162
Letters to the Editor
[9] Mehran R, Rao SV, Bhatt DL, et al. Standardized bleeding definitions for cardiovascular clinical trials: a consensus report from the Bleeding Academic Research Consortium. Circulation 2011;123:2736–47. [10] Mehran R, Pocock SJ, Nikolsky E, et al. A risk score to predict bleeding in patients with acute coronary syndromes. J Am Coll Cardiol 2010;55:2556–66. [11] Matic DM, Milasinovic DG, Asanin MR, et al. Prognostic implications of bleeding measured by Bleeding Academic Research Consortium (BARC) categorisation in patients undergoing primary percutaneous coronary intervention. Heart 2014;100:146–52.
[12] Moscucci M, Fox KA, Cannon CP, et al. Predictors of major bleeding in acute coronary syndromes: the Global Registry of Acute Coronary Events (GRACE). Eur Heart J 2003;24:1815–23. [13] Hochholzer W, Wiviott SD, Antman EM, et al. Predictors of bleeding and time dependence of association of bleeding with mortality: insights from the Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With Prasugrel–Thrombolysis in Myocardial Infarction 38 (TRITON-TIMI 38. Circulation 2011;123:2681–9.
http://dx.doi.org/10.1016/j.ijcard.2014.03.161 0167-5273/© 2014 Published by Elsevier Ireland Ltd.
Working hours, sleep duration and risk of coronary heart disease Tomoyuki Kawada ⁎ Department of Hygiene and Public Health, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-Ku, Tokyo 113-8602, Japan
a r t i c l e
i n f o
Article history: Received 11 February 2014 Accepted 26 March 2014 Available online 2 April 2014 Keywords: Working hours Sleep duration Coronary heart disease Risk assessment
To the Editor: Cheng et al. reported that long working hours and short sleep duration contribute independently to the risk of coronary heart disease (CHD) and acute myocardial infarction (AMI) in middle-aged men by case–control (1:2) study with the adjustment of psychosocial work-related factors [1]. In their study, the authors used conditional logistic regression analysis for the calculation of odds ratios of long working hours and short sleep duration. The authors clearly described the advantage and limitation of their study, and I fundamentally agree to their study outcome. I have some concerns on their statistical procedure though. First, the authors presented characteristics of cases and controls in their Table 1. They categorized sleep duration into three groups: b6 h/ day, 6–≤ 8 h/day and N8 h/day. The percentage of subjects who slept 8 h or longer was under 5%. The category of sleep duration in Tables 2 and 3 was changed from 8 to 9 h/day. Is this a simple mistake? Second, 93.9% of controls sleep for 6–8 ≤ 8 h/day, and 5.3% of controls work over 60 h/week. From these data, I understand the limitation of setting categories of sleeping duration and working hours for their analysis. But the authors mentioned that working hours and sleep duration were obtained by a standardized
⁎ Tel.: + 81 3 3822 2131; fax: +81 3 5685 3065. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.ijcard.2014.03.172 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
questionnaire. Please explain the reason of adopting these cut-off points. As the third concern, the authors collected 322 CHD cases, and they diagnosed 134 cases as AMI. They conducted conditional logistic regression analysis by using at least 16 independent variables in their model 2. In general, 10 cases are required for each independent variable in unconditional logistic regression analysis [2,3]. Although the authors adopted conditional logistic regression analysis to avoid “sparse-data” biases, AMI analysis should be handled with caution [4,5]. Finally, the authors cannot explain the interaction between long working hours and short sleep duration in their study, because they simply used two variables, working hours and sleep duration, independently. Before considering interaction, I also recommend the authors to present a simple relationship between sleep duration and working hours. Anyway, odds ratios of long working hours and short sleep duration for CHD and AMI were larger than that of a current smoker. If the authors specify non-smoker as a control, are there any change of statistical significance in odds ratio of a current smoker? I wish to express my appreciation to the members of Hygiene and Public Health, Nippon Medical School, for the preparation of this study. The author of this manuscript has certified that he complies with the principles of ethical publishing in the International Journal of Cardiology. References [1] Cheng Y, Du CL, Hwang JJ, Chen IS, Chen MF, Su TC. Working hours, sleep duration and the risk of acute coronary heart disease: a case–control study of middle-aged men in Taiwan. Int J Cardiol 2014;171:419–22. [2] Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373–9. [3] Novikov I, Fund N, Freedman LS. A modified approach to estimating sample size for simple logistic regression with one continuous covariate. Stat Med 2010;29:97–107. [4] Greenland S, Schwartzbaum JA, Finkle WD. Problems due to small samples and sparse data in conditional logistic regression analysis. Am J Epidemiol 2000;151:531–9. [5] Hamajima N, Hirose K, Inoue M, Takezaki T, Kuroishi T, Tajima K. Case–control studies: matched controls or all available controls? J Clin Epidemiol 1994;47:971–5.
