Abstract
Background
While obesity is commonly associated with increased morbidity and mortality, in patients with chronic diseases, it has have been associated with a better prognosis, a phenomenon known as the 'obesity paradox'.
Objective
We investigated the relationship between mortality, length of hospital stay (LOHS), and body mass index (BMI) in patients hospitalized to general surgical wards.
Methods
We extracted data of patients admitted to the hospital between January 2011 and December 2017. BMI was classified according to the following categories: underweight (< 18.5), normal weight (18.5–24.9), overweight (25–29.9), obesity (30–34.9) and severe obesity (≥ 35). Main outcomes were mortality at 30-day mortality and at the end-of-follow-up mortality), as well as LOHS.
Results
A total of 27,639 patients (mean age 55 ± 20 years; 48% males; 19% had diabetes) were included in the study. Median LOHS was longer in patients with diabetes vs. those without diabetes (4.0 vs 3.0 days, respectively), with longest LOHS among underweight patients. A 30-day mortality was 2% of those without (371/22,297) and 3% of those with diabetes (173/5,342). In patients with diabetes, 30-day mortality risk showed a step-wise decrease with increased BMI: 10% for underweight, 6% for normal weight, 3% for overweight, 2% for obese and only 1% for severely obese patients. In patients without diabetes, 30-day mortality was found to be 6% for underweight, 3% for normal weight and 1% across the overweight and obese categories. Mortality rate at the end-of-follow-up was 9% of patients without diabetes and 18% of those with diabetes (adjusted OR = 1.3, 95% CI, 1.2–1.5). In patients with diabetes, mortality risk showed an inverse association with respect to BMI: 52% for underweight, 29% for normal weight, 17% for overweight, 14% for obesity and 7% for severely obese patients, with similar trend in patients without diabetes.
Conclusions
The results support the ‘obesity paradox’ in the general surgical patients as those with and without diabetes admitted to surgical wards, BMI had an inverse association with short- and long-term mortality.
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Introduction
Obesity is increasing in prevalence during the last few decades and is now considered to be a global epidemic [1,2,3,4]. Obesity was shown to be associated with several comorbidities [5] and excess mortality from cardiovascular disease (CVD( [6,7,8,9] and cancer [6, 10]. Specifically, mortality rates are increasing in parallel to growing BMI [11]. Moreover, obese and severely obese individuals suffer from increased risk for major cardiac, pulmonary, renal and neurological perioperative complications, as well as surgical site infection, wound dehiscence, need for reoperation, and hospital readmission [12,13,14,15,16,17,18,19,20]. Accordingly, in the surgical critically ill population, mortality is increased in severely obese patients [21].
In contrast with obesity, underweight is not associated with an increased risk for death from CVD or cancer [6]. Nevertheless, this group of people is also at increased risk for mortality [22, 23], specifically non-cancer and non-CVD mortality [6]. It is still unclear whether underweight pose risk for the patients in the perioperative period. Underweight patients undergoing several types of elective surgery, had perioperative morbidity and mortality that was similar to normal-weight patients [24]. On the other hand, underweight patients undergoing cardiac surgery, orthopedic procedures, intra-abdominal surgery for cancer or lobectomy for lung cancer seem to be at risk of short-term mortality [19, 25,26,27,28,29,30,31].
Length of hospital stay (LOHS) of overweight and obese individuals undergoing general surgery is increased with each BMI category [32]. However, in the surgical critically ill population, LOHS is only increased in severely obese patients [21]. LOHS of underweight patients is longer as well [28, 32].
Diabetes was shown to be related to perioperative morbidity and mortality after elective surgery in general [24] and after cardiac surgery in particular [33]. Among patients undergoing non-cardiac surgery, diabetes was associated increased LOHS, ICU admission, major complications (respiratory, kidney, heart or liver failure, CNS complications) and increased 6-month mortality rates [34].
The goal of the current study was to explore the relationship between BMI with LOHS, mortality at 30 days and at the end-of-follow-up in a cohort of patients hospitalized to general surgery wards, according to diabetes status.
Methods
The study was conducted at a large 1,300-bed university-affiliated tertiary medical center. Admissions to the four general surgery wards were done mainly through the emergency department. All patient social and medical data, as well as laboratory tests and imaging were recorded in electronic medical charts. Deaths were entered into the hospital's mortality database, which was regularly updated according to the population registry of the Ministry of the Interior. This nationwide registry includes updated data on all the citizens of Israel, including the death date and enabled us to capture all fatalities.
For this historical prospective, observational study, information was collected from the electronic records of all subjects hospitalized for any cause to the hospital’s general surgery wards between January 1, 2011, and December 31, 2017. All patients were followed up until death or until April 1, 2018. Self-reported data regarding major comorbidities, smoking and alcohol use, were extracted from the medical charts as well.
