Background: The impact of massive weight loss (MWL) on body contouring procedures and outcomes has not been firmly established in the literature.

Objective: The authors investigate the correlations between MWL status, the method of achieving MWL, and the amount of weight lost with wound-healing complications after body contouring procedures.

Methods: The charts of 450 patients (124 of whom had undergone MWL) who underwent body contouring procedures including abdominoplasty, brachioplasty, thighplasty, breast mastopexy/reduction, lower bodylift, bodylift, buttock lift, and liposuction were reviewed. MWL patients were classified as having achieved weight loss through diet and exercise, gastric banding or sleeving, or gastric bypass. Postoperative complication data were collected, including cases of infection, delayed wound healing, seroma, hematoma, dehiscence, and overall wound problems. Odds ratios (OR) were estimated using 4 multivariate logistic regression models.

Results: MWL status was a significant predictor of wound problems (OR, 2.69; P < .001). Patients with 50 to 100 lbs of weight loss did not have a significantly increased risk of wound problems (OR, 1.93; P = .085), while patients with over 100 lbs of weight loss did (OR, 3.98; P < .001). Gastric bypass (OR, 3.01; P = <.001) had a higher risk correlation than did diet and exercise (OR, 2.72, P = .023) or restrictive bariatric surgery (OR, 2.31; P = .038) as a weight loss method. Patients who lost over 100 lbs demonstrated increased risk of complications if they had gastric bypass or restrictive procedures.

Conclusions: MWL was a significant risk factor for wound complications in the body contouring population. Method and amount of weight loss were also significant factors in predicting complications.

The rising rate of obesity over the past few decades is among the most pervasive health trends; currently, more than one-third of Americans are considered obese.1 Associated comorbidities such as diabetes mellitus, cardiovascular disease, hyperlipidemia, arterial hypertension, and depression have increased the need for bariatric surgery to achieve weight loss and diminish the concomitant comorbidities in this patient population effectively and safely.2 Approximately 101 000 gastric bypass, gastric banding, and gastric sleeve procedures were performed in the United States in 2011, reflecting the prevalence of obesity within the population.3

Massive weight loss (MWL) following bariatric surgery often leads to skin excess; 68% to 85% of postbariatric surgery patients desire body contouring surgery to remove the excess,4,5 which represents an area of steady growth within plastic surgery. Procedures such as abdominoplasty have increased over 360% in the last 16 years.6 Body contouring procedures are not without risk, as surgery time can be prolonged and relatively high complication rates have been cited in the literature.7,8 MWL patients also carry unique risks because of redundant skin, nutritional deficiencies, and overall metabolic differences, especially as these factors relate to the wound-healing process.9

In this study, we sought not only to distinguish how outcomes were affected by MWL status among the entire body contouring patient population but also further analyze the risk of the various methods of achieving MWL.

Methods

Study Design

A UT Southwestern institutional review board–approved retrospective chart review was performed of 1801 complex plastic surgical cases performed at a single academic medical center between January 1, 2008, and January 31, 2012. Relevant information was abstracted from patient charts and hospital records, and a database was constructed to capture a broad range of procedures with long operative times, including some combined surgeries. Patients included in the analysis received abdominoplasty, brachioplasty, thighplasty, breast mastopexy/reduction, lower bodylift, bodylift, buttock lift, and/or liposuction. Patients who returned to the study site for additional body contouring procedures at multiple dates were selected only for their first encounter to reduce statistical bias. Body contouring procedures combined with hernia repair were excluded. Of the 1801 cases included in the database, 450 cases met the inclusion criteria. Of these 450 cases, 407 patients were women and 43 were men.

Preoperative patient characteristics, including sex, age, body mass index (BMI; kg/m2), MWL status, method of weight loss, and common comorbidities associated with postoperative complications were included in the analysis.10,11 Patients were considered to have undergone MWL if they had achieved a weight loss of 50 lb or more. Method of weight loss was categorized as gastric bypass, restrictive (which included laparoscopic band and gastric sleeve procedures), and unspecified (which included patients lacking a specific history regarding their weight loss method). Postoperative complications were assessed and noted in clinical progress notes by attending physicians and included infection, delayed wound healing, seroma, hematoma, dehiscence, and overall wound problems, which is inclusive of all of the aforementioned complications. These complications were then abstracted from the chart review and included in the larger database.

Patient demographics were summarized as a mean and standard deviation for continuous variables and as a percentage for categorical variables. Univariate analyses were utilized to compare patient characteristics and comorbidities, and significant factors were selected for further analysis. Odds ratios were estimated using 4 multivariate logistic regression models: (1) a base model including MWL status, (2) a model adjusting for the amount of weight lost, (3) a model adjusting for the method used to achieve MWL, and (4) a model measuring the interaction between method of weight loss and the amount of weight lost. While history of myocardial infarction, coronary artery disease, chronic obstructive pulmonary disease, and deep venous thrombosis were included in the original models, they were eventually omitted from further statistical consideration. Statistical significance was assigned if P values were less than .05. All data analysis was conducted with Stata/SE Version 12.0 (StataCorp, Inc, College Station, Texas) statistical software.

