Objectives
This is a protocol for a Cochrane Review (intervention). The objectives are as follows:
To examine the impact of smoking cessation on mortality from cardiac disease and major adverse cardiovascular events (MACE), in people with incident coronary heart disease (CHD).
Background
Description of the condition
Cardiovascular disease (CVD) is responsible for approximately 31% of all global mortality (Kendir 2018). The Global Burden of Disease study reported that 17.9 million deaths per year are attributed to CVD globally (Roth 2017). In the United States, CVD accounts for more deaths since 1919 than any other single major cause of death, with the majority caused by coronary heart disease (CHD; 43.2%), followed by stroke (16.9%)(Benjamin 2019). According to a 2017 review, approximately 17% of the population of Europe are living with CVD (Wilkins 2017). It is well established that tobacco smoking is a risk factor for incident CVD (Banks 2019; Carter 2015; Gakidou 2017). A prospective cohort study conducted in the UK noted that out of a sample of 12,393 people diagnosed with incident CHD between 1999 and 2013, 18.2% of people with incident disease identified as a person who smokes. One year later, around half of those people were still smoking (Farley 2017). Similar rates of persistent smoking after a CVD event have been reported in South Korea (Choi 2013; Lim 2017), and Greece (Rallidis 2005).
Description of the intervention
As tobacco smoking is a risk factor for CHD, it follows that stopping smoking may improve cardiovascular health and help prevent recurrent CVD events in people diagnosed with CHD. Although many people who quit do so without support (Hummel 2018), quit attempts are more likely to be successful when supported by evidence‐based behavioural (Hartmann‐Boyce 2021), and pharmacological interventions (Cahill 2013). This review will focus on the effects of successfully quitting smoking, and not on any particular intervention to aid cessation.
How the intervention might work
There are several mechanisms by which smoking cessation could reduce recurrent CVD incidence in people with CHD. The primary underlying pathophysiological mechanism that leads to CVD is atherosclerosis, with endothelial dysfunction suggested as an early marker (Davignon 2004). Stopping smoking improves endothelial function (Celermajer 1993; Delgado 2020). Atherosclerosis is the build‐up of lipids (fats) in the inner layers of arteries, which leads to the hardening and narrowing of these arteries, and the thickening of the arterial wall. This can result in thrombosis (blood clots), which can lead to myocardial infarction (heart attacks), or ischaemic stroke (Nagareddy 2013). Smoking impairs platelet function, making their coagulation, the first stage in clot formation, more likely (Pamukcu 2011). Cessation restores normal platelet function within days, as the half‐life of platelets is only a matter of days (Morita 2005). Smoking may also affect the formation of fatty deposits in arteries and blood clots, oxidative stress, haemostatic factors (platelet function, fibrinogen, and d‐dimer), fibrinolysis, inflammation, lipid modification, and vasomotor function (IARC 2007). The 2010 US Surgeon General's report concluded that following smoking cessation, the risk for endothelial dysfunction, thrombosis, and reduced oxygen delivery can lessen within a short period (USDHHS 2010). Therefore, while reducing the incidence of recurrent CVD through cholesterol‐lowering therapies, such as statins, is an option for risk reduction, smoking cessation may also play a substantial role in reducing the risk of further CVD events in people diagnosed with CHD (Wilt 2004).
Why it is important to do this review
The recent 2020 US Surgeon General’s report concluded that for people who currently smoke and are diagnosed with CHD, there is sufficient evidence to infer a causal relationship between smoking cessation and reduced risk of new and recurrent cardiac events, all‐cause mortality, deaths due to cardiac causes, and sudden death (USDHHS 2020). While they noted the trends in risk ratios across the different studies, predominantly suggesting that smoking cessation decreased risk, they did not calculate an overall risk ratio from the evidence they gathered. A Cochrane Review, published in 2003, compared the risk of mortality in people who stopped smoking versus those who continued, and synthesised the data with a meta‐analysis (Critchley 2003). They included 20 studies that followed participants with CHD for at least two years, and found the pooled risk ratio for death was 0.64 (95% confidence interval 0.58 to 0.71) when comparing people who had quit smoking with those who had not.