In most cases, data on weight and height was based on direct measurement on admission and when on-site measurement was not possible, self-reported height and weight were used. BMI was calculated by dividing weight (in kilograms) by height (in meters squared). Patients with missing data on height or weight were excluded from the study. The study participants were classified into the following groups according to their BMI: underweight (< 18.5 kg/m2), normal weight (18.5 to < 25.0 kg/m2), overweight (25 to < 30 kg/m2), obese (30 to < 35 kg/m2) and severely obese (> 35 kg/m2).
Diagnosis of diabetes was based on data extracted from the medical records regarding a known history of diabetes prior to the admission, or based on the medication list for each patient, as the use of diabetes medications such as oral hypoglycemic agent, glucagon-like peptide 1 (GLP-1) agonist, or insulin, at time of admission.
In subjects with more than one hospital admission, we only included the first admission for analysis.
The study end-points included LOHS, all-cause mortality at 30-day after hospitalization and at the end of follow-up, according to BMI category.
The study was approved by the institutional review board.
Statistical analysis
The statistical analysis for this paper was generated using SAS Software, Version 9.4.
Continuous variables were presented by mean ± SD and categorical variables were presented by (N, %). T test was used to compare continuous variables between DM groups and Fisher's exact test was used for all categorical values, except for surgery and admission year where we used Chi-square to compare the value of categorical variables between these groups. Analysis of variance (ANOVA) was used to compare the value of continuous variables between BMI groups and the Chi-square test was used to compare the value of categorical variables between these groups. As LOHS was skewed, a common normalizing transformation of 1/LOHS was used and this variable was analyzed using ANOVA in a generalized linear model. A 30-day mortality was assessed by logistic regression. Overall survival was assessed by Kaplan–Meier survival analysis, with the log-rank test. Overall survival, adjusted for covariates, was assessed by the Cox proportional hazards model. In overall survival model there was a significant interaction between BMI categories and diabetes status and the comparison between the different BMI categories were computed from the model with this interaction. In the adjustment model two interactions between the covariates and diabetes were also included. Adjustment was made for age, gender, ischemic heart disease, congestive heart failure, hypertension, cerebrovascular disease, malignancy, chronic renal failure, smoking, as well as surgery type and year of hospitalization. LOHS was added to model and the 1/LOHS transformation was used to stabilize this variable. Two-sided p values less than 0.05 were considered statistically significant. In order to check proportional hazard assumption, we added an interaction term between log (follow-up time) and BMI to the Cox proportional hazard model (if the interaction is significant, proportional hazard is not warranted). The interaction term was not statistically significant (p = 0.74). Additionally, a model with the interaction between BMI and diabetes and mortality risks for BMI categories were computed at diabetes = 1 and at diabetes = 0.
To account for missing data on smoking and alcohol, missing data categories were created for both.
Results
Of a total of 42,188 admissions to the 4 general surgery wards between January 2011 and December 2017, we excluded 12,198 repeat admissions and 2,351 patients with missing data on height or weight to calculate BMI. Thus, the study cohort included 27,639 unique patients; of these 48% (12,840 patients) were male. Mean age (± SD) for the population was 55 ± 20 years. In total, 19% (5,342 patients) had diabetes.
As shown in Table 1, patients with diabetes were older than those without diabetes (65 ± 12 years vs. 53 ± 20 years). Unsurprisingly, congestive heart failure, chronic renal failure, ischemic heart disease, hypertension and cerebrovascular disease were more common among subjects with diabetes than those without diabetes.
In those with diabetes, mean BMI was 28.3 ± 7.0, compared to a mean BMI of 25.7 ± 5.9 kg/m2 in subjects without diabetes. Most patients, regardless of their diabetes status, had BMI < 30 kg/m2 (60% for diabetic patients and 78% for non-diabetic patients). Normal weight was the most frequent BMI category (41%) in the group of patients without diabetes, while in those with diabetes, overweight (33%) was the most common classification. BMI < 18.5 kg/m2 category was significantly more common among those without diabetes (4% vs. 1%), while BMI > 35 kg/m2 was twice as common in those with diabetes, compared to those without diabetes (19% vs. 9%). (Table 2).
The most common reasons for admissions to the surgical wards included hernia repair, including inguinal, umbilical or ventral hernia (16%), gallbladder or bile duct disease (12.6%), malignant disease (9.6%), appendiceal disease (7.1%), abdominal pain (5%), gastrointestinal bleeding (3.8%) and bowel obstruction (2.8%). The most common surgical interventions included hernia repair (22.2%), breast surgery (9.9%), appendectomy (9.7%), cholecystectomy (7.8%), partial, total or sleeve gastrectomy (6.2%), hemicolectomy (2.6%), and rectal surgery (2.1%).
