CLINICAL RESEARCH: CORONARY SURGERY
Effect of body mass index on early outcomes in patients undergoing coronary artery bypass surgery
Barnaby C. Reeves, DPhil*,
Raimondo Ascione, MD ,
Martin H. Chamberlain, FRCS and
Gianni D. Angelini, FRCS,*
* Health Services Research Unit, London School of Hygiene & Tropical Medicine, London, England, United Kingdom
Bristol Heart Institute, University of Bristol, Bristol, England, United Kingdom
Manuscript received August 30, 2002;
revised manuscript received March 20, 2003,
accepted April 17, 2003.
* Reprint requests and correspondence: Prof. Gianni D. Angelini, Bristol Heart Institute, University of Bristol, Bristol Royal Infirmary, Bristol BS2 8HW, England United Kingdom.
g.d.angelini{at}bristol.ac.uk
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Abstract
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OBJECTIVES: This study sought to quantify the effect of body mass index (BMI) on early clinical outcomes following coronary artery bypass grafting (CABG).
BACKGROUND: Obesity is considered a risk factor for postoperative morbidity and mortality after cardiac surgery, although existing evidence is contradictory.
METHODS: A concurrent cohort study of consecutive patients undergoing CABG from April 1996 to September 2001 was carried out. Main outcomes were early death; perioperative myocardial infarction; infective, respiratory, renal, and neurological complications; transfusion; duration of ventilation, intensive care unit, and hospital stay. Multivariable analyses compared the risk of outcomes between five different BMI groups after adjusting for case-mix.
RESULTS: Out of 4,372 patients, 3.0% were underweight (BMI <20 kg/m2), 26.7% had a normal weight (BMI  20 and <25 kg/m2), 49.7% were overweight (BMI 25 and <30 kg/m2), 17.1% obese (BMI 30 and <35 kg/m2) and 3.6% severely obese (BMI 35 kg/m2). Compared with the normal weight group, the overweight and obese groups included more women, diabetics, and hypertensives, but fewer patients with severe ischemic heart disease and poor ventricular function. Underweight patients were more likely than normal weight patients to die in hospital (odds ratio [OR] = 4.0, 95% CI 1.4 to 11.1), have a renal complication (OR = 1.9, 95% confidence interval [CI] 1.0 to 3.7), or stay in hospital longer (>7 days) (OR = 1.7, 95% CI 1.1 to 2.5). Overweight, obese, and severely obese patients were not at higher risk of adverse outcomes than normal weight patients, and were less likely than normal weight patients to require transfusion (ORs from 0.42 to 0.86).
CONCLUSIONS: Underweight patients undergoing CABG have a higher risk of death or complications than normal weight patients. Obesity does not affect the risk of perioperative death and other adverse outcomes compared to normal weight, yet obese patients appear less likely to be selected for surgery than normal weight patients.
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Abbreviations and Acronyms
| | ACT | | activated clotting time | | BMI | | body mass index | | CABG | | coronary artery bypass grafting | | CI | | confidence interval | | HDU | | high dependency unit | | ICU | | intensive care unit | | OR | | odds ratio |
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Over recent decades, improvement of socioeconomic conditions has led to an expansion of the overweight population worldwide. Obesity is well known to be a risk factor for the development of diabetes mellitus, hypertension, and coronary artery disease (1–3). It is also thought to be a risk factor for perioperative morbidity and mortality with cardiac surgery, evidenced by its inclusion in the Parsonnet system for stratification of risk for perioperative death (4). The latest analysis of the National Cardiac Surgery Database of the Society of Thoracic Surgeons, using data from coronary artery bypass grafting (CABG) operations in over 300,000 patients, indicated that morbid obesity remains an independent predictor of increased operative mortality in patients undergoing CABG (5).
Most studies assessing the effect of body mass index (BMI) on early clinical outcome after CABG have compared hospital mortality and morbidity between obese and non-obese patients (6,7). Grouping patients in this way means that each group includes patients with widely varying BMIs, which, in turn, may mask the true effects of varying BMI on outcome (8). Many previous attempts to address this problem have been limited by small sample sizes or a lack of data about potentially confounding factors (6–8).
The aim of this paper is to study the effect of varying BMI on early clinical outcome in patients undergoing CABG. The use in our institution of a high-quality prospective database to document all patients undergoing coronary revascularization, including data about potentially confounding factors, created the opportunity to address this research question using a cohort study design without the limitations experienced by previous researchers (6–8).
