Percent Body Fat and Prediction of Surgical Site Infection

ORIGINAL SCIENTIFIC ARTICLES Percent Body Fat and Prediction of Surgical Site Infection Emily Waisbren, BS, Heather Rosen, MD, MPH, Angela M Bader, MD, MPH, Stuart R Lipsitz, ScD, Selwyn O Rogers, Jr, MD, MPH, FACS, Elof Eriksson, MD, PhD, FACS Obesity is a risk factor for surgical site infection (SSI) after elective surgery. Body mass index (BMI) is commonly used to define obesity (BMI ⱖ30 kg/m2), but percent body fat (%BF) (obesity is ⬎25%BF [men]; ⬎31%BF [women]) might better predict SSI risk because BMI might not reflect body composition. STUDY DESIGN: This prospective study included 591 elective surgical patients 18 to 64 years of age from September 2008 through February 2009. Height and weight were measured for BMI. %BF was calculated by bioelectrical impedance analysis. Preoperative, operative, and 30-day postoperative data were captured through interviews and chart review. Our primary, predetermined outcomes measurement was SSI as defined by the Center for Disease Control and Prevention. RESULTS: Mean %BF and BMI were 34⫾10 and 29⫾8, respectively. Four-hundred and nine (69%) patients were obese by %BF; 225 (38%) were obese by BMI. SSI developed in 71 (12%) patients. With BMI defining obesity, SSI incidence was 12.3% in nonobese and 11.6% in obese patients (p ⫽ 0.8); Using %BF, SSI occurred in 5.0% of nonobese and 15.2% of obese patients (p ⬍ 0.001). In univariate analyses, significant predictors of SSI were %BF (p ⫽ 0.005), obesity by %BF (p ⬍ 0.001), smoking (p ⫽ 0.002), National Nosocomial Infections Surveillance score (p ⬍ 0.001), postoperative hyperglycemia (p ⫽ 0.03), and anemia (p ⫽ 0.02). In multivariable analysis, obese patients by %BF had a 5-fold higher risk for SSI than nonobese patients (odds ratio ⫽ 5.3; 95% CI, 1.2⫺23.1; p ⫽ 0.03). Linear regression was used to show that there is a positive, nonlinear relationship between %BF and BMI. CONCLUSIONS: Obesity, defined by %BF, is associated with a 5-fold increased SSI risk. This risk increases as %BF increases. %BF is a more sensitive and precise measurement of SSI risk than BMI. Additional studies are required to better understand this relationship. (J Am Coll Surg 2010; 210:381–389. © 2010 by the American College of Surgeons) BACKGROUND: deaths, and add between $4.5 and $5.7 billion per year to health care costs.2,3 The majority of studies on obesity and its risk factors define obesity using body mass index (BMI). In the existing body of literature, obesity, regardless of the definition used, is thought to increase the risk of SSI, possibly because of technical difficulty, altered wound physiology, and impaired immune response.4-6 Because 40% of the elective surgical population is overweight or obese, SSIs that might be a result of an obesity-related risk factor deserve additional scrutiny.7 However, the term obesity is often overused or misused. The Oxford English Dictionary defines obesity as “very fat,” and the World Health Organization defines it as a “condition with excess body fat to the extent that health and well-being are adversely affected.”8 Instead of these colloquial definitions, quantitative tools can be employed to provide an objective measure of obesity. The most common is BMI, which uses an index of weight and height to classify patients into 1 of 3 categories: healthy Surgical site infection (SSI) is defined by the Centers for Disease Control and Prevention as an infection occurring at or near the surgical incision within 30 days of a procedure.1 SSIs occur in approximately 2 million patients each year in the United States, are associated with about 90,000 Disclosure Information: Nothing to disclose. Presented at the American College of Surgeons 95th Annual Clinical Congress, Chicago, IL, October 2009. Received November 6, 2009; Revised December 21, 2009; Accepted January 5, 2010. From the Plastic Surgery Division (Waisbren, Eriksson), Center for Surgery and Public Health (Rosen, Lipsitz, Rogers), Anesthesia and Center for Preoperative Evaluation (Bader), and Division of Trauma, Burn, and Surgical Critical Care (Rogers), Brigham and Women’s Hospital, Boston, MA; and Department of Plastic Surgery, Children’s