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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:Importance:Understanding supportive care needs in patients with cancer is important for developing approaches that enhance quality of life and promote satisfaction with care. Objective:To characterize the nature and frequency of sociodemographic, medical, and psychosocial factors associated with unmet needs in patients with uveal melanoma 1 week and 3 months after diagnosis. Design, Setting, and Participants:This 3-month, prospective, longitudinal survey study was conducted at a university-based ophthalmology practice from June 1, 2007, to July 1, 2011. Data were analyzed in April 2017. Consecutive patients (n?=?429) scheduled for diagnostic evaluation for an intraocular abnormality were assessed for eligibility. Participants were ineligible (n?=?25) if they were younger than 18 years, had previous advanced cancer, or evidenced cognitive impairment. Of the patients who provided informed consent (n?=?306), those subsequently diagnosed with uveal melanoma by an ophthalmologist (n?=?107) were included in the analysis. Main Outcomes and Measures:Unmet needs (ie, desire for help in psychological, physical, health information, communication, or social domains) were assessed using the Cancer Needs Questionnaire. Multivariable regression analyses determined factors associated with unmet need severity across 3 months. Results:One hundred seven patients (58 [54%] men; mean [SD] age, 59.0 [12.8] years) completed the baseline assessment. At 1 week after diagnosis, nearly all patients (85 of 86 [99%]) expressed at least 1 unmet need, as did 68 of 79 (86%) 3 months later. The most frequently endorsed needs were in the health information and psychological domains. Patients' unmet needs declined significantly over 3 months (mean [SD] change, -10.0 [14.4]; 95% CI, -6.4 to -13.6; t?=?-5.6). Sociodemographic and medical characteristics were unrelated to unmet need severity. However, higher prediagnosis instrumental social support (b?=?-0.2; 95% CI, -0.3 to -0.1; z?=?-2.8) and lower neuroticism (b?=?0.3; 95% CI, 0.1-0.5; z?=?2.9) predicted lower unmet need severity 1 week after diagnosis. Having a smaller social network predicted lower unmet need severity 3 months after diagnosis (b?<?0.1; 95% CI, <0.1 to <0.1; z?=?2.4) as well as a decline in needs from diagnosis to 3 months later (b?<?0.1; 95% CI, <0.1 to <0.1; z?=?2.3). Conclusions and Relevance:Within 1 week after diagnosis and 3 months later, most patients with uveal melanoma cited important health information and psychological needs. These findings suggest that prior to or at diagnosis, the severity of such needs and psychosocial factors that may be associated can be identified for proactive supportive intervention.

Project description:BackgroundAlthough there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited.ObjectiveWe aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery.MethodsEMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography). The data were randomly split into training, tuning, and testing sets in a ratio of 3:1:1. Each model was evaluated with 5-fold cross-validation and with an external EMR-based cohort at a tertiary hospital. Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. The primary outcome was 30-day mortality following invasive treatment.ResultsGBM showed the best performance with area under the receiver operating characteristic curve (AUROC) of 0.99; RF had a similar AUROC of 0.98. AUROCs of FNN and LR were 0.96 and 0.93, respectively. GBM had the highest area under the precision-recall curve (AUPRC) of 0.80, and the AUPRCs of RF, LR, and FNN were 0.73, 0.68, and 0.63, respectively. All models showed low Brier scores of <0.1 as well as highly fitted calibration plots, indicating a good fit of the ML-based models. On external validation, the GBM model demonstrated maximal performance with an AUROC of 0.90, while FNN had an AUROC of 0.85. The AUROCs of LR and RF were slightly lower at 0.80 and 0.79, respectively. The AUPRCs of GBM, LR, and FNN were similar at 0.47, 0.43, and 0.41, respectively, while that of RF was lower at 0.33. Among the categories in the GBM model, time-series dynamic data demonstrated a high AUROC of >0.95, contributing majorly to the excellent results.ConclusionsExploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.

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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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