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Methods
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Patient selection and data collection.
Standard data are collected prospectively for all patients undergoing CABG at our institution. The data collection form includes five sections that are filled in consecutively by anesthetist, surgeon, intensive care unit (ICU), high dependency unit (HDU), and ward nurses. Data are entered into a database (Patient Analysis & Tracking System, Dendrite Clinical Systems, London, United Kingdom). For this study, data were extracted from the database for consecutive patients who had undergone CABG between April 1996 and September 2001. During this period, some surgeons adopted an off-pump surgical technique for many patients; all patients, whether undergoing surgery with cardiopulmonary bypass or off-pump, are included.
Cardiac catheterization was performed using standard methods during the course of routine clinical care. Angiography reports were reviewed before surgery to assess the severity of coronary artery disease, expressed as the number of diseased vessels. Priority of surgery was assessed by the cardiothoracic surgeon and was defined as follows: emergency (the surgery should be performed within hours to prevent morbidity or death), urgent (medical factors require the patient to stay in hospital waiting for an operation), elective (the clinical status of the patient allows discharge from hospital with readmission for surgery at a later date).
Surgical technique and postoperative management.
Anesthetic and surgical techniques were standardized for all patients and have been previously reported (9,10). At the end of surgery, patients were transferred to the ICU and were extubated as soon as they met the following criteria: hemodynamic stability, no excessive bleeding (<80 ml/h), normothermia, and consciousness with pain control. Postoperatively, fluid management and electrolyte deficiency were managed as previously reported (9).
Clinical data collection, monitoring, and definitions.
Data were entered prospectively into the Patient Analysis & Tracking System. The research question was posed after the collection of the data but before any data analysis. Perioperative death was defined as any death occurring within 30 days of operation (regardless of where the death occurred) or in hospital (in patients who had not been discharged following the operation). Perioperative myocardial infarction, ST segment changes, pacing, arrhythmias, and inotropic requirement were recorded and defined as previously reported (9). Pulmonary complication included chest infection, ventilation failure, reintubation, and tracheostomy. Postoperative blood loss was defined as total chest tube drainage (11). Neurological complication included permanent and transient stroke (12) and Glasgow Coma Score <15 without sedation. Renal complication included postoperative creatinine >200 µmol/l and acute renal failure as defined by the requirement of hemodialysis. Finally, infective complication included septicemia and sternal and leg wound infection as defined by positive culture and requiring antibiotic therapy (9).
Statistical analysis.
Of the commonly used measures of obesity, BMI (defined as kg/m2) is the body size measurement that best correlates with body fat content (13). Body mass index values were categorized into one of five groups: underweight (<20 kg/m2); normal weight (20 and <25 kg/m2); overweight (25 and <30 kg/m2); obese (30 and <35 kg/m2); severely obese (35 kg/m2). These groups were based, in part, on criteria specified by the American Heart Association guidelines for defining overweight (3). Additional groups—i.e., the underweight, normal weight, and severely obese—were included to characterize the entire BMI range. We chose not to analyze BMI as a continuous variable because this would have required making assumptions about underlying relationships between BMI and the clinical outcomes investigated in order to carry out multivariable modeling.
Continuously or discretely measured prognostic variables (age, Parsonnet score, number of grafts) and outcomes (blood loss; red blood cell, platelet, and fresh frozen plasma transfusion; postoperative hemoglobin levels; duration of ventilation, ICU stay, combined ICU and HDU stay, and total postoperative stay) were also grouped for analysis. All continuously or discretely measured outcomes were dichotomized for multivariable analyses.
Comparisons of the distributions of prognostic factors among BMI groups were carried out using chi-square or Fisher's exact tests when one or more expected frequency was <5. All comparisons of outcome among BMI groups were carried out using logistic regression modeling to take into account the consultant team responsible for a patient. (The four consultant teams were modeled as "fixed" effects using indicator variables.) Likelihood ratio tests were used to determine the probability of overall distributions of outcomes across BMI groups having arisen by chance. Regression models also estimated odds ratios (ORs) and 95% confidence intervals for the effects of underweight, overweight, obesity, and severe obesity compared with normal weight on the outcomes of interest.