Hospital Boston, Boston, MA (Rosen). Correspondence address: Elof Eriksson, MD, PhD, Plastic Surgery Division, Brigham and Women’s Hospital, 45 Francis St, Boston, MA 02115. Email: eeriksson@partners.org © 2010 by the American College of Surgeons Published by Elsevier Inc. 381 ISSN 1072-7515/10/$36.00 doi:10.1016/j.jamcollsurg.2010.01.004 382 Waisbren et al Body Fat and Surgical Site Infection Risk Abbreviations and Acronyms BIA %BF BMI SSI ⫽ ⫽ ⫽ ⫽ bioelectrical impedance analysis percent body fat body mass index surgical site infection weight, overweight, or obese. According to the World Health Organization8 and for the purposes of this study, obesity is defined as a BMI ⱖ30. However, BMI has known limitations, such as failure to distinguish fat mass from lean mass.9 The ability of BMI to correctly identify obesity in a population is widely debated.10-14 This study aims to examine a different quantitative tool, percent body fat (%BF), which is measured by bioelectrical impedance analysis (BIA). This measure can be obtained easily and inexpensively by using a standing scale with metal plates onto which each foot is placed. After entering variables such as patient age; height; and weight, %BF, along with a variety of additional variables (eg, total body water, total body fat, fat mass), is calculated within seconds. This information is then stored in a computer spreadsheet with a patient identifier. The implementation of BIA to measure %BF is easy, inexpensive, and accessible, and it is foreseeable that this device will be implemented in clinics everywhere.15,16 The American Council on Exercise defines obesity as men with ⬎25%BF and women with ⬎31%BF. The ability of BMI to identify “fatness” or obesity is based on the assumption that body mass correctly estimates body fat.17 However, in some situations, BMI fails to identify “fatness.” Some individuals, eg, body builders, are overweight for their height but not necessarily “overfat,” and others have a normal BMI but their body fat exceeds what is healthy. %BF can better measure obesity than BMI.18 In this report, we describe a prospective cohort study that uses data to analyze the relationship between %BF and SSI incidence to determine whether %BF is a more accurate predictor than BMI. Our aims were as follows: to define the relationship between obesity and SSI using the quantitative definitions for obesity, including %BF and BMI; to determine the predictive value of %BF for development of SSI; and to characterize the relationship between %BF and BMI. METHODS Patient population After approval from our Institutional Review Board, we culled the daily registry of surgical procedures at the Weiner Center for Preoperative Evaluation at Brigham and Women’s Hospital for patients who met eligibility criteria. Pa- J Am Coll Surg tients were eligible for inclusion in the study if they were between the ages of 18 and 64 years and planning to undergo elective surgery in 1 of 7 categories, ie, general, orthopaedic, cardiothoracic, gynecologic, plastic surgery, otolaryngology, or urology. This particular age group was chosen in an attempt to be consistent with prior studies of using BIA to measure %BF, the majority of which focuses on this mid-range age group, and because we aimed to study as homogenous a group as possible.15,16,18,19 Trauma patients, patients undergoing emergency procedures, and immune-compromised patients (eg, HIV/AIDS infection, organ transplant recipients, and patients receiving corticosteroids or chemotherapy) were excluded. The final cohort was a 591-patient representative sample of the patients seen at the Center from September 2008 through February 2009. This group of patients was selected randomly from the clinic schedule before their appointment. Measurement of adiposity and end points Preoperative data were collected by 1 investigator (EW) using the following procedure: medical record review to determine inclusion/exclusion criteria; administration of a questionnaire through personal interview; and focused physical examination, which included measurement of height and weight (for calculation of BMI) and measurement of %BF by BIA (model TBF310; Tanita). Use of body-fat estimation by BIA has been