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Medical and sociodemographic factors predict persistent smoking after coronary events.

ABSTRACT: Understanding the determinants of persistent smoking after a coronary event constitutes the basis of modelling interventions of smoking cessation in secondary prevention programs. We aim to identify the potentially modifiable medical, sociodemographic and psychosocial factors, comprising the study factors, associated with unfavourable risk factor control after CHD events.A cross-sectional explorative study used logistic regression analysis to investigate the association between study factors and smoking status in 1083 patients hospitalized with myocardial infarction and/or coronary revascularization. Hospital record data, a self-report questionnaire, clinical examination and blood samples were applied.At the index hospitalization, 390 patients were smoking and at follow-up after 2-36 months 167 (43%) of these had quit, while 230 reported persistent smoking. In adjusted analyses, unemployed or disability benefits (Odds ratio (OR) 4.1), low education (OR 3.5), longer smoking duration (OR 2.3) and not having ST-elevation myocardial infarction (STEMI) as index event (OR 2.3) were significantly associated with persistent smoking. Psychosocial factors at follow-up were not associated with persistent smoking. Smokers reported high motivation for cessation, with 68% wanting help to quit. Only 42% had been offered nicotine replacement therapy or other cessation aids. Smokers rated use of tobacco as the most important cause of their coronary disease (6.8 on a 1-10 Likert scale).Low socioeconomic status, prior duration of smoking, and not having STEMI as index event were associated with persisting smoking. Persistent smokers in this study seem to have an acceptable risk perception and were motivated to cease smoking, but needed assistance through cessation programs including prescription of pharmacological aids.Registered at ClinicalTrials.gov: NCT02309255 , registered retrospectively.

SUBMITTER: Sverre E

PROVIDER: S-EPMC5588720 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Medical and sociodemographic factors predict persistent smoking after coronary events.

Sverre Elise E Otterstad Jan Erik JE Gjertsen Erik E Gullestad Lars L Husebye Einar E Dammen Toril T Moum Torbjørn T Munkhaugen John J

BMC cardiovascular disorders 20170906 1

<h4>Background</h4>Understanding the determinants of persistent smoking after a coronary event constitutes the basis of modelling interventions of smoking cessation in secondary prevention programs. We aim to identify the potentially modifiable medical, sociodemographic and psychosocial factors, comprising the study factors, associated with unfavourable risk factor control after CHD events.<h4>Methods</h4>A cross-sectional explorative study used logistic regression analysis to investigate the as ...[more]

PMID: 28877684

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Project description:Background One measure to quantify the degree of dysregulation is allostatic load (AL). Typically, AL incorporates information on diverse biomarkers and is associated with health outcomes such as cardiovascular diseases or the incidence of coronary events (C-E). Aims This study investigates the predictive performance of different AL scoring methods on the incidence of coronary events (C-E). This study also elaborates sex differences in the baseline risks of C-E and the AL associated risks of C-E. Design Longitudinal data analysis of the Heinz Nixdorf Recall Study (Risk Factors, Evaluation of Coronary Calcification, and Lifestyle) of 4327 participants free of C-E at study baseline aged 45–75. The data contains over 13 biomarkers measuring AL. Methods After conducting multiple imputations on missing values on AL for 826 participants, the analysis sample consisted of N = 4327 participants. We applied the two most commonly used methods of AL scoring AL (count-based and Z-score) and a recently developed logistic regression weighting method (LRM) approach. Cox regression was used to predict the incidence of C-E for each AL score. Results were estimated without (M0) and with (M1) covariate adjustment, and in a final model (M2), with an interaction between AL and sex. Results We found no violation of the proportional hazard assumption and significant differences in the survival curves between the sexes for C-E (Log-rank test: prob. > Chi2 = 0.000). In M0, all AL-scoring methods predicted C-E significantly, with the LRM based AL-score having the best performance (hazard ratio = 3.133; CI: [2.630, 3.732]; Somer's D = 0.717). After covariate inclusion, differences between the scoring methods levelled, though the count-based method and LRM performed better than the Z-scoring method. The interaction analysis in M2 showed a significant multiplicative interaction for the count-based method (1.254; [1.066, 1.475]) and for the LRM (1.746; [1.132, 2.692]). The additive relative excess risk due to interaction (RERI) measure was negative for the count-based method (RERI = −1.967; [-3.778; −0.156]) and the LRM (RERI = −1.909 [-3.910; 0.091]), indicating subadditivity. Conclusion AL scores are suitable for predicting C-E. Differences between the AL-scoring algorithms were only present after including interactions. We value the count-based method as suitable for clinical practice since its calculation is relatively simple, and performance was among the best. Interaction analysis revealed that despite strong sex differences in baseline C-E, the effect of AL is more pronounced for females at high levels of AL; thus, females could benefit more from a potential intervention on AL. We suggest further investigation of sex differences concerning the mediation by physiological and psychological intermediates.

Project description:BackgroundResearch on risk factors for neuropsychiatric adverse events (NAEs) in smoking cessation with pharmacotherapy is scarce. We aimed to identify predictors and develop a prediction model for risk of NAEs in smoking cessation with medications using Bayesian regularization.MethodsBayesian regularization was implemented by applying two shrinkage priors, Horseshoe and Laplace, to generalized linear mixed models on data from 1203 patients treated with nicotine patch, varenicline or placebo. Two predictor models were considered to separate summary scores and item scores in the psychosocial instruments. The summary score model had 19 predictors or 26 dummy variables and the item score model 51 predictors or 58 dummy variables. A total of 18 models were investigated.ResultsAn item score model with Horseshoe prior and 7 degrees of freedom was selected as the final model upon model comparison and assessment. At baseline, smokers reporting more abnormal dreams or nightmares had 16% greater odds of experiencing NAEs during treatment (regularized odds ratio (rOR) = 1.16, 95% credible interval (CrI) = 0.95 - 1.56, posterior probability P(rOR > 1) = 0.90) while those with more severe sleep problems had 9% greater odds (rOR = 1.09, 95% CrI = 0.95 - 1.37, P(rOR > 1) = 0.85). The prouder a person felt one week before baseline resulted in 13% smaller odds of having NAEs (rOR = 0.87, 95% CrI = 0.71 - 1.02, P(rOR < 1) = 0.94). Odds of NAEs were comparable across treatment groups. The final model did not perform well in the test set.ConclusionsWorse sleep-related symptoms reported at baseline resulted in 85%-90% probability of being more likely to experience NAEs during smoking cessation with pharmacotherapy. Treatment for sleep disturbance should be incorporated in smoking cessation program for smokers with sleep disturbance at baseline. Bayesian regularization with Horseshoe prior permits including more predictors in a regression model when there is a low number of events per variable.

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Medical and sociodemographic factors predict persistent smoking after coronary events.

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Medical and sociodemographic factors predict persistent smoking after coronary events.

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