Predictors of Quitting Smoking in Cardiac Rehabilitation

Covariates of success in quitting smoking: a systematic review of studies from 2008 to 2021 conducted to inform the statistical analyses of quitting outcomes of a hospital-based tobacco dependence treatment service in the United Kingdom

Background

Smoking cessation interventions are being introduced into routine secondary care in the United Kingdom (UK), but there are person and setting-related factors that could moderate their success in quitting smoking. This review was conducted as part of an evaluation of the QUIT hospital-based tobacco dependence treatment service ( https://sybics-quit.co.uk). The aim of the review was to identify a comprehensive set of variables associated with quitting success among tobacco smokers contacting secondary healthcare services in the UK who are offered support to quit smoking and subsequently set a quit date. The results would then be used to inform the development of a statistical analysis plan to investigate quitting outcomes.

Methods

Systematic literature review of five electronic databases. Studies eligible for inclusion investigated quitting success in one of three contexts: (a) the general population in the UK; (b) people with a mental health condition; (c) quit attempts initiated within a secondary care setting. The outcome measures were parameters from statistical analysis showing the effects of covariates on quitting success with a statistically significant (i.e., p-value <0.05) association.

Results

The review identified 29 relevant studies and 14 covariates of quitting success, which we grouped into four categories: demographics (age; sex; ethnicity; socio-economic conditions; relationship status, cohabitation and social network), individual health status and healthcare setting (physical health, mental health), tobacco smoking variables (current tobacco consumption, smoking history, nicotine dependence; motivation to quit; quitting history), and intervention characteristics (reduction in amount smoked prior to quitting, the nature of behavioural support, tobacco dependence treatment duration, pharmacological aids).

Conclusions

In total, 14 data fields were identified that should be considered for inclusion in datasets and statistical analysis plans for evaluating the quitting outcomes of smoking cessation interventions initiated in secondary care contexts in the UK.

PROSPERO registration

CRD42021254551 (13/05/2021).

Clinical challenges facing patient participation in cardiac rehabilitation: cigarette smoking

INTRODUCTION

Cardiac rehabilitation (CR) is highly effective at reducing morbidity and mortality. However, CR is underutilized, and adherence remains challenging. In no group is CR attendance more challenging than among patients who smoke. Despite being more likely to be referred to CR, they are less likely to enroll, and much more likely to drop out. CR programs generally do not optimally engage and treat those who smoke, but this population is critical to engage given the high-risk nature of continued smoking in those with cardiovascular disease.

AREAS COVERED

This review covers four areas relating to CR in those who smoke. First, we review the evidence of the association between smoking and lack of participation in CR. Second, we examine how smoking has historically been identified in this population and propose objective screening measures for all patients. Third, we discuss the optimal treatment of smoking within CR. Fourth, we review select populations within those who smoke (those with lower-socioeconomic status, females) that require additional research and attention.

EXPERT OPINION

Smoking poses a challenge on multiple fronts, being a significant predictor of future morbidity and mortality, as well as being strongly associated with not completing the secondary prevention program (CR) that could benefit those who smoke the most.

Cardiac arrest prediction in smokers using enhanced Artificial Bee Colony algorithm with stacked autoencoder model

In the recent times, the cardiac arrest is a severe heart disease, which results in millions of annual casualties. In this article, the heart rate variability (HRV) parameters are used for predicting cardiac arrest in smokers based on the deep learning techniques. First, the input data is collected from MITU Skillogies dataset, which consists of 1562 smoker and non-smoker instances with 19 HRV input attributes/features. After data collection, the enhanced Artificial Bee Colony algorithm (EABC) is developed for feature selection. The EABC algorithm includes two new multi-objective functions for decreasing the number of attributes in the MITU Skillogies dataset. This mechanism superiorly reduces the burden of computational complexity and improves classification accuracy. Further, the selected attributes are given to the stacked autoencoder classifier for non-cardiac arrest and cardiac arrest classification in smokers for early diagnosis. The extensive experiment showed that the EABC with stacked autoencoder model obtained 96.26% of classification accuracy, which is better related to the traditional machine learning models.

Predictors of Successful Smoking Cesstion in Cardiac Rehabilitation Patients with a History of Acute Coronary Syndrome

Aim: To establish socio-psychological and clinical smoking predictors cessation at the stage of health-resort rehabilitation in patients who have recently suffered an acute coronary event. Materials and Methods: 68 patients aged 42-68 years (average age 56.70}6.1 years) who underwent a cardiorehabilitation program in the heart rehabilitation department of the health-resort complex „Morshinkurort” after a recent ACS (no more than 28 days ago) were examined. Depending on the smoking habit, all patients were divided into two groups. The first (I) group included smoking patients who gave up smoking in the course of CR (n=38, average age 57.10}6.73 years), the second (II) group included smokers who continued smoking during the healthresort stay treatment (n=30, average age 56.58}5.74 years). Predictors of smoking cessation were determined in patients of the studied groups using the method of binary logistic regression. Results: In the course of the study, 5 independent predictors of quitting smoking were established, which turned out to be statistically significant - smoking index, anxiety level, body mass index, comorbidity and marital status. The chances of quitting smoking were 1.79 times greater (95% CI from 1.25 to 2.56) in patients with higher BMI and 3.23 times (95% CI from 0.57 to 18.40) in those who are in family status. High comorbidity, higher SI score, and higher anxiety levels were significantly associated with a reduced likelihood of quitting smoking. Conclusions: Cardiac rehabilitation patients with high nicotine dependence, lower body weight, loneliness, significant anxiety symptoms, and multiple comorbidities are less likely to successfully quit smoking. Established independent predictors of smoking cessation must be used when choosing strategies for the treatment of tobacco addiction at the stage of CR. Screening and treatment for anxiety disorders should be included in smoking cessation programs to improve the SI effectiveness.

