO and spirometry were used together in three trials, resulting in the following ORs (95% CI): 0.60 (0.25 to 1.46), 2.45 (0.73 to 8.25) and 3.50 (0.88 to 13.92). Spirometry results alone were used in one other trial with an OR (95% CI) of 1.21 (0.60 to 2.42). Ultrasonography of carotid and femoral arteries performed on light smokers gave an OR (95% CI) of 3.15 (1.06 to 9.31).

CONCLUSIONS

Scarcity and limited quality of the current evidence does not support the hypothesis that biomedical risk assessment increases smoking cessation as compared with the standard treatment.

Biomedical risk assessment as an aid for smoking cessation

BACKGROUND

A possible strategy for increasing smoking cessation rates could be to provide smokers who have contact with healthcare systems with feedback on the biomedical or potential future effects of smoking, e.g. measurement of exhaled carbon monoxide (CO), lung function, or genetic susceptibility to lung cancer. We reviewed systematically data on smoking cessation rates from controlled trials that used biomedical risk assessment and feedback.

OBJECTIVES

To determine the efficacy of biomedical risk assessment provided in addition to various levels of counselling, as a contributing aid to smoking cessation.

SEARCH STRATEGY

We systematically searched he Cochrane Collaboration Tobacco Addiction Group Specialized Register, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (1966 to 2004), and EMBASE (1980 to 2004). We combined methodological terms with terms related to smoking cessation counselling and biomedical measurements.

SELECTION CRITERIA

Inclusion criteria were: a randomized controlled trial design; subjects participating in smoking cessation interventions; interventions based on a biomedical test to increase motivation to quit; control groups receiving all other components of intervention; an outcome of smoking cessation rate at least six months after the start of the intervention.

DATA COLLECTION AND ANALYSIS

Two assessors independently conducted data extraction on each paper, with disagreements resolved by consensus.

MAIN RESULTS

From 4049 retrieved references, we selected 170 for full text assessment. We retained eight trials for data extraction and analysis. One of the eight used CO alone and CO + Genetic Susceptibility as two different intervention groups, giving rise to three possible comparisons. Three of the trials isolated the effect of exhaled CO on smoking cessation rates resulting in the following odds ratios (ORs) and 95% confidence intervals (95% CI): 0.73 (0.38 to 1.39), 0.93 (0.62 to 1.41), and 1.18 (0.84 to 1.64). Combining CO measurement with genetic susceptibility gave an OR of 0.58 (0.29 to 1.19). Exhaled CO measurement and spirometry were used together in three trials, resulting in the following ORs (95% CI): 0.6 (0.25 to 1.46), 2.45 (0.73 to 8.25), and 3.50 (0.88 to 13.92). Spirometry results alone were used in one other trial with an OR of 1.21 (0.60 to 2.42). Two trials used other motivational feedback measures, with an OR of 0.80 (0.39 to 1.65) for genetic susceptibility to lung cancer alone, and 3.15 (1.06 to 9.31) for ultrasonography of carotid and femoral arteries performed in light smokers (average 10 to 12 cigarettes a day).

AUTHORS' CONCLUSIONS

Due to the scarcity of evidence of sufficient quality, we can make no definitive statements about the effectiveness of biomedical risk assessment as an aid for smoking cessation. Current evidence of lower quality does not however support the hypothesis that biomedical risk assessment increases smoking cessation in comparison with standard treatment. Only two studies were similar enough in term of recruitment, setting, and intervention to allow pooling of data and meta-analysis.

Coronary heart disease multiple risk factor reduction. Providers' perspectives

BACKGROUND

Although primary care physicians understand the importance of preventive services for patients with multiple risk factors (MRF) for coronary heart disease, physician intervention is limited. This study investigated (1) physicians' views of challenges faced in managing patients with MRF; (2) the counseling and management methods they utilize; and (3) possible strategies to enhance MRF intervention in the primary care setting.

METHODS

Two focus groups were conducted with primary care physicians from varying settings to gain insight into these issues noted above. Each group was co-facilitated by a physician and a behavioral scientist using a previously developed semistructured interview guide. The group discussions were tape recorded and subsequently transcribed. Transcripts were analyzed using the constant comparative method for analysis.

RESULTS

Physicians are challenged by knowledge limitations (contribution of individual risk factors to overall risk); limited support (guidelines, materials, and staff); and logistic difficulties (organizational issues, time limitations). Their approach to MRF management tends to be highly individualized with an initial preference for lifestyle change interventions rather than prescription of medications with some qualifying circumstances. Physicians favored a serial rather than a parallel approach to MRF intervention, starting with behaviors that the patient perceives as a priority. Proposed solutions to current challenges emphasize physician education and the development of innovative approaches that include physician assistance and a team approach.

CONCLUSIONS

Physicians are aware of and sensitive to the complexity of MRF management for their patients and themselves. However, future MRF interventions will require nonphysician staff involvement and increased systems support.

Smoking cessation interventions for in-patients: a selective review with recommendations for hospital-based health professionals

A selective review of the literature was conducted to provide evidence-based recommendations for the clinical management of hospitalized smokers. The Cochrane library, in particular the Cochrane review of 'Interventions for smoking cessation in hospitalised patients', was the basis for the review and was supplemented with other clinical and non-clinical literature where the review did not inform clinicians sufficiently. Evidence was reviewed on issues considered by the authors to be of importance to health professionals interested in providing a smoking cessation intervention to their patients. The review suggests that effective hospital interventions: incorporate an in-patient intervention lasting greater than 20 minutes in duration with extended post discharge follow-up; consist of at least five intervention contacts; and be delivered over at least a 3-month period. Furthermore, interventions should include in-patient advice and counselling, the provision of nicotine replacement therapy and extended proactive post discharge telephone support. The review also indicates that cessation interventions are particularly effective when delivered to patients with a cardiovascular diagnosis. All health professionals may be effective in providing cessation treatments; however, the addition of a specialist smoking cessation counsellor appears to improve interventions in this setting. Finally, without the development of supportive systems, routine intervention with smoking patients by health professionals is unlikely. Recommendations for the delivery of effective smoking cessation interventions in hospitals are provided. [Wolfenden L, Campbell E, Walsh RA, Wiggers J. Smoking cessation interventions for in-patients: a selective review with recommendations for hospital-based health professionals.

Are biomarkers a useful aid in smoking cessation? A review and analysis of the literature

Healthcare providers' ability to motivate people to try to quit smoking or to remain abstinent is limited. Even with our best treatments, most smokers relapse within 1 year. Therefore it is important that we constantly strive to develop and test new, effective smoking interventions. Providing feedback on one's biomarkers (e.g., biological indices of smoking-related harm, harm exposure, or genetic susceptibility to disease) have been suggested as potentially useful for increasing smokers' motivation or ability to quit smoking. In fact, variations of this strategy are commonly incorporated into behavioral smoking-cessation interventions, but little empirical evidence has specifically addressed whether this approach is effective. In this article, the author reviews the theoretical rationale and empirical evidence regarding this practice. Although the preliminary evidence is promising, more research is needed to determine the efficacy of using biomarkers and the limits of the strategy's effectiveness. Future investigations should address these issues.