Results

The average age of the study population was 45.4 years (range, 16–79 years). Overall patient demographics and complication rates are summarized in Table 1. The complication rate for the study population was 19.1% with an average follow-up time of 6 months. Of the 450 body contouring cases, 124 were classified as MWL patients, and univariate analyses comparing MWL patients with non-MWL patients are summarized in Table 2. The methods of achieving weight loss and corresponding wound problem rates are summarized in Table 3.

Table 1

Patient Demographics (450 Patients)

Value%Average ± SD
Sex (male)43/450   9.56
Age >45 y213/450   47.345.4 ± 12.3
BMI ≥30115/450   25.627.2 ± 5.10
MWL124/450   27.6
Smoker26/450   5.78
Diabetes24/450   5.33
Hypertension92/450   20.4
CAD2/450   0.44
MI2/450   0.44
Other cardiac64/450   14.2
COPD1/450   0.22
Other pulmonary34/450   7.6
Renal diseases13/450   2.89
Cancer36/450   8.00
DVT5/450   1.11
Wound problem86/450   19.1
Infection14/450   3.12
Dehiscence18/450   4.00
Erythema32/450   7.11
Necrosis9/450   2.00
Seroma23/450   5.11
Hematoma5/450   1.11
Delayed wound healing12/450   2.67
Value%Average ± SD
Sex (male)43/450   9.56
Age >45 y213/450   47.345.4 ± 12.3
BMI ≥30115/450   25.627.2 ± 5.10
MWL124/450   27.6
Smoker26/450   5.78
Diabetes24/450   5.33
Hypertension92/450   20.4
CAD2/450   0.44
MI2/450   0.44
Other cardiac64/450   14.2
COPD1/450   0.22
Other pulmonary34/450   7.6
Renal diseases13/450   2.89
Cancer36/450   8.00
DVT5/450   1.11
Wound problem86/450   19.1
Infection14/450   3.12
Dehiscence18/450   4.00
Erythema32/450   7.11
Necrosis9/450   2.00
Seroma23/450   5.11
Hematoma5/450   1.11
Delayed wound healing12/450   2.67

BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DVT, deep venous thrombosis; MI, myocardial infarction; MWL, massive weight loss.

Table 1

Patient Demographics (450 Patients)

Value%Average ± SD
Sex (male)43/450   9.56
Age >45 y213/450   47.345.4 ± 12.3
BMI ≥30115/450   25.627.2 ± 5.10
MWL124/450   27.6
Smoker26/450   5.78
Diabetes24/450   5.33
Hypertension92/450   20.4
CAD2/450   0.44
MI2/450   0.44
Other cardiac64/450   14.2
COPD1/450   0.22
Other pulmonary34/450   7.6
Renal diseases13/450   2.89
Cancer36/450   8.00
DVT5/450   1.11
Wound problem86/450   19.1
Infection14/450   3.12
Dehiscence18/450   4.00
Erythema32/450   7.11
Necrosis9/450   2.00
Seroma23/450   5.11
Hematoma5/450   1.11
Delayed wound healing12/450   2.67
Value%Average ± SD
Sex (male)43/450   9.56
Age >45 y213/450   47.345.4 ± 12.3
BMI ≥30115/450   25.627.2 ± 5.10
MWL124/450   27.6
Smoker26/450   5.78
Diabetes24/450   5.33
Hypertension92/450   20.4
CAD2/450   0.44
MI2/450   0.44
Other cardiac64/450   14.2
COPD1/450   0.22
Other pulmonary34/450   7.6
Renal diseases13/450   2.89
Cancer36/450   8.00
DVT5/450   1.11
Wound problem86/450   19.1
Infection14/450   3.12
Dehiscence18/450   4.00
Erythema32/450   7.11
Necrosis9/450   2.00
Seroma23/450   5.11
Hematoma5/450   1.11
Delayed wound healing12/450   2.67

BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DVT, deep venous thrombosis; MI, myocardial infarction; MWL, massive weight loss.