Many health guidelines, such as the American College of Cardiology/American Heart Association (ACC/AHA) Guidelines for secondary prevention in people with coronary artery disease, now include smoking cessation as a secondary prevention intervention for CVD events in people with CHD (Smith 2011). However, there are still a large proportion of people diagnosed with CHD who continue to smoke, as previously discussed (Choi 2013; Farley 2017; Lim 2017; Rallidis 2005). Despite the evidence that smoking cessation interventions prevent CVD events, there appears to be a gap between the knowledge, and translating it into providing active smoking cessation treatment to people diagnosed with CHD, which we know can increase quit rates (Cahill 2013; Hartmann‐Boyce 2021). An update of the evidence to understand the magnitude and speed of this risk reduction due to smoking cessation could motivate clinicians to provide cessation treatment, as well as statins and aspirin for people with CHD, who are under their care. Therefore, this review aims to establish the certainty of the evidence that smoking cessation changes the prognosis of CHD, the strength of the effect, and how this effect changes in the time following diagnosis.
This protocol and the subsequent review will build upon the previously published Cochrane Review (Critchley 2003). The previous review focused solely on studies with a minimum two‐year follow‐up. We decided to shorten the required follow‐up period, to establish whether beneficial effects of smoking cessation occur more quickly following cessation. This review will also investigate new outcomes, such as the risk for major adverse cardiac endpoints, which have been informed by patient and public involvement (PPI), and attempt to understand the role of potential mediating factors on smoking cessation effects in CHD.
Objectives
To examine the impact of smoking cessation on mortality from cardiac disease and major adverse cardiovascular events (MACE), in people with incident coronary heart disease (CHD).
Methods
Criteria for considering studies for this review
Types of studies
Cohort studies, and both cluster and individually randomised controlled trials (RCTs) of at least six months duration. All studies included will be assessed as cohort studies.
Types of participants
We will include adults diagnosed with CHD (including myocardial infarction, stable or unstable angina, heart failure due to atherosclerosis), who smoke tobacco at study baseline in this review. To maximise our review's sensitivity, we will include any definition of CHD used by the included studies, as explicit definitions may exclude studies that fail to report their diagnostic criteria. We will include any participants that are defined as smoking at baseline, based on the criteria of the individual studies.
Types of interventions
Conceptually, the intervention in this review is smoking cessation, not interventions offered to support smoking cessation. The comparator is continued smoking. In order to identify people who have stopped smoking in contrast to those who have continued, smoking status should be measured on at least two occasions during the study; at baseline, and at some point after that. We will accept the definition of smoking cessation described by each included study. If multiple definitions are provided, we will favour the most stringent (i.e. continuous over point prevalence abstinence, and biochemically validated abstinence over self‐report).
Types of outcome measures
Primary outcomes
Death from cardiovascular disease
Major adverse cardiovascular events (MACE), defined as cardiovascular death, non‐fatal myocardial infarction, and non‐fatal stroke
Secondary outcomes
All‐cause mortality
Stroke
Myocardial infarction (STEMI (ST‐elevation myocardial infarction)), NSTEMI (non‐STEMI), or both)
New‐onset angina
Quality of life (QOL)
Where possible, we will collect follow‐up outcome results at two time points; up to a year, and more than a year. Should the studies present results for multiple time points within either time period, we will select the time point closest to one year for the first follow‐up period, and the longest follow‐up time point for the second.
To be included in the review, studies have to measure at least one of the outcomes above, with at least a six‐month follow‐up from baseline. In the studies that measure these outcomes, we will also investigate the following secondary manifestations of arterial disease (SMART) risk score laboratory results as potential mediators of the effect of smoking cessation on death from cardiovascular disease and major cardiac endpoints (Dorresteijn 2013):
HDL‐cholesterol (high‐density lipoprotein cholesterol)
Total cholesterol
eGFR (estimated glomerular filtration rate)
High‐sensitivity CRP (C‐reactive protein)
Search methods for identification of studies
Electronic searches
We will search the following databases from inception to date of search.