Length of hospital stay
Median LOHS was longer in patients with diabetes (4.0 days; interquartile range [IQR]: 3.0–6.0 days), compared to those without diabetes (3.0 days; IQR: 2.0–7.0 days). As LOHS was skewed, a common normalizing transformation of 1/LOHS was used and the mean was 0.29 (95% CI, 0.28–0.29) for patients with diabetes and 0.36 (95% CI, 0.35–0.36) (p < 0.001) for patients without diabetes, supporting the association between LOHS and BMI category.
The Longest LOHS was evident among underweight patients, whether they had diabetes (10.4 ± 13.6 days) or not (6.2 ± 8.2). The LOHS of those BMI in the normal range was longer among those with diabetes (6.4 ± 8.2 days), compared to those without diabetes (4.5 ± 5.6 days). Among all patients, regardless of the diabetes status, the LOHS was similar for overweight, obese and severely obese patients (4.3 days for non-diabetic patients and 5.6 days for diabetic patients, Table 3).
Mortality
30-day mortality
Overall 30-day mortality was 2% (544/27,930), including 2% of patients without diabetes (371/22,297 patients) and 3% of those with diabetes (173/5,342 patients).
In the total study cohort, underweight patients had the highest mortality risk (6%), followed by subjects with BMI in the normal range (3%). Mortality was similar in patients who were overweight, obese or severely obese (1%).
Among subjects with diabetes, BMI has an inverse association with 30-day mortality risk. The 30-day mortality risk decreased in parallel to increasing BMI. It was 10% for underweight patients, 6% for normal-weight patients, 3% for overweight patients, 2% for obese patients and 1% for the severely obese patients. Similarly, BMI < 18.5 kg/m2 was also associated with the maximal mortality risk in those without diabetes (6%), followed by patients with BMI in the normal range (2%), but there was no statistically significant difference in mortality risks for patients who were overweight, obese and severely obese (1%).
Diagnosis of diabetes was associated with a 2-times greater 30-day mortality risk (OR = 2.0, 95% CI, 1.7–2.4). Adjustment for age, gender, ischemic heart disease, congestive heart failure, hypertension, cerebrovascular disease, malignancy, chronic renal failure, smoking and alcohol attenuated that risk, though it was still significantly higher with diabetes (aOR = 1.3, 95% CI, 1.1–1.6). The association remained significant following further adjustment for surgery type and admission year (aOR = 1.2, 95% CI, 1.0–1.5).
For each of the BMI categories in our study, mortality risk was higher for patients with diabetes, compared to patients without diabetes, except for severe obesity category. Mortality risk associated with severe obesity was similar for patients suffering from diabetes and those without (1%).
Mortality at the end of follow-up
The median follow-up time for all patients was 2.4 years (IQR: 1.2–3.7 years); the median follow-up time for patients with diabetes was 2.1 years (IQR: 1.0–3.5 years) and for those without diabetes it was 2.4 years (IQR: 1.2–4.1 years). At the end of follow-up, mortality rate was 11% (2,921/27,639), including 18% of subjects with diabetes (949/5,342 patients) and 9% of those without diabetes (1,972/22,297 patients). Kaplan–Meier curves revealed that all patients, including those with diabetes and those without diabetes, survived better when BMI was above 25 kg/m2, compared to subjects with normal BMI and the prognosis was worse for those with BMI lower than 18.5 kg/m2. (Fig. 1).
In overall survival model, there was a significant interaction between BMI categories and diabetes status (p = 0.005) and the comparisons between the different BMI categories were computed from the model with this interaction. In patients with diabetes, an inverse association between mortality and BMI at the end of follow-up was shown. Mortality risk was highest for patients who were underweight (52%) and thereafter there was a decrease in mortality risk with increasing BMI category, from 29% for patients with normal BMI, 17% for overweight, 14% for obese patients and lowest mortality risk was evident for severely obese patients (7%).
Similar findings were evident in those without diabetes with maximal mortality risk for underweight patients (22%), followed by 11% mortality risk for those with normal BMI, 7% for overweight, 6% for obese and 4% for severely obese patients without diabetes. Assessing mortality risk by diabetes status revealed that in every BMI category separately, the risk was greater in those with diabetes, compared to subjects without diabetes (Table 3).