Table 2

Univariate Analysis: No MWL vs MWL

No MWLMWLP Value
Sex (male)36/326   7/124   .082   
Age >45 y155/326   58/124   .884   
BMI ≥3074/326   41/124   .024a   
Smoker18/326   8/124   .706   
Diabetes9/326   15/124   <.001a   
Hypertension62/326   30/124   .224   
CAD1/326   1/124   .476   
MI2/326   0/124   .382   
Other cardiac43/326   21/124   .309   
COPD0/326   1/124   .105   
Other pulmonary20/326   14/124   .064   
Renal diseases10/326   3/124   .714   
Cancer32/326   4/124   .021a   
DVT2/326   3/124   .103   
Wound problem48/326   38/124   <.001a   
Infection6/326   8/124   .012a   
Dehiscence5/326   13/124   <.001a   
Erythema16/326   16/124   .003a   
Necrosis5/326   4/124   .252   
Seroma17/326   6/124   .871   
Hematoma1/326   4/124   .008a   
Delayed wound healing4/326   8/124   .002a   
No MWLMWLP Value
Sex (male)36/326   7/124   .082   
Age >45 y155/326   58/124   .884   
BMI ≥3074/326   41/124   .024a   
Smoker18/326   8/124   .706   
Diabetes9/326   15/124   <.001a   
Hypertension62/326   30/124   .224   
CAD1/326   1/124   .476   
MI2/326   0/124   .382   
Other cardiac43/326   21/124   .309   
COPD0/326   1/124   .105   
Other pulmonary20/326   14/124   .064   
Renal diseases10/326   3/124   .714   
Cancer32/326   4/124   .021a   
DVT2/326   3/124   .103   
Wound problem48/326   38/124   <.001a   
Infection6/326   8/124   .012a   
Dehiscence5/326   13/124   <.001a   
Erythema16/326   16/124   .003a   
Necrosis5/326   4/124   .252   
Seroma17/326   6/124   .871   
Hematoma1/326   4/124   .008a   
Delayed wound healing4/326   8/124   .002a   

BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DVT, deep venous thrombosis; MI, myocardial infarction; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

Table 2

Univariate Analysis: No MWL vs MWL

No MWLMWLP Value
Sex (male)36/326   7/124   .082   
Age >45 y155/326   58/124   .884   
BMI ≥3074/326   41/124   .024a   
Smoker18/326   8/124   .706   
Diabetes9/326   15/124   <.001a   
Hypertension62/326   30/124   .224   
CAD1/326   1/124   .476   
MI2/326   0/124   .382   
Other cardiac43/326   21/124   .309   
COPD0/326   1/124   .105   
Other pulmonary20/326   14/124   .064   
Renal diseases10/326   3/124   .714   
Cancer32/326   4/124   .021a   
DVT2/326   3/124   .103   
Wound problem48/326   38/124   <.001a   
Infection6/326   8/124   .012a   
Dehiscence5/326   13/124   <.001a   
Erythema16/326   16/124   .003a   
Necrosis5/326   4/124   .252   
Seroma17/326   6/124   .871   
Hematoma1/326   4/124   .008a   
Delayed wound healing4/326   8/124   .002a   
No MWLMWLP Value
Sex (male)36/326   7/124   .082   
Age >45 y155/326   58/124   .884   
BMI ≥3074/326   41/124   .024a   
Smoker18/326   8/124   .706   
Diabetes9/326   15/124   <.001a   
Hypertension62/326   30/124   .224   
CAD1/326   1/124   .476   
MI2/326   0/124   .382   
Other cardiac43/326   21/124   .309   
COPD0/326   1/124   .105   
Other pulmonary20/326   14/124   .064   
Renal diseases10/326   3/124   .714   
Cancer32/326   4/124   .021a   
DVT2/326   3/124   .103   
Wound problem48/326   38/124   <.001a   
Infection6/326   8/124   .012a   
Dehiscence5/326   13/124   <.001a   
Erythema16/326   16/124   .003a   
Necrosis5/326   4/124   .252   
Seroma17/326   6/124   .871   
Hematoma1/326   4/124   .008a   
Delayed wound healing4/326   8/124   .002a   

BMI, body mass index; CAD, coronary artery disease; COPD, chronic obstructive pulmonary disease; DVT, deep venous thrombosis; MI, myocardial infarction; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

Table 3

Method of Weight Loss (450 Patients)

Value%
Gastric bypass51/12441.1
   Wound problem 17/5133.3
Restrictive band/sleeve40/12432.2
   Wound problem 12/4030.0
Diet and exercise33/12426.6
   Wound problem 9/3327.3
Value%
Gastric bypass51/12441.1
   Wound problem 17/5133.3
Restrictive band/sleeve40/12432.2
   Wound problem 12/4030.0
Diet and exercise33/12426.6
   Wound problem 9/3327.3
Table 3

Method of Weight Loss (450 Patients)

Value%
Gastric bypass51/12441.1
   Wound problem 17/5133.3
Restrictive band/sleeve40/12432.2
   Wound problem 12/4030.0
Diet and exercise33/12426.6
   Wound problem 9/3327.3
Value%
Gastric bypass51/12441.1
   Wound problem 17/5133.3
Restrictive band/sleeve40/12432.2
   Wound problem 12/4030.0
Diet and exercise33/12426.6
   Wound problem 9/3327.3

Multivariate logistic regression using the base model showed that MWL status was a statistically significant (odds ratio [OR], 2.69; P < .001) predictor of wound problems in body contouring patients (Table 4). A second logistic regression model (Table 5) estimating the effect of the amount of weight loss (50-100 lb and 100+ lb) demonstrated a statistically significant result for those in the 100+-lb category (OR, 3.98; P < .001) and, for the 50- to 100-lb group, a result that approached statistical significance (OR, 1.93, P = .085). A related model demonstrated an odds ratio of 1.0094 (P < .001) per each additional pound lost.

