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Ann Thorac Surg 2007;84:720-722
© 2007 The Society of Thoracic Surgeons

Editorials

Obesity and Stroke After Cardiac Surgery: The Impact of Grouping Body Mass Index

Giovanni Filardo, PhD, MPHa,b,*, Cody Hamilton, PhDa, Baron Hamman, MDc, Paul Grayburn, MDd

a Institute for Health Care Research and Improvement, Baylor Research Institute, Dallas, Texas
b Department of Statistical Science, Southern Methodist University, Dallas, Texas
c Cardiology Research Clinic, Dallas, Texas
d Baylor Heart and Vascular Institute, Baylor University Medical Center, Dallas, Texas

* Address correspondence to Dr Filardo, Institute for Health Care Research and Improvement, 8080 N Central Expressway, Suite 500, Dallas, TX 75206 (Email: giovanfi{at}baylorhealth.edu).

Factors associated with morbidity and mortality after isolated coronary artery bypass graft surgery (CABG) have been extensively investigated, yet uncertainty regarding the risk associated with obesity (or cachexia) and a number of postoperative adverse outcomes remains [1–15]. For the most part, research focused on describing the relationship between body mass index (BMI), as a proxy for body fatness, and post-CABG adverse events using a wide variation of pre-defined categorizations (eg, the World Health Organization or the American Heart Association) or using arbitrary BMI categorizations [2–15]. Typically such studies described the relationship between BMI and a broad spectrum of postoperative adverse outcomes (eg, stroke, operative mortality) using the same BMI categorization for all the outcomes [2–15]. However, the shape of the association between BMI and each of the postoperative adverse outcomes is unique, and ignoring this during data analysis (ie, using a single BMI categorization to investigate all adverse outcomes) can critically affect study results [16–19]. Moreover, grouping BMI into classes carries serious dangers in itself, as categorization can bias inference regarding BMI and post-CABG morbidity and mortality [16, 17, 19–21]. We hypothesize that BMI categorization may be one cause of the inconsistent findings regarding the association between obesity and cachexia and adverse outcomes after cardiac surgery.

To investigate this hypothesis, we considered the relationship between BMI and the risk of stroke after CABG. We conducted literature searches using PubMed to gather studies that have reported on this topic. Search strategies were formulated to retrieve records published in English that combined terms related to BMI, isolated CABG, and stroke. Articles listed in the references of the identified reports were also considered for a review of the statistical methods and results (see Table 1). All the identified studies [2–13, 15] investigated the effect of BMI on multiple adverse outcomes in addition to stroke (including mortality, renal failure, deep sternal infection, and extended length of stay), yet reported results were typically based on a single BMI categorization. Categorization schemes used to analyze BMI were found to be very inconsistent across the 13 studies reviewed (Table 1). This inconsistency was especially pronounced in the categorizations used to define BMI classes for extreme values. Results regarding the effect of BMI on the risk of stroke were inconclusive, and in some instances they seemed to conflict across the studies (Table 1).


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  		Table 1 Adjusted Association Between BMI (kg/m2) and Stroke, and BMI Categorization Schemes for Studies Investigating Adverse Operative Outcomes After Coronary Artery Bypass Graft Surgery
 
We used data on all consecutive patients who underwent isolated CABG surgery at Baylor University Medical Center (Dallas, TX) between January 1, 1997 and November 30, 2003 to investigate the association between BMI and risk of stroke. These data are described elsewhere [21]. Preoperative endocarditis, previous valve surgery, minimally invasive procedure, ventricular assist device, or missing BMI (n = 41) resulted in the exclusion of patients. Those patients with missing variables were considered for the present investigation, and multiple imputation was performed for continuous and ordinal missing variables by predictive mean matching [22], as recommended by The Society of Thoracic Surgeons’ workforce evidence-based surgery report [23]. The final study cohort included 5,762 patients (98.11% of the initial population). Stroke was defined as a central neurologic deficit persisting postoperatively for greater than 72 hours as in The Society of Thoracic Surgeons’ adult cardiac database (see http://www.sts.org) [24, 25]. The BMI was investigated without being forced into categories; a smoothing technique (restricted cubic splining [17]) was used instead. A generalized propensity score [26] approach was used to account for possible confounding of the relationship between BMI and stroke in this cohort of patients. The score was created by a linear regression of BMI onto clinical and nonclinical factors considered by The Society of Thoracic Surgeons as risk factors for stroke following CABG (see http://www.sts.org). The complete list of variables included in the propensity model is presented in Figure 1. Among the BMI categorization schemes identified from the literature, the one used by Reeves and colleagues [2] used the largest number of BMI categories, and hence was ostensibly the most flexible (ie, certainly more flexible than simply dichotomizing BMI [eg, at 30 kg/m2]). We contrasted our findings from the smoothed fit with those obtained using the Reeves and colleagues [2] categorization, which we adjusted using the same generalized propensity score.


