Categorizing BMI may lead to biased results in studies investigating in-hospital mortality after isolated CABG

被引:68
作者
Filardo, Giovanni
Hamilton, Cody
Hamman, Baron
Ng, Hon K. T.
Grayburn, Paul
机构
[1] Baylor Hlth Care Syst, Inst Hlth Care Res & Improvement, Dallas, TX 75206 USA
[2] So Methodist Univ, Dept Stat Sci, Dallas, TX 75275 USA
[3] Baylor Univ, Ctr Med, Cardiol Res Clin, Dallas, TX USA
[4] Baylor Univ, Ctr Med, Baylor Heart & Vasc Inst, Dallas, TX USA
关键词
body mass index; coronary artery bypass; postoperative adverse outcomes; in-hospital mortality; epidemiologic methods; smoothing methods;
D O I
10.1016/j.jclinepi.2007.01.008
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objective: To investigate how categorizing body mass index (BMI) into weight classes can impact the assessment of the relationship between BMI and in-hospital mortality after coronary artery bypass graft (CABG) surgery. Study Design and Setting: BMI-mortality (in-hospital) relationship was assessed in 5,762 patients who underwent isolated CABG at Baylor University Medical Center (Dallas, TX) from January 1, 1997 to November 30, 2003. Different ways of modeling BMI were used to investigate this association in a propensity-adjusted model, controlling for risk factors identified by the Society of Thoracic Surgeons (STS) and other clinical/nonclinical details. Results: A highly significant (P = 0.003) association between BMI (modeled with a restricted cubic spline) and mortality was found. Reduced risk of in-hospital mortality was observed for subjects with BMI in the low-30s as compared with patients with BMI in the mid20s or over 40 kg/m(2). Results were strongly affected by the way BMI was specified in the multivariable model. Only five of the 10 BMI categorizations considered produced significant results, and these results did not fully determine the effect of BMI on mortality. Conclusions: BMI categorization critically impacts study results. Conceivably, findings of other studies investigating BMI and adverse outcomes after CABG may be similarly affected. Investigators should consider smoothing techniques to avoid categorization. (C) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:1132 / 1139
页数:8
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