Five propensity scores (describing the probability of classification into each BMI group as a function of all prognostic factors and year of operation) were derived using polytomous logistic regression, to take account of imbalances in the distribution of prognostic factors between BMI groups (14). Logistic regression models were then fitted including the five propensity scores divided into quintiles, consultant team, and BMI group. Additional models, including key prognostic factors as well as the propensity scores, were investigated, but they are not reported here because they did not alter the estimates of effect of BMI group (15). The large number of prognostic variables and outcomes of interest resulted in many statistical comparisons. No correction was made for multiple comparisons. Our interpretation of the findings is based on the consistency of the findings and their magnitude, as well as their statistical significance. All analyses were carried out using STATA version 7 (Stata Corporation, College Station, Texas).
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Results
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A total of 4,467 patients underwent CABG (25.3% off-pump surgery) during the study period, but BMI was missing or invalid for 95 (2.1%). Of the remaining 4,372, 133 (3.0%) were classified as underweight, 1,166 (26.7%) as normal weight, 2,170 (49.7%) as overweight, 747 (17.1%) as obese, and 156 (3.6%) as severely obese. Data for prognostic factors were missing for a small number of patients (<1.0% of patients for any single prognostic factor), preventing the calculation of propensity scores for 112 (2.6%) of the 4,372 patients. Outcomes were also occasionally missing (<1.2% of patients for any single outcome, except for blood loss and transfusion requirements; these latter data were not collected from April 1996 to March 1997 but were available for >97.5% of patients undergoing CABG from April 1997).
Comparisons of risk factors between BMI groups.
The distributions of a wide range of prognostic characteristics in the five BMI groups are shown in Table 1. It is striking that the majority of prognostic factors were distributed unequally across BMI groups. Some variables showed a steady change across groups, with the overweight and obese groups having a more favorable risk profile—e.g., fewer older patients, fewer patients with left main stem stenosis >50%, lower Parsonnet score, higher ejection fraction, less extensive coronary disease (trend tests across BMI groups all significant p < 0.001). Only two variables, the proportion of females and the prevalence of hypercholesterolemia, showed a clear trend in the opposite direction, i.e., the frequency of the risk factor increased with increasing obesity (trend test p < 0.001). Some variables had inverted U-shape distributions, with risk factors being more common at both extremes of BMI—e.g., prevalence of unstable angina, diabetes, and hypertension. Two risk factors, creatinine >200 µmol/l and prevalence of chronic obstructive airways disease, were more prevalent in the underweight group and appeared similarly distributed across the other groups (trend tests p < 0.001 and p = 0.13, respectively).
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Table 1 Distribution of Prognostic Variables in Underweight, Normal Weight and Overweight Groups of Increasing BMI at Admission
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Comparisons of early clinical outcomes between BMI groups.
The distributions of early clinical outcomes are shown in Table 2. Likelihood tests showed that several outcomes were distributed unequally between BMI groups. Odds ratios for each outcome for different BMI groups relative to the normal weight group are tabulated in Table 3, and key outcomes are shown graphically in the figures (Fig. 1: death, complications, and blood loss; Fig. 2: markers of delayed recovery following surgery). The figures show ORs before and after taking account of imbalances in prognostic factors between groups using the propensity scores. Adjusted estimates tended to be closer to the line of no effect (OR = 1), but it is notable that the overall profiles of the likelihood of outcomes as a function of BMI were largely unaltered by adjustment.
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Table 2 Distributions of Early Clinical Outcomes in Underweight, Normal Weight and Overweight Groups of Increasing BMI
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Table 3 Adjusted ORs for Early Clinical Outcomes in Underweight, Normal Weight and Overweight Groups of Increasing BMI
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Figure 1 The effect of body mass index (BMI) on perioperative death, postoperative complications, and postoperative blood loss following coronary artery bypass grafting. Unadjusted (closed circles) and adjusted (open circles) odds ratios and 95% confidence intervals for underweight and overweight groups of increasing BMI compared with a normal weight group. *For hospital death, the two highest BMI categories were combined for analysis.
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Figure 2 The effect of body mass index (BMI) on markers of delayed postoperative recovery following coronary artery bypass grafting. Unadjusted (closed circle) and adjusted (open circle) odds ratios and 95% confidence intervals for underweight and overweight groups of increasing BMI compared with a normal weight group.