Figure 1
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  		Fig 1. Propensity-adjusted risk (%) of stroke by body mass index (BMI) (kg/m2) investigated using restricted cubic spline and a selected categorization for 5,762 patients who underwent coronary artery bypass graft surgery at Baylor University Medical Center (Dallas, TX) between January 1997 and November 2003 by BMI. The propensity adjusted model includes the following risk factors: age, gender, race, smoking status, diabetes mellitus, renal failure, hypertension, preoperative creatinine level, chronic lung disease, peripheral vascular disease, history of cerebrovascular disease, previous revascularization, congestive heart failure, days from myocardial infarction (if any), preoperative angina, ejection fraction, number of anastamoses, left main disease, preoperative intraaortic balloon pump, and operative status (elective or not).

 
The median BMI in this cohort of patients was 27.9 kg/m2, and postoperative stroke occurred in 1.9% of the patients (n = 111). The propensity-adjusted model for the smoothed association between BMI and postoperative stroke revealed a moderate effect of BMI on the risk of stroke (p = 0.056). Based on the model using the categorization scheme followed by Reeves and colleagues [2], patients within the ranges of 25 kg/m2 to 29.9 kg/m2 (odds ratio = 0.54; 95% confidence interval: 0.34, 0.85) and 30 kg/m2 to 34.9 kg/m2 (odds ratio = 0.53; 95% confidence interval: 0.30, 0.95) were less likely to experience postoperative stroke compared with patients whose BMIs were within the 20 kg/m2 to 24.9 kg/m2 range. The elevated risk associated with cachexia was not found to be significant likely due to the small number of cachectic patients.

The plot of the smoothed association between BMI and the adjusted predicted stroke risk (Fig 1) showed a U-shaped relationship between BMI and the risk of stroke, with the lowest estimated risk of stroke for subjects with a BMI in the low 30s and sharply increasing stroke risk for subjects with BMI values lower than 30 or higher than the mid-40s. Figure 1 also indicates that the Reeves and colleagues [2] categorization provides a very poor fit to the smoothed risk as the estimated hazard for certain BMI categories (BMI, 30 kg/m2 to 34.9 kg/m2) deviate from the curve representing the BMI and stroke association while forcing entire ranges of BMI to receive the same risk estimate. As a result of the poor fit, inferences regarding risk differences between patients in these BMI groupings will be inaccurate. Furthermore, the categorization produces steep changes in the predicted risk, which would result in conclusions that are clinically illogical. For example, consider 2 patients with BMI equaling 24.99 kg/m2 and 25.01 kg/m2, respectively, which is a difference in BMI of 0.02 kg/m2. Based on the results of the model following the Reeves and colleagues [2] categorization, these 2 patients received quite different risk estimates, despite the almost negligible differences in their BMI values. On the other hand, a patient with a BMI of 24.99 kg/m2 and a patient with a BMI of 20.01 kg/m2 (equaling a difference in BMI of 4.98 kg/m2) receive exactly the same risk estimate, despite having a much larger difference in BMI values. The problem with assigning all patients within each BMI category a single risk estimate is particularly notable in the BMI > 35 kg/m2 category, in which it masks the increasing risk associated with a BMI > 40 kg/m2. As there are more patients with BMI closer to 35 kg/m2 than there are with a BMI > 40 kg/m2, the odds ratio comparing the >35 kg/m2 BMI category to the reference group (BMI 20 kg/m2 to 24.9 kg/m2) is heavily weighted toward the patients with a BMI close to 35 kg/m2. Because these patients have a reduced risk of stroke (as shown in Fig 1), the entire >35 kg/m2 BMI category has a biased (low) estimated risk. Furthermore, conclusions based on the risk estimates associated with the categorization will erroneously indicate that the risk of stroke will decrease with increasing BMI.

Taken together, these results demonstrate how categorizations can "miss-specify" the relationship between BMI and the risk of stroke. Conceivably, this consideration can be extended to any study investigating the effect of BMI through categorization and any adverse cardiac surgical outcome in the literature. In addition to the poor fit that BMI groupings provide, even categorizations that by chance mimic the true association between BMI and surgical risk may miss significant findings due to small patient counts in some of the BMI groups, especially at the extremities of the BMI range. Future research should avoid grouping BMI and other continuous risk factors, such as age or ejection fraction, into categories to allow for a more flexible and unbiased association with surgical risk.


   Acknowledgments
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 Acknowledgments
References
 
We would like to acknowledge the use of software from Prof Frank Harrell (Hmisc and Design Libraries) and thank Briget da Graca for her writing and editorial assistance.


   References
Top
 Acknowledgments
 References
 

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