Table 4

Multivariate Logistic Regression: Massive Weight Loss Status

VariableOdds Ratio95% CIP Value
MWL2.691.60–4.52<.001a 
Sex (male)1.200.51–2.82.674
Age >45 y1.260.74–2.15.386
BMI ≥301.450.82–2.55.200
Smoker0.130.02–1.02.053
Diabetes0.380.10–1.42.151
Hypertension1.500.79–2.85.211
Other cardiac0.780.36–1.69.521
Other pulmonary0.650.24–1.76.397
Renal diseases1.670.39–7.19.493
Cancer0.530.17–1.60.260
VariableOdds Ratio95% CIP Value
MWL2.691.60–4.52<.001a 
Sex (male)1.200.51–2.82.674
Age >45 y1.260.74–2.15.386
BMI ≥301.450.82–2.55.200
Smoker0.130.02–1.02.053
Diabetes0.380.10–1.42.151
Hypertension1.500.79–2.85.211
Other cardiac0.780.36–1.69.521
Other pulmonary0.650.24–1.76.397
Renal diseases1.670.39–7.19.493
Cancer0.530.17–1.60.260

BMI, body mass index; CI, confidence interval; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

Table 4

Multivariate Logistic Regression: Massive Weight Loss Status

VariableOdds Ratio95% CIP Value
MWL2.691.60–4.52<.001a 
Sex (male)1.200.51–2.82.674
Age >45 y1.260.74–2.15.386
BMI ≥301.450.82–2.55.200
Smoker0.130.02–1.02.053
Diabetes0.380.10–1.42.151
Hypertension1.500.79–2.85.211
Other cardiac0.780.36–1.69.521
Other pulmonary0.650.24–1.76.397
Renal diseases1.670.39–7.19.493
Cancer0.530.17–1.60.260
VariableOdds Ratio95% CIP Value
MWL2.691.60–4.52<.001a 
Sex (male)1.200.51–2.82.674
Age >45 y1.260.74–2.15.386
BMI ≥301.450.82–2.55.200
Smoker0.130.02–1.02.053
Diabetes0.380.10–1.42.151
Hypertension1.500.79–2.85.211
Other cardiac0.780.36–1.69.521
Other pulmonary0.650.24–1.76.397
Renal diseases1.670.39–7.19.493
Cancer0.530.17–1.60.260

BMI, body mass index; CI, confidence interval; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

Table 5

Multivariate Logistic Regression: 50- to 100-lb Loss vs 100+-lb Loss

VariableOdds Ratio95% CIP Value
MWL (50–100 lb)1.930.91–4.06.085
MWL (100+ lb)3.981.93–8.23<.001a 
Sex (male)1.160.49–2.75.731
Age >45y1.170.68–2.04.588
BMI ≥301.480.82–2.67.194
Smoker0.130.02–0.97.053
Diabetes0.440.11–1.77.245
Hypertension1.590.82–3.08.172
Other cardiac0.780.35–1.73.536
Other pulmonary0.740.27–2.02.552
Renal diseases1.450.33–6.42.628
Cancer0.560.18–1.70.306
VariableOdds Ratio95% CIP Value
MWL (50–100 lb)1.930.91–4.06.085
MWL (100+ lb)3.981.93–8.23<.001a 
Sex (male)1.160.49–2.75.731
Age >45y1.170.68–2.04.588
BMI ≥301.480.82–2.67.194
Smoker0.130.02–0.97.053
Diabetes0.440.11–1.77.245
Hypertension1.590.82–3.08.172
Other cardiac0.780.35–1.73.536
Other pulmonary0.740.27–2.02.552
Renal diseases1.450.33–6.42.628
Cancer0.560.18–1.70.306

BMI, body mass index; CI, confidence interval; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

Table 5

Multivariate Logistic Regression: 50- to 100-lb Loss vs 100+-lb Loss

VariableOdds Ratio95% CIP Value
MWL (50–100 lb)1.930.91–4.06.085
MWL (100+ lb)3.981.93–8.23<.001a 
Sex (male)1.160.49–2.75.731
Age >45y1.170.68–2.04.588
BMI ≥301.480.82–2.67.194
Smoker0.130.02–0.97.053
Diabetes0.440.11–1.77.245
Hypertension1.590.82–3.08.172
Other cardiac0.780.35–1.73.536
Other pulmonary0.740.27–2.02.552
Renal diseases1.450.33–6.42.628
Cancer0.560.18–1.70.306
VariableOdds Ratio95% CIP Value
MWL (50–100 lb)1.930.91–4.06.085
MWL (100+ lb)3.981.93–8.23<.001a 
Sex (male)1.160.49–2.75.731
Age >45y1.170.68–2.04.588
BMI ≥301.480.82–2.67.194
Smoker0.130.02–0.97.053
Diabetes0.440.11–1.77.245
Hypertension1.590.82–3.08.172
Other cardiac0.780.35–1.73.536
Other pulmonary0.740.27–2.02.552
Renal diseases1.450.33–6.42.628
Cancer0.560.18–1.70.306