validated; the correlation between BIA and dual-energy x-ray absorptiometry was 0.85 for men and 0.88 for women. The correlation between fat-free mass by BIA and hydrodensitometry in healthy adults under age 50 has been reported to be 0.98, with a standard error of estimation of 2.29 kg.18,19 The cost of the %BF measuring device was under $2,000 and was very easy to use, similar to a traditional standing scale. It involves the patient standing on the metal foot pads with bare feet and the measurement is printed out and transferred to a computer spreadsheet within seconds. Perioperative variables were collected from the anesthesia record and operative note, and postoperative outcomes variables were collected from the medical record (including laboratory values) and follow-up telephone questionnaire. The primary surgical outcomes of interest included SSI, as defined by Centers for Disease Control and Prevention criteria.1 Secondary outcomes included other wound complications, such as wound dehiscence; fat and skin necrosis; seroma; hematoma; or other perioperative non⫺woundrelated outcomes, such as mortality; myocardial infarction; arrhythmia; pulmonary embolism, deep-vein thrombosis, cerebral vascular accident, acute renal failure, urinary tract infection; pneumonia; sepsis; bacteremia; and Clostridium difficile infection. Vol. 210, No. 4, April 2010 Waisbren et al Body Fat and Surgical Site Infection Risk 383 Table 1. Univariate Analysis for Development of Surgical Site Infection Variable %BF, mean ⫾ SD Obese (%BF), n (%) Current smoker, n (%) Pedal edema, n (%) History of cancer, n (%) Diabetes, n (%) Peripheral vascular disease, n (%) Duration of operation, h, mean ⫾ SD Postoperative length of stay, d, mean ⫾ SD Drain placement, n (%) Wound classification, n (%) Clean Clean-contaminated Contaminated Dirty ASA classification, n (%) I II III IV NNIS score, n (%) 0 1 2 3 Postoperative glucose,* mg/dL, mean ⫾ SD Postoperative hyperglycemia, n (%)† Postoperative hyperglyemia in nondiabetics, n (%) Postoperative anemia‡,§, n (%) History of SSI, n (%) Participants with postoperative SSI (nⴝ71) Participants without postoperative SSI (n ⴝ 520) 37.4 ⫾ 8.8 62 (87.3) 22 (31) 18 (25.4) 38 (53.5) 12 (16.9) 6 (8.5) 3.7 ⫾ 2.9 4.0 ⫾ 4.5 28 (39.4) 33.7 ⫾ 10.5 347 (66.7) 83 (16) 73 (14) 200 (38.5) 49 (9.4) 17 (3.3) 2.7 ⫾ 1.7 2.0 ⫾ 2.8 132 (25.4) 31 (43.7) 36 (50.7) 2 (2.8) 2 (2.8) 303 (58.3) 200 (38.5) 13 (2.5) 4 (0.8) 4 (5.6) 39 (54.9) 28 (39.4) 0 (0) 52 (10) 354 (68.1) 109 (21.0) 5 (1.0) 18 (25.4) 28 (39.4) 24 (33.8) 1 (1.4) 161.6 ⫾ 57.8 38 (84.4) 29 (82.9) 42 (59.2) 5 (7.0) 250 (48.1) 210 (40.4) 55 (10.6) 5 (1.0) 142.2 ⫾ 41.5 170 (68.8) 132 (63.8) 233 (44.8) 7 (1.4) p Value 0.005 ⬍0.001 0.002 0.01 0.02 0.05 0.03 ⬍0.001 ⬍0.001 0.01 0.04 0.01 ⬍0.001 0.007 0.03 0.03 0.02 0.001 All variables were included in the stepwise multivariate analysis. *Postoperative glucose recorded in 298 patients. † Fasting glucose ⬎120 mg/dL. ‡ Hematocrit ⬍36 women, ⬍41 men. § Postoperative hematocrit recorded in 286 patients. ASA, American Society of Anesthesiologists; %BF, percent body fat; NNIS, National Nosocomial Infections Surveillance; SSI, surgical site infection. Statistical analysis We consulted with the National Surgical Quality Improvement Program at our institution. Their data were used to guide our sample size calculations. With at least 500 total patients and at least one-third of them nonobese (determined by BMI or %BF), this study is designed to have 80% power to detect an odds ratio of at least 2.3 between SSI and obesity using multivariable logistic regression with a type I error of 5%; this calculation assumed a coefficient of determination of 0.3 between the dichotomous variable obesity and all possible confounders.20 Patient characteristics and study results were summarized using proportions, means with standard deviations, and medians with interquartile ranges. Wilcoxon rank sum tests and t-tests (where appropriate) were used for comparing continuous variables between 2 groups. For dichotomous and categorical variables, chi-square and Fisher’s exact tests (where appropriate) were used to determine differences in proportions between 2 groups.The main a priori confounders, which