Cochrane Tobacco Addiction Group's Specialised Register, for details of how this register is populated see the Cochrane Tobacco Addiction Group's website;
Cochrane Central Register of Controlled Trials (CENTRAL), in the Cochrane Library;
MEDLINE, Ovid;
Embase, Ovid;
Cumulative Index to Nursing and Allied Health Literature (CINAHL), EBSCOhost.
The most recent searches carried out for Critchley 2003, were in April 2003; however, as we changed the inclusion criteria and secondary outcomes, we will search the databases from inception, rather than from the previous search date. See Appendix 1 for the MEDLINE search strategy.
Searching other resources
We will contact experts in the field to identify existing reviews, studies, or research underway. We will explore trial databases to identify research that is currently underway or recently completed, including ClinicalTrials.gov (www.clinicaltrials.gov) and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP; who.int/ictrp).
Data collection and analysis
Selection of studies
Two review authors will independently screen titles and abstracts of records returned by the searches. Should disagreements arise, these will be resolved through discussion or referral to a third review author. We will aim to maximise sensitivity in this initial screening by including studies that might not have information directly relevant to our research question presented in the title and abstract. We will translate non‐English language studies, as indicated. We will acquire the full text of potentially relevant articles identified at the title and abstract screening stage. Two review authors will independently screen these texts for final inclusion. Disagreements will be resolved through discussion or referral to a third review author. We will record reasons for exclusion at the full‐text screening stage. We will record the selection process in sufficient detail to complete a PRISMA flow diagram (Moher 2009).
Data extraction and management
We will first pilot the data extraction form, and subsequently, make any appropriate and necessary changes. We will extract the following data from each study:
Authors
Date and country of publication
Study design
Study dates
Inclusion and exclusion criteria
Analysis method
Outcome measure(s)
Length of follow‐up
Coronary heart disease definition
Smoking cessation intervention(s) used (if relevant)
Author declarations of interest
Study funding source
Additional comments
N at baseline and follow‐up
Percentage (%) female
Mean age (standard deviation (SD))
Smoking cessation definition
Type of biochemical validation (if any)
Covariates adjusted for
Potential mediators
Risk of bias information for making assessments using ROBINS‐I (Sterne 2016)
Unadjusted and adjusted estimates to calculate the hazards ratios (HR) of CVD mortality outcome, MACE outcomes, all‐cause mortality, non‐fatal stroke, myocardial infarction, and new‐onset angina for smokers and non‐smokers
Unadjusted and adjusted estimates to calculate the standardised mean difference (SMD) in quality of life outcomes: for each group – mean at baseline and follow‐up, mean change from baseline to follow‐up, and the difference in mean change from baseline to follow‐up, and variance
Details of any analyses investigating the difference in effects moderated by sex
Assessment of risk of bias in included studies
We will assess the risk of bias for all primary outcomes reported in our included studies using ROBINS‐I (Sterne 2016). This tool determines the risk of bias in non‐randomised studies based on the following domains: bias due to confounding; bias in selection of participants into the study; bias in classification of interventions; bias due to deviations from intended interventions; bias due to missing data; bias in measurement of outcomes; and bias in selection of the reported result. Two review authors will independently assess bias. Should disagreements arise, these will be resolved through discussion or referral to a third review author. We will categorise the risk of bias as low, moderate, serious, or critical for each domain, and for each study overall, using the signalling questions built into the tool.
As part of our assessment, we will investigate whether each study has controlled for the following potential confounders:
Socioeconomic status
Age
The severity of the initial event, risk of future events
Lipids
Hypertension
Medications for secondary prevention of CHD (aspirin, statins, and beta‐blockers)
Diabetes status
Measures of treatment effect
We will compare the CVD mortality rates and MACE outcomes (primary outcomes), between participants who quit smoking versus those who continued to smoke for each included study that reports these outcomes. We will also compare the all‐cause mortality rate, changes in quality of life, non‐fatal stroke rates, myocardial infarction, and new‐onset angina (secondary outcomes) between participants who quit smoking versus those who continued smoking for each included study, where these are measured.
We will extract data to calculate the HR and 95% confidence interval (CI) for CHD‐related mortality, MACE outcomes, all‐cause mortality rate, and rates of non‐fatal stroke, myocardial infarction, and new‐onset angina for people who smoke and those who stop smoking. Should studies provide both unadjusted and adjusted estimates, we will extract both.