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The likelihood of complications as a function of BMI in the underweight, overweight, obese, and severely obese groups tended either to be uniform and not significantly different compared to the normal weight group (e.g., neurological complication, postoperative chest infection) or to be raised for the underweight group and not significantly different from the normal weight group for overweight and obese groups (e.g., perioperative death, need for an intra-aortic balloon pump) (Fig. 1, Table 3). The latter type of profile across BMI groups was also shown for perioperative myocardial infarction and postoperative renal complication (Table 3), but the OR estimates for the underweight group, although substantially higher than unity (1.92 and 1.58, respectively) were not significantly different from unity (OR = 1). These comparisons had low power to detect differences between groups because of the paucity of events.
Odds ratios for blood loss and transfusion requirement across BMI groups (Fig. 1, Table 3) show that overweight and obese patients tend to be protected against these outcomes compared with normal weight patients, with underweight patients having the same odds of these outcomes as normal weight patients. This tendency was clearly significant for postoperative blood loss >1,000 ml and the need for transfusion of platelets or fresh frozen plasma. The finding that obese patients were more likely to have a postoperative hemoglobin level 10 mg/dl was consistent with the lower odds of transfusion of platelets or fresh frozen plasma in these patients.
With respect to indicators of overall recovery, underweight patients appeared to recover less quickly than normal weight patients. They were more likely to be ventilated for >10 h, tended to require an ICU stay >1 night, and were more likely to stay in hospital >7 days. There was a suggestion from the ORs for these outcomes that obese and severely obese patients were also more likely than normal weight patients to stay in hospital >7 days.
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Discussion
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Our study has two main findings. First, obese patients do not experience greater morbidity and mortality than normal weight patients after CABG after taking account of imbalances in key prognostic factors. The overweight group did not have significantly worse outcomes than the normal weight group for any of the adverse outcomes studied. Obese and severely obese groups fared worse than the normal weight group only with respect to the likelihood of staying in hospital >7 days after the operation. In contrast, underweight patients appeared to have a higher risk of death and common complications and to recover more slowly.
Second, in our institution, obese patients with severe ischemic heart disease (e.g., extent of coronary disease, ejection fraction, left main stem stenosis; Table 1) are relatively under-represented among patients undergoing CABG. Conversely, there appears to be a relative excess of low-weight patients with severe ischemic heart disease and important comorbidities who needed urgent or emergency CABG.
Study limitations.
Without blinding, assessment of some outcomes could have been biased by knowledge of the BMI of patients, e.g., obese patients may have been kept in hospital longer as a precaution. In our institution, strict local guidelines are used to make decisions about perioperative patient care management; these guidelines were applied carefully throughout the period of the study and minimized the susceptibility of outcomes to bias. Moreover, given the prevailing view that obesity is a risk factor for poor outcome (4,5,16), such biases would have resulted in the overweight and obese groups appearing to be more at risk than in fact they were. Therefore, bias cannot explain why we did not observe an increase in the risk of mortality and morbidity in overweight and obese patients, although it may explain why obese and severely obese patients were more likely to stay in hospital >7 days.
The BMI groups consisted of patients with, on average, different risk profiles. Multiple regression modeling can never entirely account for these differences, and adjusted effects may still be influenced by residual confounding. However, if confounding was a serious problem, one would expect unadjusted and adjusted estimates of the ORs to differ quite markedly. Moreover, the risk of adverse outcomes was consistently lower or not significantly different in overweight and obese groups compared with the normal weight group. The protective effects against mortality and morbidity observed in obese patients may be optimistic, but it is unlikely that residual confounding could reverse the direction of these effects.
We are uncertain about the mechanism underlying the apparent increase in the risk of poorer outcomes for underweight patients. The underweight group had, on average, worse left ventricular function, was older, and had a higher percentage of patients with diabetes and chronic obstructive airways disease than the normal weight group. Statistical adjustment for these differences may have been incomplete.
Variation in the risk of adverse outcomes for different BMI groups.
Many studies either have dichotomized patients into obese and non-obese groups, with varying cut-off criteria for defining obesity (4–7,17), or have divided patients into more than two groups on the basis of observed centiles (18,19). Studies have also used different criteria for inclusion; one considered only elective patients (7), and three studies included patients undergoing heart valve repair as well as CABG (4,6,20). With respect to the varying risk of adverse outcomes for different BMI groups, however, it is clear that ours is not the first study to contradict the prevailing view that obesity is a risk factor for operative mortality and morbidity following CABG (6,7,17,19) or to conclude that obesity may be protective against some adverse outcomes (8,18).