were identified through a literature search before data collection, are given in Table 1. To determine if these possible confounders are related to the SSI, the Wilcoxon rank sum test was used for comparing continuous confounders between patients with and without SSI; similarly, Fisher’s exact tests were used to determine differences in proportions between patients with and without SSI. 384 Waisbren et al Body Fat and Surgical Site Infection Risk Multivariable logistic regression was used to determine if obesity (defined either by BMI or %BF) was an important predictor of SSI, while controlling for potential confounders. Forward selection was used to identify potential confounders. When added to the logistic regression model in the forward selection, any potential confounder was kept in the multivariable model if it led to a ⱖ10% change in the odds ratio estimate between outcomes and obesity. Once a confounder entered the model in the forward selection, it was not dropped out afterward. Diabetes mellitus was kept in the multivariable model because all authors agreed that it was most likely the strongest confounder. Linear regression was used to characterize the relationship between %BF and BMI. We report odds ratios, p values, and 95% confidence intervals from the logistic regressions. After finding the best-fitting logistic regression model, adjusted probabilities of SSI (adjusted for significant confounders) were calculated and plotted as a function of obesity. Statistical analyses were performed using Stata/IC (version 10, Stata Corp). All p values were 2-sided. RESULTS Clinical characteristics Of the 591 recruited participants, 12 were excluded from follow-up because of cancellation or deferral of surgery. The postoperative telephone questionnaire was completed by 586 of 591 patients (99%). Preoperative characteristics and operative variables for the final cohort are shown in Table 2. A total of 68% of participants were defined as “relatively healthy” at baseline, based on the American Society of Anesthesiologists score of 2. Most surgical wounds were classified as “clean” or “clean-contaminated.” Of the surgical specialties included, the majority of participants underwent general (26%, n ⫽ 153) and orthopaedic surgery (18%, n ⫽ 107). One-hundred and nine participants (18%) underwent laparoscopic procedures, 28 (4.7%) underwent bariatric surgery, and permanent implants were placed in 165 (28%) of the patients. Pre- and postoperative laboratory data were available for a subgroup of patients. Analysis of serum laboratory values showed no statistically significant difference in preoperative glucose levels in SSI and non-SSI patients (p ⫽ 0.15). However, those in whom SSI developed were more commonly hyperglycemic (glucose level ⬎120 mg/dL) after the operation than those in whom SSI did not develop (84% of SSI patients versus 69% of non-SSI patients; p ⫽ 0.03). Nondiabetics in whom SSI developed had a higher incidence of hyperglycemia than those in whom SSD did not develop (83% versus 64%; p ⫽ 0.03). Of 286 patients for whom a postoperative hematocrit was recorded, those in whom SSI developed had a higher incidence of postoperative anemia J Am Coll Surg (women with hematocrit ⬍36 and men with hematocrit ⬍41) than non-SSI patients (59% versus 45%; p ⫽ 0.02). Nonobese patients by %BF were younger than obese patients by %BF, but the ages were the same when obese versus nonobese patients by BMI. Additionally, there were more obese women (%BF) than the nonobese group (%BF), but there was no difference in gender between obese and nonobese patients when obesity was measured using BMI. SSI and postoperative complications In the 30-day postoperative follow-up period, complications were experienced by 127 (25%) participants, among whom SSI was identified in 71 (12%). Eighty percent of SSIs were classified as superficial incisional infections. SSI was most common after oncologic (23% of oncologic procedures resulted in SSI) and cardiothoracic (17% of cardiothoracic procedures resulted in SSI) operations. Gynecologic surgery had the lowest rate of SSI (8% of gynecologic procedures resulted in SSI). There was no statistically significant difference in the incidence of