BMI, body mass index; CI, confidence interval; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

The method of weight loss was also analyzed in a multivariate logistic regression that regressed on each respective method: gastric bypass, restrictive (including gastric banding or gastric sleeve), and diet and exercise. All 3 methods demonstrated statistically significant differences regarding wound problems from the non-MWL population. Gastric bypass (OR, 3.01; P = .002) demonstrated a higher odds ratio than either diet and exercise (OR, 2.72; P = .023) or restrictive bariatric surgery (OR, 2.31; P = .038). These data are summarized in Table 6.

Table 6

Multivariate Logistic Regression Based on Surgery Type

VariableOdds Ratio95% CIP Value
Diet and exercise2.721.15–6.43.023a
Restrictive (banding/sleeve)2.311.05–5.10.038a
Gastric bypass3.011.49–6.07.002a
Sex (male)1.200.51–2.83.671
Age >451.250.73–2.13.417
BMI ≥301.440.81–2.53.210
Smoker0.130.02–1.43.053
Diabetes0.380.10–1.02.151
Hypertension1.500.79–2.85.213
Other cardiac0.780.36–1.70.528
Other pulmonary0.640.24–1.75.385
Renal diseases1.680.39–7.30.486
Cancer0.530.17–1.60.256
VariableOdds Ratio95% CIP Value
Diet and exercise2.721.15–6.43.023a
Restrictive (banding/sleeve)2.311.05–5.10.038a
Gastric bypass3.011.49–6.07.002a
Sex (male)1.200.51–2.83.671
Age >451.250.73–2.13.417
BMI ≥301.440.81–2.53.210
Smoker0.130.02–1.43.053
Diabetes0.380.10–1.02.151
Hypertension1.500.79–2.85.213
Other cardiac0.780.36–1.70.528
Other pulmonary0.640.24–1.75.385
Renal diseases1.680.39–7.30.486
Cancer0.530.17–1.60.256

BMI, body mass index; CI, confidence interval.

a

Result is statistically significant as P < .05.

Table 6

Multivariate Logistic Regression Based on Surgery Type

VariableOdds Ratio95% CIP Value
Diet and exercise2.721.15–6.43.023a
Restrictive (banding/sleeve)2.311.05–5.10.038a
Gastric bypass3.011.49–6.07.002a
Sex (male)1.200.51–2.83.671
Age >451.250.73–2.13.417
BMI ≥301.440.81–2.53.210
Smoker0.130.02–1.43.053
Diabetes0.380.10–1.02.151
Hypertension1.500.79–2.85.213
Other cardiac0.780.36–1.70.528
Other pulmonary0.640.24–1.75.385
Renal diseases1.680.39–7.30.486
Cancer0.530.17–1.60.256
VariableOdds Ratio95% CIP Value
Diet and exercise2.721.15–6.43.023a
Restrictive (banding/sleeve)2.311.05–5.10.038a
Gastric bypass3.011.49–6.07.002a
Sex (male)1.200.51–2.83.671
Age >451.250.73–2.13.417
BMI ≥301.440.81–2.53.210
Smoker0.130.02–1.43.053
Diabetes0.380.10–1.02.151
Hypertension1.500.79–2.85.213
Other cardiac0.780.36–1.70.528
Other pulmonary0.640.24–1.75.385
Renal diseases1.680.39–7.30.486
Cancer0.530.17–1.60.256

BMI, body mass index; CI, confidence interval.

a

Result is statistically significant as P < .05.

The interaction between the method of weight loss and the amount of weight loss was explored in the final multivariate logistic regression (Table 7). Although the regression estimates that the method of weight loss and a weight loss of 50 to 100 lb did not demonstrate a statistically significant risk, the interaction between 100 lb or more lost and the method of weight loss was statistically significant, except in the diet and exercise population.

