The Impact of Smoking Status 1 Year After ST-Segment Elevation Myocardial Infarction on Cardiovascular Events and Mortality in Patients Aged ≤60 Years.

Smoking is associated with increased risk for acute ST-elevation myocardial infarction (STEMI) at a young age. Although smoking is a modifiable risk factor, smoking cessation rates after STEMI are suboptimal. We investigated the association between smoking status 1 year after STEMI and adverse events in patients (n = 765) aged ≤60 years. Patients were categorized as: (1) nonsmokers, (2) quit smoking, and (3) continued/resumed smoking. The association between smoking status and risk for major adverse cardiovascular events (MACEs) was analyzed during a median follow-up of 8 years. At presentation with STEMI, the mean age was 51 ± 7 years (88% men) and 427 (56%) were smokers. A year after STEMI, 272 continued smoking, 35 quit but later resumed smoking (summed to a single group; n = 307), and 120 quit smoking. Continued smoking was associated with younger age, male gender, lower weight, and low socioeconomic status. Compared with nonsmokers, the adjusted hazard ratio (95% confidence interval) for myocardial infarction, stroke, unstable angina, death, and MACE was 2.51 (1.67 to 3.73), 2.07 (0.94 to 4.56), 3.73 (1.84 to 7.58), 2.52 (1.53 to 4.13), and 2.40 (1.80 to 3.22), accordingly, in those who continued to smoke. However, the adjusted hazard ratio was not significantly associated with these outcomes in patients who quit smoking (MACE: 1.20 [0.77 to 1.87], p=0.414; nonsignificant for individual end points). In conclusion, the prevalence of smoking in young and middle-aged patients presenting with STEMI is high and smoking cessation rates are low. A year after STEMI, those who continued to smoke had worse cardiovascular outcomes and death compared with nonsmokers; however, the long-term outcomes among those who quit smoking appear to be comparable with nonsmokers. The results highlight the contrast between health benefits of quitting smoking after STEMI and low abstinence rates in clinical practice.

Computerized model for objectively evaluating cutting performance using a laparoscopic box trainer simulator.

BACKGROUND: Laparoscopic box trainer simulator has recently become a tool for assessment of physicians' surgical and laparoscopic skills, and training using such a simulator has been incorporated into the curricula of surgery syllabus. With the increased use of box trainer simulators, there is a great need for obtaining reliable and objective evaluations of the trainees' performances. Here, we introduce an automated tool for assessing laparoscopic cutting performance by using image-processing algorithms. METHODS: Twenty-seven interns specializing in the fields of gynecology, urology and general surgery participated in 4-6 training sessions, in which each trainee cut a circular patch positioned inside a low-cost laparoscopic box trainer simulator. The trainees' performances were analyzed using software that we developed. The analysis of the trainees' performances was based upon quantitative measurements of the following four parameters obtained in each training session: standard deviation, circle-cutout area, skewness, and number of peaks. We believe that high performance in terms of a combination of the four parameters provides a reliable measure of good laparoscopic skills, and therefore we developed the software so as to generate, for each session, a score of a trainee's laparoscopic circle-cutout performance that results from achievements related to the four parameters in combination. RESULTS: On average, the total score of experienced interns was higher than the total score of inexperienced interns. Also, some improvement from session-to-session in the scores of novice trainees was detected. CONCLUSIONS: Our proposed scoring system, which is based on various image-processing algorithms, can evaluate cutting performances of trainees and classify residents by their experience. This allows each trainee to improve his/her performance by analyzing errors indicated by our software.