SSI between men and women (p ⫽ 0.36). Wound-related complications, including dehiscence, necrosis, and seroma or hematoma formation occurred in 80 (13.5%) participants and 57 (9.6%) experienced non– wound-related complications. Wound intervention was required in 22 (3.7%) participants, and the incidence of hospital readmission was 5%. SSI and obesity There was a positive, nonlinear relationship between %BF and BMI (BMI and BMI2 were both significant at p ⬍ 0.001 in a linear regression model with %BF as one of the outcomes). %BF identified more patients as obese (69% of the cohort) than did BMI (38% of the cohort). Patients in whom SSI developed had a significantly higher %BF than those in whom SSI did not develop (37 ⫾ 9 versus 34 ⫾ 11; p ⫽ 0.005). More patients in the SSI group than in the non-SSI group were obese by %BF (87% versus 67%; p ⬍ 0.001). There was no statistically significant difference in the percentage of obese patients as defined by BMI in those with and without SSI (p ⫽ 0.79). Additionally, there was no significant difference in the incidence of SSI between the obese and nonobese BMI groups (12% of patients versus 12.1%; p ⫽ 0.79). When patients are grouped according to discrete ranges of BMI, the relationship between SSI and BMI is not nearly as linear or predictable as it is when patients are grouped according to %BF (Fig. 1A, 1B). With %BF to define obesity, patients who were obese had a higher occurrence of SSI than nonobese patients (15% of obese patients versus 5% nonobese patients; p ⬍ 0.001). Differentiation in SSI incidence between the obese and nonobese groups was attenuated when men were ana- Vol. 210, No. 4, April 2010 Waisbren et al Body Fat and Surgical Site Infection Risk 385 Table 2. Demographic and Clinical Characteristics of Obese and Nonobese Patients Characteristic All participants (nⴝ591) Obese (%BF) (nⴝ409) 49.7 ⫾ 10.1 50.5 ⫾ 9.9 Nonobese (%BF) (nⴝ182) p Value* Obese (BMI) (nⴝ225) Nonobese (BMI) (nⴝ366) p Value† 0.89 Preoperative demographic and clinical characteristics 48 ⫾ 10.3 0.006 49.8 ⫾ 9.6 49.7 ⫾ 10.4 Women, n (%) Age, y, mean ⫾ SD 396 (67) 288 (70) 108 (59) 0.008 148 (66) 248 (68) 0.62 History of cancer, n (%) 238 (40.3) 168 (41) 70 (39) 0.55 73 (32) 165 (45) 0.002 Antihypertensives, n (%) 187 (31.6) 159 (39) 28 (15) ⬍0.001 103 (46) 84 (23) ⬍0.001 Current smoker, n (%) 105 (17.8) 70 (17) 35 (19) 0.53 40 (18) 65 (18) 1.0 Alcohol (⬎6⫻/wk), n (%) 95 (16.1) 67 (16) 28 (15) 0.76 29 (13) 66 (18) 0.1 COPD, n (%) 97 (16.4) 72 (18) 25 (14) 0.24 44 (20) 53 (15) 0.11 Pedal edema, n (%) 91 (15.4) 73 (18) 18 (10) 0.01 53 (24) 38 (10) ⬍0.001 Recent surgery, n (%) 72 (12.2) 50 (12) 22 (12) 0.96 32 (14) 40 (11) 0.24 Remote infection, n (%) 64 (10.8) 45 (11) 19 (10) 0.84 27 (12) 37 (10) 0.47 History of SSI, n (%) 12 (2) 10 (2) 2 (1) 0.36 5 (2) 7 (2) 0.8 ⬍0.001 Diabetes, n (%) 61 (10.3) 53 (13) 8 (4) 0.002 41 (18) 20 (6) History of myocardial infarction, n (%) 14 (2.4) 12 (3) 2 (1) 0.25 9 (4) 5 (1) 0.04 Angina, n (%) 42 (7.1) 29 (7) 13 (7) 0.98 20 (9) 22 (6) 0.19 Peripheral vascular disease, n (%) 23 (3.9) 15 (4) 8 (4) 0.67 8 (4) 15 (4) 0.74 Cerebrovascular accident, n (%) 19 (3.2) 17 (4) 2 (1) 0.07 10 (4) 9 (3) 0.18 192 (32.5) 133 (33) 59 (32) 0.98 75 (33) 117 (32) 0.73 Duration of operation, (mean ⫾ SD, h) 2.8 ⫾ 1.9 2.9 ⫾ 2.1 2.4 ⫾ 1.4 0.003 2.9 ⫾ 1.6 2.8 ⫾ 2.1 0.51 Perioperative antibiotics, n (%) 569 (97.1) 395 (98) 174 (96) 0.15 217 (97) 352 (97) Recent weight loss, n (%) Operative variables Wound classification, n (%) 0.01 Clean 334 (56.5) 238 (58) 96 (53) 129 (57) 205 (56) Clean-contaminated 236 (39.9) 160 (39) 76 (42) 89 (40) 147 (40) 15 (2.5) 7 (2) 8 (4) 5 (2) 10 (3) 6 (1) 4 (1) 2 (1) 2 (1) 4 (1) Contaminated Dirty ASA classification, n (%) 0.01 0.001 I 56 (9.5) 28 (7) 28 (15) 10 (4) 46 (13) II 393 (66.5) 277 (68) 116 (64) 148 (66) 245 (67) III 137 (23.2) 100 (25) 37 (20) 64 (30) 73 (20) IV 5 (0.9) 4 (1) 1 (1) 3 (1) 2 (1) NNIS score, n (%) 0.02 0.008 0 268 (45.4) 179 (44) 89 (49) 92 (41) 176 (48) 1 238 (40.3) 164 (40) 74 (41) 89 (40) 149 (41) 2 79 (13.4) 64 (16) 15 (8) 43 (19) 36 (10) 2 (0.5) 4 (2) 5 (2) 7 (2) 3 6 (1) 0.81 0.001 Implant recipient, n (%) 165 (27.9) 121 (30) 44 (24) 0.18 77 (34) 88 (24) Drain placement, n (%) 160 (27.1) 116 (28) 44 (24) 0.31 64 (28) 96 (26) 0.007 0.57 Catheter placement, n (%) 332 (56.2) 239 (58) 93 (51) 0.1 139 (62) 193 (53) 0.03 Postoperative length of stay, d, mean ⫾ SD 2.2 ⫾ 3.1 2.4 ⫾ 3.2 1.9 ⫾ 2.9 0.09 2.2 ⫾ 2.8 2.8 ⫾ 2.1 0.77 Blood loss, mL, mean ⫾ SD 138 ⫾ 281 157 ⫾ 317 96 ⫾ 168 0.02 148 ⫾ 251 133 ⫾ 298 0.55 *p Value for obese versus nonobese (%BF). † p Value for obese versus nonobese (BMI). SSI, surgical site infection; Obesity, defined by body-mass index (BMI ⬎30 kg/m2) or % body fat (males ⬎25 %BF, females ⬎31 %BF); Current smoker, cigarette use within 1 year; Recent surgery, surgical procedure within 1 mo of current operation; Angina, within 1 y of current operation; Weight loss, ⬍5 lbs within 6 mo; ASA Score, American Society of Anesthesiology preoperative assessment score: 1. Healthy, 2. Mild systemic disease, 3. Severe systemic disease, 4. Severe systemic disease that is a constant threat to life; NNIS, National Nosocomial Infections Surveillance System, risk index developed to predict a surgical patients’ risk of acquiring a SSI, ranges from 0-3, variables included are ASA score ⬎3, and operation classified as “contaminated (3)” or “dirty (4)”, and an operation lasting over T hours (T depends on operative procedure being preformed); Wound classification clean, operative procedure does not enter into a normally colonized viscus or lumen of the body; clean-contaminated, operative procedure enters into a colonized viscus or cavity of the body, but under elective and controlled circumstances; contaminated, gross contamination is present at the surgical site in the absence of obvious infection; dirty, active infection is already present. 386 Waisbren et al Body Fat and Surgical Site Infection Risk J Am Coll Surg Figure 1. Risk of surgical site infection (SSI) developing (A) measured by body mass index in men and women. There was a nonlinear relationship between BMI and SSI risk for the whole group. (B) SSI measured by percent body fat in men and women, revealing a linear, predictable relationship for percent body fat. lyzed separately from women (13.2% versus 5.4%; p ⫽ 0.09). When patients were grouped in discrete ranges of body fat, there was a positive linear relationship between %BF and incidence of SSI. When this analysis was stratified by gender, the positive linear relationship was maintained for women, but attenuated for men. Univariate predictors of SSI are shown in Table 1. Both continuous %BF and obesity as defined by %BF were significantly different between the SSI and non-SSI groups (for %BF continuous: p ⫽ 0.005; for obese by %BF: p ⱕ0.001). All components of National Nosocomial Infections Surveillance score (which includes duration of operation, wound classification, and American Society of Anesthesiologists classification) were significantly different as well (duration of operation: p ⬍ 0.001; wound classification: p ⫽ 0.04; ASA score: p ⫽ 0.01). A higher percentage of patients had a history of previous SSI in the current SSI group (7% versus 1%, p ⫽ 0.001). There was no difference in the receipt of perioperative antibiotics between the groups. After adjustment for smoking, National Nosocomial Infections Surveillance score, and diabetes in a multivariable analysis, obese patients as defined by %BF were ⬎5 times more likely to have an SSI than nonobese patients (odds ratio ⫽ 5.3; 95% CI, 1.2⫺23.0; p ⫽ 0.03). In a separate model using BMI to define obesity, obesity was not an independent predictor of SSI (odds ratio ⫽ 1.3; 95% CI, 0.6⫺2.7; p ⫽ 0.45), see Table 3 for results. DISCUSSION The aims of this study were to define the relationship between obesity and SSI, determine the predictive capabilities of %BF for development of SSI, and identify correla- Vol. 210, No. 4, April 2010 Waisbren et al Body Fat and Surgical Site Infection Risk 387 Table 3. Results of Multiple Logistic Regression Analysis for Surgical Site Infection Characteristic Nonobese by percent body fat (reference) Obese by percent body fat Nonobese by BMI (reference) Obese by BMI, kg/m2 Cigarette smoking NNIS score 0 (reference) NNIS score 1 NNIS score 2 NNIS score 3 Diabetes Odds ratio NA NA 1 1.3 2.3 1 1.8 5.8 1.9 1.3 BMI model 95% CI NA NA NA 0.6⫺2.7 