Carbon monoxide monitoring to objectively measure smoking status in cardiac rehabilitation

OBJECTIVE

Participation in cardiac rehabilitation (CR) is associated with reduced morbidity and mortality. However, most programs rely on self-report measures when assessing the critical risk factor of smoking. This study examined smoking status using self-report versus objective measurement using expired carbon monoxide (CO) and compared patient characteristics by CO level.

METHOD

Patients were screened for smoking status when entering CR by self-report and by objectively measured CO. Measures of aerobic fitness, educational attainment, depressive symptoms, and self-reported physical function were also collected. The discrepancy between smoking status based on self-report and objective measurement was examined and patient characteristics by CO measurement were compared.

RESULTS

Of the 853 patients screened, 62 self-reported current smoking and 112 had a CO of ≥ 4 ppm. Using a cut-off of ≥ 4 ppm encompassed almost all self-reported smokers (specificity: 98.5%) and identified 61 patients (not reporting current smoking) needing further screening. Further questioning yielded an additional 21 patients with combusted use (tobacco/cannabis), six nonsmoking patients with environmental CO exposure, and 34 where the reason for elevated CO was unknown. CO ≥ 4 ppm patients were younger (62.2 vs. 67.7, p < .01), had higher depression scores (5.6 vs. 3.7, Patient Health Questionairre-9, p < .01), had lower educational attainment (59.0% ≤ high school vs. 31.3%, p < .01), had lower levels of fitness (after controlling for clinical characteristics, p < .01), and completed fewer CR sessions (18 vs. 22, p < .01).

CONCLUSIONS

A substantial number of patients who are actively smoking may be misclassified by relying on patient report alone. CO monitoring provides a simple and objective method of systematically screening patients. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Suboptimal control of cardiovascular risk factors in myocardial infarction survivors in a cardiac rehabilitation program

INTRODUCTION AND OBJECTIVES

As short-term mortality continues to decrease after myocardial infarction (MI), secondary prevention strategies attain increasing relevance. This study aimed at assessing the control of cardiovascular (CV) risk factors, including dyslipidemia, hypertension and diabetes, in a contemporary cohort of MI survivors who completed an exercise-based cardiac rehabilitation (EBCR) program.

METHODS

Observational, retrospective cohort study including patients admitted to a tertiary center with acute MI between November 2012 and April 2017, who completed a phase II EBCR program after discharge. Achievement of low-density lipoprotein (LD) cholesterol, blood pressure and HbA1c guideline recommended targets was assessed. Lipid profile parameters were assessed and compared at three time points (hospitalization, beginning and end of the program).

RESULTS

A total of 379 patients were included. Mean age was 58.8±10.6 years; 81% were male. Considering the European Society of Cardiology's guidelines on contemporary data collection, 61%, 87% and 71% achieved the recommended LDL cholesterol, blood pressure and HbA1c targets, respectively, at the end of the program. Combining all three risk factors, 42% achieved the recommended targets. High-sensitivity C-reactive protein decreased between the beginning and the end of the program [0.14 (0.08-0.29) mg/L to 0.12 (0.06-0.26) mg/L; p<0.001].

CONCLUSION

Despite contemporary management strategies, including enrollment in a structured EBCR program, a substantial number of patients presented suboptimal control of CV risk factors. Considering the dyslipidemia, hypertension and diabetes results, less than half of the enrolled individuals achieved the recommended targets. These findings highlight a pivotal unmet need which could be particularly relevant in improving CV outcomes by enhancing secondary prevention profiles.

Is Weight Gain Inevitable for Patients Trying to Quit Smoking as Part of Cardiac Rehabilitation?

The literature is uncertain about the extent to which those who attend cardiac rehabilitation (CR) gain weight while trying to quit smoking. This study aimed to determine the extent of CR-based smoking cessation provision and whether CR, as delivered in routine practice, is associated with helping patients quit smoking and avoid weight gain. Data from the UK National Audit of Cardiac Rehabilitation database, between April 2013 and March 2016, were used. Smoking status is categorised as smokers and quitters assessed by patient self-report. Outcomes included body weight, blood pressure, depression, and physical activity. A multiple linear regression model was constructed to understand the effect of continuing smoking or quitting smoking on CR outcomes. CR outcome scores were adjusted by the baseline CR score for each characteristic. An e-survey collected information about the smoking cessation support offered to patients attending CR. A total of 2052 smokers (58.59 ± 10.49 years, 73.6% male) and 1238 quitters (57.63 ± 10.36 years, 75.8% male) were analysed. Overall, 92.6% of CR programmes in the United Kingdom (UK) offer smoking cessation support for CR attenders. Quitting smoking during CR was associated with a mean increase in body weight of 0.4 kg, which is much less than seen in systematic reviews. Quitters who attended CR also had better improvements in physical activity status and psychosocial health measures than smokers. As delivered in routine practice, CR programmes in the UK adhere to the guideline recommendations for smoking cessation interventions, help patients quit smoking, and avoid weight gain on completion of CR.