Table 7

Interaction Effect Between Weight Loss Method and Amount of Weight Lost

VariableOdds Ratio95% CIP Value
Diet and exercise and 50–100 lb2.400.86–6.71.094
Restrictive and 50–100 lb1.340.35–5.08.671
Gastric bypass and 50–100 lb1.960.47–8.15.353
Diet and exercise and >100 lb4.58 0.345–60.76.249
Restrictive and >100 lb4.84 1.60–14.58.005a
Gastric bypass and >100 lb3.421.35–8.69.010a
Sex (male)1.160.49–2.74.735
Age >45 y1.210.68–2.17.510
BMI ≥301.510.84–2.74.171
Smoker0.120.01–1.01.052
Diabetes0.450.11–1.82.261
Hypertension1.560.80–3.03.198
Other cardiac0.760.34–1.71.510
Other pulmonary0.740.27–2.07.568
Renal diseases1.430.32–6.40.637
Cancer0.540.17–1.68.288
VariableOdds Ratio95% CIP Value
Diet and exercise and 50–100 lb2.400.86–6.71.094
Restrictive and 50–100 lb1.340.35–5.08.671
Gastric bypass and 50–100 lb1.960.47–8.15.353
Diet and exercise and >100 lb4.58 0.345–60.76.249
Restrictive and >100 lb4.84 1.60–14.58.005a
Gastric bypass and >100 lb3.421.35–8.69.010a
Sex (male)1.160.49–2.74.735
Age >45 y1.210.68–2.17.510
BMI ≥301.510.84–2.74.171
Smoker0.120.01–1.01.052
Diabetes0.450.11–1.82.261
Hypertension1.560.80–3.03.198
Other cardiac0.760.34–1.71.510
Other pulmonary0.740.27–2.07.568
Renal diseases1.430.32–6.40.637
Cancer0.540.17–1.68.288

BMI, body mass index; CI, confidence interval; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

Table 7

Interaction Effect Between Weight Loss Method and Amount of Weight Lost

VariableOdds Ratio95% CIP Value
Diet and exercise and 50–100 lb2.400.86–6.71.094
Restrictive and 50–100 lb1.340.35–5.08.671
Gastric bypass and 50–100 lb1.960.47–8.15.353
Diet and exercise and >100 lb4.58 0.345–60.76.249
Restrictive and >100 lb4.84 1.60–14.58.005a
Gastric bypass and >100 lb3.421.35–8.69.010a
Sex (male)1.160.49–2.74.735
Age >45 y1.210.68–2.17.510
BMI ≥301.510.84–2.74.171
Smoker0.120.01–1.01.052
Diabetes0.450.11–1.82.261
Hypertension1.560.80–3.03.198
Other cardiac0.760.34–1.71.510
Other pulmonary0.740.27–2.07.568
Renal diseases1.430.32–6.40.637
Cancer0.540.17–1.68.288
VariableOdds Ratio95% CIP Value
Diet and exercise and 50–100 lb2.400.86–6.71.094
Restrictive and 50–100 lb1.340.35–5.08.671
Gastric bypass and 50–100 lb1.960.47–8.15.353
Diet and exercise and >100 lb4.58 0.345–60.76.249
Restrictive and >100 lb4.84 1.60–14.58.005a
Gastric bypass and >100 lb3.421.35–8.69.010a
Sex (male)1.160.49–2.74.735
Age >45 y1.210.68–2.17.510
BMI ≥301.510.84–2.74.171
Smoker0.120.01–1.01.052
Diabetes0.450.11–1.82.261
Hypertension1.560.80–3.03.198
Other cardiac0.760.34–1.71.510
Other pulmonary0.740.27–2.07.568
Renal diseases1.430.32–6.40.637
Cancer0.540.17–1.68.288

BMI, body mass index; CI, confidence interval; MWL, massive weight loss.

a

Result is statistically significant as P < .05.

Discussion

The connection between MWL and body contouring surgery has been the subject of an amalgam of original research as well as advisories regarding the evaluation and treatment of this patient population.1215 Our analysis indicated that MWL patients have a statistically significant increased risk of developing wound complications in the postoperative period compared with the non-MWL population. However, this conclusion has been debated in the body contouring literature; Vastine et al16 and Kerviler et al17 indicated that bariatric surgery did not correlate with an increased risk of complications in body contouring procedures, while Greco et al12 and Staalsen et al18 published results to the contrary. Despite their differing conclusions, these studies all had a comparatively small sample size, and statistical power consequently may have been hampered.

Our analysis showed an almost 2-fold increased risk of complications conditional on the amount of weight lost in the MWL patient. This finding was corroborated by Coon et al,19 who found that maximum BMI and change in BMI were associated with developing wound complications, as change in BMI can be associated with the amount of weight lost. Our study would have benefited from similarly utilizing change in BMI as a variable for analysis; however, due to constraints in the data set, this metric was unavailable. Furthermore, our study was unable to compare complication rates among the various body contouring procedures due to a relatively small sample size that would have produced statistically unreliable results. While larger procedures like lower bodylift would ostensibly have higher complication rates than strictly liposuction, for example, the procedures analyzed all fall under the purview of “body contouring” as it is defined in the literature and textbooks and were thus studied as a whole.20,21 Differences between these body contouring procedures, however, should be studied in the future.

