Determinants of persistent smoking after acute myocardial infarction: an observational study

Background!#!Smoking cessation is one of the most effective secondary prevention measures after acute myocardial infarction (AMI). However, around 50% of smokers do not quit smoking after AMI. The aim of the present study is to estimate the proportion of patients quitting smoking and to identify determinants of persistent smoking after AMI in a region with increased cardiovascular mortality. We also assessed the time of smoking cessation after AMI.!##!Methods!#!We used follow-up data of patients registered with the Regional Myocardial Infarction Registry in Saxony-Anhalt (RHESA) in Germany. We assessed smoking status and determinants of persistent smoking six weeks after discharge from hospital after AMI. Information on smoking, sociodemographic characteristics, risk factors for AMI, experienced symptoms of AMI, and clinical care were gathered in a computer-assisted telephone interview and questionnaires filled out by study subjects and physicians or study nurses.!##!Results!#!Out of 372 smokers at the time of AMI, 191 (51.3%) reported that they quit smoking within six weeks after discharge from hospital after AMI. Strongest determinant of persistent smoking was a previous AMI before the current one (OR = 2.19, 95%CI 1.10-4.38) and strongest determinants of smoking cessation were experiencing complications in the hospital (0.37, 95%CI 0.12-1.12) and having a life partner (0.56, 95%CI 0.34-0.95). Most individuals who stopped smoking did so during the initial stay in the hospital, before the cardiac rehabilitation (CR).!##!Conclusions!#!Persistent smoking after AMI and its determinants were similar in our region to previous studies. CR cannot be viewed as determinant of smoking cessation - more likely the same teachable moment induces behavioural change with regard to smoking and participation in CR

Application of multivariate curve resolution alternating least squares (MCR-ALS) to the quantitative analysis of pharmaceutical and agricultural samples

10 pages, 6 figures, 3 tables.-- PMID: 18371770 [PubMed].-- Available online Aug 30, 2007.Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of different analytes in different type of pharmaceutical and agricultural samples is shown. In particular, MCR-ALS is applied first to the UV spectrophotometric quantitative analysis of mixtures of commercial steroid drugs, and second to the near-infrared (NIR) spectrophotometric quantitative analysis of humidity and protein contents in forage cereal samples. Quantitative results obtained by MCR-ALS are compared to those obtained using the well established partial least squares regression (PLSR) multivariate calibration method.Peer reviewe