1.3⫺4.1 NA 0.9⫺3.4 2.8⫺12.0 0.2⫺18.5 0.6⫺2.8 p Value Odds ratio % Body fat model 95% Cl p Value NA NA NA 0.45 0.006 NA 0.07 ⬍0.001 0.58 0.52 1 5.3 NA NA 2.2 1 1.8 5.2 3.7 1 NA 1.2⫺23.1 NA NA 1.2⫺4.0 NA 0.95⫺3.4 2.6⫺10.7 0.36⫺38.9 0.47⫺2.13 NA 0.03 NA NA 0.008 NA 0.07 ⬍0.001 0.27 1 BMI, body mass index; NNIS, National Nosocomial Infections Surveillance System. tions between %BF and BMI. We found that obese patients as defined by %BF were ⬎5 times more likely to have SSI develop after surgery than nonobese patients. In contrast, BMI-defined obesity was not an independent risk factor for SSI. Although the risk of SSI developing increases with an increase in %BF in a predictable pattern, there is no corresponding consistent relationship when using BMI. Given these results, we conclude that %BF is better than BMI in determining the association between obesity and risk of occurrence of postoperative SSI. Many studies have shown that obesity by BMI is associated with an increased risk for SSI development.7,21-23 However, another body of literature supports our results that obesity measured by BMI is not a risk factor for SSI.24,25 Our results agree with the latter, most likely a reflection of our large sample size. No studies to date have shown the strength of the association when using %BF. Regardless of the BMI debate, our results show a very strong relationship between SSI and obesity measured by %BF. Obesity can increase the risk of SSI by several proposed mechanisms, such as technical difficulty during surgery, tissue trauma, and tension on the healing wound. The wound might also have decreased circulation and decreased oxygenation, which have been shown to be risk factors for SSI.26 An impaired immune response can also result from the interactions of adipocytes with the immune system.27-32 Percent body fat allows us to accurately categorize patients in a way that reflects the risk of SSI developing. Even though BMI is the one of the most widely used measures of obesity, our data question how successfully this classification scheme identifies individuals at increased risk because of obesity. In our study, patients who are obese per BMI do not necessarily have increased morbidity. With a more consistent relationship between %BF and SSI risk, measuring %BF allows us to understand the risks of SSI developing associated with each increment of %BF (Fig. 1B). Using %BF in the clinical setting helps physicians and nurses identify patients who are in the high-risk category. Our finding that obese patients by %BF are ⬎5 times more likely to have SSI develop than nonobese patients should be taken seriously. It can be beneficial to counsel patients on the merits of preoperative weight loss as a preventive measure. Our study identified a nonlinear relationship between BMI and %BF, suggesting that BMI does not adequately define obesity. Other studies have also found a quadratic relationship between body fat and BMI,10,11,18 which might explain why the incidence of SSI was much higher when using %BF than BMI. The quadratic relationship suggests that BMI does not accurately reflect “adiposity” or “fatness” for many patients. In a large study using 2,032 adult patients from the Framingham study, Roubenoff and colleagues found that BMI was a poor predictor of fatness in women (R2 ⫽ 0.55) and men (R2 ⫽ 0.38) and imprecise (standard error of estimate ⫽ 5 percentage points), and there was a quadratic relationship between %BF and BMI.18 Frankenfield and colleagues10 showed that measurement of body fat is a more appropriate way to assess obesity in people with a BMI ⬍30. They found that 30% of men and 46% of women with BMI ⬍30 had obesity levels of body fat and were misclassified by BMI.10 In the clinical setting, %BF is a far more accurate and precise measure of fatness than BMI. Interestingly, nonobese patients by %BF were younger than obese patients by %BF, but the ages were the same when obese versus nonobese patients by BMI (Table 2). Perhaps this is because %BF is sensitive to differences in body composition that change subtly with age (ie, with age muscle mass is replaced by fat mass33), which might not be detected by BMI because it calculates only height and weight, which