The method of achieving MWL proved an indicator in predicting complication rates for body contouring surgery. Our data indicated that regardless of the method of MWL, these patients demonstrated increased risk for wound problems compared with the non-MWL patient. Gastric bypass patients, however, had a greater risk than either the restrictive bariatric surgery population or the diet and exercise population. The current literature is sparse regarding the correlation of restrictive bariatric surgeries and outcomes for body contouring surgeries but has focused on differences between weight loss achieved through dietetic means and gastric bypass.1618 Gusenoff et al22 noted that there was no significant difference in outcomes between patients treated with bariatric procedures and patients who dieted and exercised.

A trend relating the amount of weight lost and the method of weight loss emerged in our study, as MWL below 100 lb was not a significant risk factor, no matter the method. Weight loss greater than 100 lb indicated significant risk associated with both restrictive bariatric procedures and gastric bypass, but not diet and exercise. The diet and exercise group with more than 100 lb lost included only 4 patients; thus, a larger sample size within this population would more accurately estimate the odds ratio.

The cause of the MWL patients' increased risk of wound complication may be multifactorial since nutritional deficiencies, biomechanical changes within the skin, and biological and physiologic alterations may occur within this patient population.23 Collagen and elastic fiber differences as well as tissue protein expression were found to be abnormal among a population of MWL patients receiving body contouring surgery.24 Furthermore, an examination of wound regulatory proteins among cancer, burn, obese, and posttransplant populations has suggested that similar changes may occur in the MWL population.25 Among the chief concerns for the postbariatric patient are nutritional deficiencies. Nutritional intake can remain inadequate as much as a year after surgery, and among gastric bypass patients, intake is often under 1000 kcal/d with significant protein deficiencies 1 year removed from surgery.26,27 While there is a mixture of nutritional deficiencies that contribute to inadequate wound healing, addressing protein deficiencies has been targeted as a potential means of reducing these complications, especially as protein is necessary for fibroblast formation as well as collagen production and angiogenesis.9,28 One study of body contouring procedures after MWL utilized protein supplementation and noted improved outcomes compared with a control group and overall complication rates similar to nonbariatric patients.29 Surgeons at our study site currently utilize preoperative tests for prealbumin and daily protein supplementation on average of 30 to 40 g for 1 month prior to surgery for the MWL patient. Our results may indicate that dietary supplementation should be tailored specifically to the method of weight loss as well as the amount of weight lost.

Conclusions

Massive weight loss status is a significant factor in determining wound complications in the body contouring population. The amount of weight lost and the means through which this weight loss is achieved also demonstrate significant effects on predicting wound complications. Surgeons should be cognizant of these factors when discussing body contouring procedures with their patients and also prepare for surgery by utilizing protein supplementation.

Acknowledgments

The authors thank Roberto Cortez, Rachel Hein, Kendall Anigian, Travis Miller, James Jewell, Natalie Sciano, Bhavani Gannavarapu, Janeiro Okafor, and Alan Wang for their help compiling the original database. They also thank Krista Hardy and Jerzy Lysikowski for their feedback. Additionally, they thank Debby Noble and the research support team at UT Southwestern for their invaluable efforts.

Disclosures

Dr Davis receives research grants from Convatec (Skillman, New Jersey), Thermotek (Flower Mound, Texas), Unilever (London, England), Kensey Nash (Exton, PA), Andrew Technologies (Tustin, California), TA Sciences (New York, New York), and Innovative Therapies (Pompano Beach, Florida). She is a paid consultant for Thermotek and Innovative Therapies, Inc. Dr Kenkel is a paid investigator for Allergan (Irvine, California), Erchonia (McKinney, Texas), and Ultrashape (Irvine, California), and is on the Advisory Board of Kythera (Calabasas, CA). Mr Constantine has nothing to disclose.

Funding

The authors received no financial support for the research, authorship, and publication of this article.

References

1.

National Center for Health Statistics
.
National Health and Nutrition Examination Survey: questionnaires, datasets, and related documentation
. http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm.
Accessed August 9, 2013
.

2.

Fontaine
KR
Redden
DT
Wang
C
et al. .
Years of life lost due to obesity
.
JAMA
.
2003
;
289
(
2
):
187
193
.

3.

Buchwald
H
Oien
DM
.
Metabolic/bariatric surgery worldwide 2011
.
Obes Surg.
2013
;
23
:
427
436
.

4.

Kitzinger
HB
Abayev
S
Pitterman
A
et al. .
After massive weight loss: patients' expectations of body contouring surgery
.
Obes Surg.
2012
;
22
:
544
548
.

5.

Gusenoff
JA
Messing
S
O'Malley
W
Langstein
HN
.
Temporal and demographic factors influencing the desire for plastic surgery after gastric bypass surgery
.
Plast Reconst Surg.
2008
;
121
:
2120
2126
.

6.

American Society of Aesthetic Plastic Surgeons
.
Cosmetic Surgery National Databank: statistics 2012
.
Aesthetic Surg J.
2013
;
33
:
1S
21S
.

7.

Friedman
T
O'Brien-Coon
D
Michaels
J
et al. .
Fleur-de-Lis abdominoplasty: a safe alternative to traditional abdominoplasty for the massive weight loss patient
.
Plast Reconstr Surg.
2010
;
125
:
1525
1535
.