We identified only one previous study that estimated the risk of operative morbidity and mortality in a defined "low weight" group, i.e., separating a "lower-than-normal" weight group (BMI <20 kg/m2) from a normal weight group (BMI 20 and <25 kg/m2) (20). This study also found that underweight patients had an increased risk of mortality and morbidity. A further study found that "low" BMI was an independent predictor of mortality and morbidity but took no account of differences in case-mix (8). Our study has quantified the independent and important role of low weight (BMI <20 kg/m2) as a predictor of perioperative death and major complications compared with normal weight, after adjusting for case-mix. We conclude that the increased risk of adverse outcomes in lower weight patients is largely confined to those who are underweight.
One of our striking findings was that, compared to the normal weight group, overweight and obese groups were significantly less likely to experience excessive postoperative bleeding (>1,000 ml) and to require transfusion of platelets or fresh frozen plasma. This result is counterintuitive but is consistent with those of other studies (8,18). A further study found that obese patients (>30 kg/m2) had a decreased risk of reexploration for bleeding (20).
Persistence of belief that obesity is a risk factor for mortality and morbidity after CABG.
There are at least three possible reasons obesity is still considered to be an important risk factor for morbidity following CABG: - the "power" and influence of analyses from national databases (5) and perceived methodological problems with single center studies;
- the inclusion of obesity in the Parsonnet score (4);
- the difficulty for physicians in partitioning the complex effects of other risk factors that may often be associated with obesity.
What is clear from our own analysis and those of others is: 1) it is not sufficient simply to compare obese and non-obese groups; and 2) adjustment for case-mix may be critically important, given the imbalances in other important prognostic factors that are often observed with varying BMI. Ironically, obesity may have been erroneously included in the Parsonnet risk scoring system because the coefficient (i.e., ln[odds ratio]) for morbid obesity in the published multiple logistic regression was significantly less than zero (i.e., significantly protective against mortality) (4). It is notable that obesity is included neither in the more recent EuroSCORE risk stratification method (21) nor in the New Zealand priority scoring system (22). Consideration of these factors and findings of previous studies and our own lead us to conclude that obesity is truly not a risk factor for mortality and morbidity following CABG.
Under-representation of obese patients with severe ischemic heart disease.
We found that obese patients undergoing CABG were more likely to be female, diabetic, and hypertensive, as did many other studies (7,8,17,19,20). However, the data for our institution suggest that obese patients accepted for surgery have a more favorable risk profile in other respects, i.e., less extensive coronary disease, better left ventricular function, and less likely to have urgent or emergency surgery compared with normal weight patients.
Other studies have also found major imbalances in the distribution of the extent of coronary disease and comorbidity among BMI groups, with almost every one finding obese patients undergoing CABG or other cardiac surgery procedures to be younger, on average, than non-obese patients (6–8,17,18,20). Some studies have shown similar patterns for other risk factors to the ones we observed (7,8,17,18). Other studies failed to find or did not report major imbalances between obese and non-obese patients in the distribution of the extent of coronary disease (% left main stem stenosis or number of diseased coronary vessels) and left ventricular function (7,16,20).
There are two possible reasons for the imbalances we observed. First, obese patients may die younger without developing severe disease. If so, one would expect similar imbalances across BMI groups in different institutions because the etiology and progression of ischemic heart disease is the same across the developed world. This could explain why obese patients undergoing CABG in our study were, on average, younger than patients with normal weight but not why they had less severe ischemic heart disease. Second, cardiologists or cardiac surgeons may tend to select obese patients for surgery in a different way than they do non-obese patients, i.e., they may be less likely to consider obese than normal weight patients for surgery when they have less favorable risk profiles. Inadvertent discrimination of this kind against obese patients could arise because of the prevailing view that obesity is a risk factor for mortality and morbidity following CABG. If this is the main reason, then differences in imbalances in prognostic factors between institutions might be expected, dependent on the precise criteria used by cardiologists or cardiac surgeons to select patients for surgery.
Conclusions.
Obesity does not increase the risk of perioperative death and other adverse outcomes in patients undergoing CABG. Underweight patients, in contrast, do appear to have a higher risk of death and complications and to recover from surgery more slowly. There is a need to investigate reasons for the increased risk of adverse outcomes in patients who are underweight. Interventions to modify the risks of adverse outcomes should be evaluated by randomized, controlled trials.
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Footnotes
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This study was carried out with the support of funding from the British Heart Foundation and the Garfield Weston Trust.
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Abstract
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