For continuous measures of quality of life, we will first calculate mean differences (MD), so we can subsequently calculate standardised mean difference (SMD) and 95% CI. We will extract data in this order of preference for meta‐analysis: 1) adjusted or unadjusted MD (difference in change from baseline to follow‐up) and measure of variation between exposure groups (preference or adjusted estimates); 2) mean change in quality of life scores from baseline to follow‐up and measure of variance, by exposure group; 3) mean quality of life scores and measures of variance at baseline and final follow‐up, by exposure group. We will then use a standard formula to calculate the mean change and variance by exposure group (Follmann 1992). From there, we will calculate the SMD using standard formulae outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2021). We will classify the effect size according to Cohen’s D, where we will deem an effect size of 0.2 to be small (Cohen 1988).
Unit of analysis issues
We do not anticipate these because individuals, not clusters, will continue smoking or stop smoking, and we will not analyse participants according to their randomised groups.
Dealing with missing data
We will write to trial authors if additional data are necessary to calculate effect estimates for any included studies. If we cannot obtain outcome data, we will report studies narratively.
Assessment of heterogeneity
We will assess whether, and which studies we can pool, based on the similarity of participants and their diagnoses at baseline, and the methodological characteristics of studies. We will not carry out meta‐analyses if the between‐study variance is too great. Where meta‐analysis is appropriate, we will investigate statistical heterogeneity using the I² statistic, which quantifies the percentage of between‐study variability explained by genuine heterogeneity rather than chance. An I² value above 75% indicates substantial heterogeneity (Higgins 2021). Therefore, if we detect an I² of 75% or higher, we will assess whether presenting a pooled analysis is appropriate. We will conduct the subgroup and sensitivity analyses described below to investigate any causes of observed heterogeneity.
Assessment of reporting biases
When we carry out a meta‐analysis of ten studies or more, we will develop funnel plots to examine if there is evidence of asymmetry. Funnel plots are used to illustrate the relationship between the effect estimates of individual studies against the study's size or precision – the greater the asymmetry, the greater the risk of reporting bias.
Data synthesis
We will use random‐effects generic inverse variance methods to pool eligible studies reporting on our dichotomous outcomes, and present the resulting HRs and 95% CIs.
For our quality of life outcome, we will pool SMDs and 95% CIs for individual studies using random‐effects generic inverse variance methods, to generate a pooled SMD and 95% CIs. An SMD greater than zero will indicate that quitting smoking is associated with better quality of life at follow‐up.
When individual studies report adjusted and unadjusted outcomes, we will pool the adjusted estimates in our analyses.
We will account for the impact of our prespecified mediators by running a meta‐regression, and adjusting for the mediators measured at the 'per study' level, where possible. The predictor variable for a continuously measured mediator in the meta‐regression will be the average value reported in that study, resulting in a between‐study comparison. We will also identify any studies that carried out in‐study mediation analyses, and summarise these results narratively.
In addition, we will identify studies that carried out in‐study analyses to investigate any potential differences in effect by sex, and summarise these results narratively.
We will conduct all meta‐analyses using Review Manager Web (RevMan Web), and meta‐regressions using Stata.
Subgroup analysis and investigation of heterogeneity
We plan to carry out the following subgroup analyses, if appropriate:
comparing the effect estimates from studies that present adjusted estimates versus unadjusted estimates;
comparing effect estimates based on the time of longest follow‐up;
comparing studies that use more stringent methods of classifying people’s smoking status with those that use less stringent measures;
comparing studies that explicitly classify nicotine replacement therapy (NRT) and e‐cigarette users as participants who have ceased smoking versus those who do not deem NRT, e‐cigarette users (or both) as quitters, versus studies that did not note whether NRT and e‐cigarette users were classified as quitters or not;
comparing effect estimates based on the sex of the studies’ populations (men only versus women only versus mixed population of men and women).
Sensitivity analysis
We will conduct the following sensitivity analyses for all our outcomes:
testing the impact of excluding studies that are deemed to be at higher risk of bias from analyses of the primary outcomes, i.e. assessed at critical and serious risk, or at critical risk (if all other studies are classed at serious risk), in one of the ROBINS‐I domains);
testing the difference between using adjusted or unadjusted estimates for studies that provide both.