Letters to the Editor
[9] Mehran R, Rao SV, Bhatt DL, et al. Standardized bleeding definitions for cardiovascular clinical trials: a consensus report from the Bleeding Academic Research Consortium. Circulation 2011;123:2736–47. [10] Mehran R, Pocock SJ, Nikolsky E, et al. A risk score to predict bleeding in patients with acute coronary syndromes. J Am Coll Cardiol 2010;55:2556–66. [11] Matic DM, Milasinovic DG, Asanin MR, et al. Prognostic implications of bleeding measured by Bleeding Academic Research Consortium (BARC) categorisation in patients undergoing primary percutaneous coronary intervention. Heart 2014;100:146–52.
[12] Moscucci M, Fox KA, Cannon CP, et al. Predictors of major bleeding in acute coronary syndromes: the Global Registry of Acute Coronary Events (GRACE). Eur Heart J 2003;24:1815–23. [13] Hochholzer W, Wiviott SD, Antman EM, et al. Predictors of bleeding and time dependence of association of bleeding with mortality: insights from the Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With Prasugrel–Thrombolysis in Myocardial Infarction 38 (TRITON-TIMI 38. Circulation 2011;123:2681–9.
http://dx.doi.org/10.1016/j.ijcard.2014.03.161 0167-5273/© 2014 Published by Elsevier Ireland Ltd.
Working hours, sleep duration and risk of coronary heart disease Tomoyuki Kawada ⁎ Department of Hygiene and Public Health, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-Ku, Tokyo 113-8602, Japan
a r t i c l e
i n f o
Article history: Received 11 February 2014 Accepted 26 March 2014 Available online 2 April 2014 Keywords: Working hours Sleep duration Coronary heart disease Risk assessment
To the Editor: Cheng et al. reported that long working hours and short sleep duration contribute independently to the risk of coronary heart disease (CHD) and acute myocardial infarction (AMI) in middle-aged men by case–control (1:2) study with the adjustment of psychosocial work-related factors [1]. In their study, the authors used conditional logistic regression analysis for the calculation of odds ratios of long working hours and short sleep duration. The authors clearly described the advantage and limitation of their study, and I fundamentally agree to their study outcome. I have some concerns on their statistical procedure though. First, the authors presented characteristics of cases and controls in their Table 1. They categorized sleep duration into three groups: b6 h/ day, 6–≤ 8 h/day and N8 h/day. The percentage of subjects who slept 8 h or longer was under 5%. The category of sleep duration in Tables 2 and 3 was changed from 8 to 9 h/day. Is this a simple mistake? Second, 93.9% of controls sleep for 6–8 ≤ 8 h/day, and 5.3% of controls work over 60 h/week. From these data, I understand the limitation of setting categories of sleeping duration and working hours for their analysis. But the authors mentioned that working hours and sleep duration were obtained by a standardized
⁎ Tel.: + 81 3 3822 2131; fax: +81 3 5685 3065. E-mail address: [email protected].
http://dx.doi.org/10.1016/j.ijcard.2014.03.172 0167-5273/© 2014 Elsevier Ireland Ltd. All rights reserved.
questionnaire. Please explain the reason of adopting these cut-off points. As the third concern, the authors collected 322 CHD cases, and they diagnosed 134 cases as AMI. They conducted conditional logistic regression analysis by using at least 16 independent variables in their model 2. In general, 10 cases are required for each independent variable in unconditional logistic regression analysis [2,3]. Although the authors adopted conditional logistic regression analysis to avoid “sparse-data” biases, AMI analysis should be handled with caution [4,5]. Finally, the authors cannot explain the interaction between long working hours and short sleep duration in their study, because they simply used two variables, working hours and sleep duration, independently. Before considering interaction, I also recommend the authors to present a simple relationship between sleep duration and working hours. Anyway, odds ratios of long working hours and short sleep duration for CHD and AMI were larger than that of a current smoker. If the authors specify non-smoker as a control, are there any change of statistical significance in odds ratio of a current smoker? I wish to express my appreciation to the members of Hygiene and Public Health, Nippon Medical School, for the preparation of this study. The author of this manuscript has certified that he complies with the principles of ethical publishing in the International Journal of Cardiology. References [1] Cheng Y, Du CL, Hwang JJ, Chen IS, Chen MF, Su TC. Working hours, sleep duration and the risk of acute coronary heart disease: a case–control study of middle-aged men in Taiwan. Int J Cardiol 2014;171:419–22. [2] Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:1373–9. [3] Novikov I, Fund N, Freedman LS. A modified approach to estimating sample size for simple logistic regression with one continuous covariate. Stat Med 2010;29:97–107. [4] Greenland S, Schwartzbaum JA, Finkle WD. Problems due to small samples and sparse data in conditional logistic regression analysis. Am J Epidemiol 2000;151:531–9. [5] Hamajima N, Hirose K, Inoue M, Takezaki T, Kuroishi T, Tajima K. Case–control studies: matched controls or all available controls? J Clin Epidemiol 1994;47:971–5.