tend to be consistent as we age. Similarly, we found that there were more obese women (%BF) than the nonobese group (%BF), but there was no difference in gender between obese and nonobese patients 388 Waisbren et al Body Fat and Surgical Site Infection Risk when obesity was measured using BMI (Table 2). Our study group did not have equal numbers of men and women, so a prospective study looking specifically at this would be interesting. However, we hypothesize that women have different body composition than men and perhaps the sensitivity of %BF allows the subtle differences to be detected. To further investigate this relationship, we altered Figure 1 to include only men and only women, but we did not find a substantial difference from the Figure 1 shown in this publication. Limitations of our study are as follows: We collected data using both medical records and oral questionnaires for each participant. A telephone interview was conducted that asked questions useful in identifying SSI and other woundrelated complications (eg, Did you observe purulent discharge from the wound? Did erythema/edema/pain develop around the wound?). These data were used to make sure that we did not miss any SSIs or complications that might not have been documented in medical records. This questionnaire was conducted in the postoperative period, introducing recall bias as one limitation. Use of questionnaires to obtain details of the medical history can be complicated by bias inherent in self-reported data, although this bias should be nondifferential between the groups of patients. Because patients were recruited from a tertiary care hospital that serves as a referral center in a major city, they might not be representative of the general population, as they were typically older than 50 years or had substantial comorbidities. Although obesity by %BF was found to be an independent predictor of SSI by both bivariate and multivariable analysis, it is a complex disease and it is possible that %BF is a surrogate for other factors that are the true cause of the increased risk of SSI. One limitation of our measurement technique is that BIA values are affected by numerous variables, including body position, hydration status, consumption of food and beverages, ambient air and skin temperature, recent physical activity, and conductance of the examining table. Although we were unable to control for all of these, we requested that all patients were fasting at the time of measurement. We also measured %BF in the morning to be consistent. Additionally, we measured body fat 3 times in each participant and took the mean of this score to reduce intrameasurement variability. In conclusion, obesity, as defined by %BF, is an independent predictor of SSI. As %BF increases, the risk of SSI developing also increases. Additionally, we conclude that a positive, nonlinear relationship exists between %BF and BMI. Based on these findings in the preoperative setting, %BF is a more accurate predictor of SSI than BMI. The 5-fold greater SSI incidence in %BF obese than BMI obese patients is convincing evidence that %BF J Am Coll Surg should be used to assess preoperative SSI risk. %BF was consistently found to be a more sensitive marker for obesity risks and a better way to detect obesity than BMI. Yet, even with %BF’s potential use, prospective studies are needed to pinpoint the proper obesity category cutoffs for men and women. Data within this study should be used for several groups, such as obese patients considering an elective surgical procedure and surgeons assessing risk and providing informed consent. There might be a benefit to counseling patients, when possible and clinically appropriate, to consider preoperative weight loss as a preventive measure. Although BMI is used almost universally in the clinical setting, it might not be a useful or necessary measurement. Rather, to calculate the risk posed by obesity, it is better to measure %BF, which is easy, quick, and inexpensive and yields more useful information than BMI. Additional studies should be undertaken to determine if our results are consistent with other adverse outcomes in addition to SSI. 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