Summary of findings and assessment of the certainty of the evidence
We will create a summary of findings table reporting the pooled effect estimates for our primary outcomes using GRADEpro GDT software. Two review authors will judge the certainty of this evidence according to GRADE's eight considerations for non‐randomised studies (risk of bias, inconsistency, imprecision, indirectness, publication bias, size of the effect, plausible confounding, dose response gradient (Schünemann 2013)). We will categorise the certainty of the evidence for each primary outcome as high, moderate, low, or very low, and we will take these assessments into account when drawing our conclusions.
History
Protocol first published: Issue 4, 2021
Acknowledgements
We would like to thank Jonathan Livingstone‐Banks and Nia Roberts (University of Oxford) for their contributions to the search strategy. We would like to thank Amanda Farley (University of Birmingham) and Julia Critchley (St George’s, University of London) for providing peer review, Sandra Wilcox for providing consumer review, and Tim Coleman (University of Nottingham) for providing editorial review. This project was partially supported by the British Heart Foundation (studentship to ADW) and the National Institute for Health Research (NIHR), via Cochrane Infrastructure funding to the Tobacco Addiction Group. The views and opinions expressed herein are those of the authors, and do not necessarily reflect those of the Systematic Reviews Programme, NIHR, National Health Service, or the Department of Health and Social Care. PA is an NIHR senior investigator, and is funded by NIHR Oxford Biomedical Research Centre (BRC) and the NIHR Collaboration for Leadership in Applied Health Research and Care (CLAHRC) Oxford.
Appendices
Appendix 1. MEDLINE search strategy
smoking cessation.mp. or exp Smoking Cessation/
tobacco cessation.mp. or "Tobacco‐Use‐Cessation"/
(nicotine dependence or tobacco dependence).mp.
exp Smoking/th
"Tobacco‐Use‐Disorder"/
Smoking reduction/ or Smoking reduction.mp.
exp Pipe smoking/ or exp Tobacco smoking/ or exp Tobacco Products/ or exp Smoking/
((quit$ or stop$ or ceas$ or giv$ or abstain* or abstinen*) adj5 (smoking or smoke* or tobacco)).ti,ab.
exp Tobacco/ or exp Nicotine/
1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9
myocardial infarct*.mp. [mp=ti, ab, ot, nm, hw, fx, kf, ox, px, rx, ui, sy, tc, id, tm, mh]
acute coronary syndrome.mp. [mp=ti, ab, ot, nm, hw, fx, kf, ox, px, rx, ui, sy, tc, id, tm, mh]
(coronary heart disease or MI or myocardial iscahemia or myocardial ischemia or CHD or CAD or heart attack or heart disease or NSTEMI or STEMI or angina or coronary artery disease).mp.
11 or 12 or 13
14 and 10
Epidemiologic studies/
exp case control studies/
exp cohort studies/
Case control.tw.
(cohort adj (study or studies)).tw.
Cohort analy$.tw.
(Follow up adj (study or studies)).tw.
(observational adj (study or studies)).tw.
Longitudinal.tw.
Cross sectional.tw.
Cross‐sectional studies/
Retrospective.tw.
Follow‐up studies/
16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 28
15 and 29
Contributions of authors
ADW drafted the protocol with comments and revisions from JHB, NL, & PA.
Sources of support
Internal sources
-
Nuffield Department of Primary Care Health Sciences, University of Oxford, UK
Employer of JHB, NL & PA and host institution of ADW
External sources
-
British Heart Foundation, UK
Studentship funding for ADW
-
NIHR, UK
Cochrane TAG infrastructure funding
-
NIHR, UK
Paul Aveyard is an NIHR senior investigator
-
NIHR Applied Research Collaboration, UK
Paul Aveyard is funded by NIHR Oxford Applied Research Collaboration
-
NIHR Biomedical Research Centre, UK
Paul Aveyard is funded by NIHR Oxford Applied Research Centre
Declarations of interest
ADW: none
JHB: none
NL: none
PA: none
New
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