8.

Chong
T
Coon
D
Toy
J
et al. .
Body contouring in the male weight loss population: assessing gender as a factor in outcomes
.
Plast Reconstr Surg.
2012
;
130
:
325e
330e
.

9.

Agha-Mohammadi
S
Hurwitz
DJ
.
Potential impacts of nutritional deficiency of postbariatric patients on body contouring surgery
.
Plast Reconstr Surg.
2008
;
122
:
1901
1914
.

10.

Elixhauser
A
Steiner
C
Harris
DR
Coffey
RM
.
Comorbidity measures for use with administrative data
.
Med Care
.
1998
;
36
:
8
27
.

11.

Romano
PS
Roos
LL
Jollis
JG
.
Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives
.
J Clin Epidemiol.
1993
;
46
(
10
):
1075
1079
.

12.

Greco
JA
Castaldo
ET
Nanney
LB
et al. .
The effect of weight loss surgery and body mass index on wound complications after abdominal contouring operations
.
Ann Plast Surg.
2008
;
61
:
235
242
.

13.

Bossert
RP
Rubin
JP
.
Evaluation of the weight loss patient presenting for plastic surgery consultation
.
Plast Reconstr Surg.
2012
;
130
:
1361
1369
.

14.

Song
AY
Jean
RD
Hurwitz
DJ
et al. .
A classification of contour deformities after bariatric weight loss: the Pittsburgh rating scale
.
Plast Reconstr Surg.
2005
;
116
:
1535
1544
.

15.

Michaels
JV
Coon
D
Rubin
JP
.
Complications in postbariatric body contouring: postoperative management and treatment
.
Plast Reconstr Surg.
2011
;
127
:
1693
1700
.

16.

Vastine
VL
Morgan
RF
Williams
GS
et al. .
Wound complications of abdominoplasty in obese patients
.
Ann Plast Surg.
1999
;
42
:
34
39
.

17.

Kerviler
SD
Hüsler
R
Banic
A
et al. .
Body contouring surgery following bariatric surgery and dietetically induced massive weight reduction: a risk analysis
.
Obes Surg.
2009
;
19
:
553
559
.

18.

Staalsen
T
Olsén
MF
Elander
A
.
Complications of abdominoplasty after weight loss as a result of bariatric surgery or dieting/postpregnancy
.
J Plast Surg Hand Surg.
2012
;
46
:
416
420
.

19.

Coon
D
Gusenoff
A
Kannan
N
et al. .
Body mass and surgical complications in the postbariatric reconstructive patient: analysis of 511 cases
.
Ann Surg.
2009
;
249
:
397
401
.

20.

Coon
D
Michaels
J
Gusenoff
JA
et al. .
Multiple procedures and staging in the massive weight loss population
.
Plast Reconstr Surg.
2010
;
125
:
691
698
.

21.

Nahai
F
.
The Art of Aesthetic Surgery: Principles and Techniques
.
St Louis, MO
:
Quality Medical Publishing
;
2005
.

22.

Gusenoff
JA
Coon
D
Rubin
JP
.
Implications of weight loss method in body contouring outcomes
.
Plast Reconstr Surg.
2009
;
123
:
373
376
.

23.

Fearmonti
RM
Blanton
M
Bond
JE
et al. .
Changes in dermal histomorphology following surgical weight loss versus diet-induced weight loss in the morbidly obese patient
.
Ann Plast Surg.
2012
;
68
:
507
512
.

24.

D'Ettorre
M
Gniuli
D
Iaconelli
A
et al. .
Wound healing process in post-bariatric patients: an experimental evaluation
.
Obes Surg.
2010
;
20
:
1552
1558
.

25.

Albino
FP
Koltz
PF
Gusenoff
JA
.
A comparative analysis and systematic review of the wound-healing milieu: implications for body contouring after massive weight loss
.
Plast Reconstr Surg.
2009
;
124
:
1675
1682
.

26.

Dias
MC
Riberio
AG
Scabim
VM
et al. .
Dietary intake of female bariatric patients after anti-obesity gastroplasty
.
Clinics
.
2006
;
61
:
93
.

27.

Kenler
Ha
Brolin
RE
Cody
RP
.
Changes in eating behavior after horizontal gastroplasty and Roux-en-Y gastric bypass
.
Am J Clin Nutr.
1990
;
52
:
87
.

28.

Spanheimer
RG
Peterkofsky
B
.
A specific decrease in collagen synthesis in acutely fasted, vitamin C-supplemented, guinea pigs
.
J Biol Chem.
1985
;
260
:
3955
.

29.

Agha-Mohammadi
S
Hurwitz
DJ
.
Enhanced recovery after body-contouring surgery: reducing surgical complication rates by optimizing nutrition
.
Aesthetic Plast Surg.
2010
;
34
:
617
625
.