In subjects with diabetes and in those without diabetes, the adjusted hazard ratios (aHR) for mortality at the end of follow-up, compared to normal BMI, indicated the risk was three times greater for underweight patients, while for overweight, obese and severely obese patients, the mortality risk was 30–60% lower, compared to normal BMI (Table 3, Model 2). Adjustment for LOHS had no statistically significant impact on mortality risk (Table 3, model 3). Further adjustment for surgery type and admission year had no statistically significant impact on the findings (Table 3, Model 4).
In all participants, regardless of their diabetes status, (diabetic as well as non-diabetic), the adjusted HR for mortality at the end of follow-up was significantly higher for underweight patients in comparison with all other BMI categories. In patients with diabetes, severe obesity was found to be protective compared to all other BMI categories, except obesity. Among those without diabetes, severe obesity was also associated with decreased mortality risk, compared with underweight or normal weight, but there was no statistically significant difference compared to those with overweight or obesity (Table 3).
To account for missing data on smoking and alcohol, missing data categories were created for both. In patients without diabetes, mortality risk was not statistically significantly different for smokers vs. non-smokers, but was lower for those with missing data on smoking habits vs. non-smokers (HR,0.59; 95% CI, 0.4–0.8). Furthermore, compared with patients who did not consume alcohol, mortality risk was not statistically significantly different for those who consumed alcohol, but the risk was increased for those with missing data on alcohol consumption (HR 1.6; 95% CI, 1.2–2.2). On the other hand, in patients with diabetes there was no statistically significant difference in mortality risk for each of the 3 categories of smoking or alcohol consumption.
A separate model with the interaction between BMI and diabetes included both patients with diabetes and those without diabetes. In patients without diabetes, the aHRs for mortality at the end of follow-up, compared to normal BMI, were 2.3 (2.0–2.7) for underweight patients, 0.6 (0.6, 0.7) for overweight patients, 0.6 (0.5, 0.7) for obese patients and 0.5 (0.4, 0.6) for severely obese patients. In patients with diabetes, the aHRs for mortality at the end of follow-up, compared to normal BMI, were 2.3 (1.7–3.3) for underweight patients, 0.6 (0.5, 0.7) for overweight patients, 0.6 (0.5, 0.7) for obese patients and 0.4 (0.3, 0.5) for severely obese patients.
Age categories
Patients were divided to age groups according to registered age on their first admission: young-less than 40 years of age, middle-aged—between 40 and 65 years of age and elderly—65 years of age and above. Of patients without diabetes, 6,722 were young, 8,475 were middle-aged and 7,100 were elderly. The group of subjects with diabetes included 372 young, 1,905 middle-aged and 3,065 elderly patients.
Patients without diabetes
Mortality risk at the end of follow-up was 1% (60/6,722 patients for young patients, 5% (462/8,475 patients) for middle-aged and 20% (1,405/7,100 patients) for elderly patients.
For young patients (< 40 years) there was no statistically significant difference in risk for mortality at the end of follow-up between patients in different BMI categories.
Among middle-aged patients, underweight was associated with a significantly increased mortality risk, compared to all other BMI categories (aHR = 4.6, 95% CI = 3.1–6.7, compared to normal weight). Severe obesity was again found to be protective, compared to underweight (aHR = 0.07, 95% CI = 0.04–0.1), or normal weight (aHR = 0.3, 95% CI = 0.2–0.5).
This pattern was similar in elderly patients, as underweight was associated with an increased mortality risk compared to all other BMI categories (aHR = 2.6, 95% CI = 2.0–3.3, compared to normal weight). Severe obesity was again found to be protective, compared to underweight (aHR = 0.2, 95% CI = 0.1–0.3), or normal weight (aHR = 0.6, 95% CI = 0.4–0.8).
Patients with diabetes
We did not analyze mortality cases of young patients, due to the small number of deceased patients < 40 years of age with diabetes.
Risk for mortality at the end of follow-up was 9% for middle-aged (176/1,905 patients) and 25% (770/3,065 patients) for elderly patients.
Among middle-aged patients, underweight was associated with a significant increased mortality risk, compared to all other BMI categories (aHR = 2.8, 95% CI = 1.0–8.2, compared to normal weight). Severe obesity was again found to be protective, compared to all other BMI categories, including underweight (aHR = 0.1, 95% CI = 0.04–0.4), normal weight (aHR = 0.3, 95% CI = 0.2–0.7), overweight (aHR = 0.50, 95% CI = 0.3–0.9), or obesity (aHR = 0.5, 95% CI = 0.3–1.0).
This pattern was similar in elderly patients, as underweight was found to be associated with an increased mortality risk when compared to other BMI categories (aHR = 2.8, 95% CI = 1.7–4.6, compared to normal weight). Severe obesity was found to be protective, compared to underweight (aHR = 0.1, 95% CI = 0.07–0.2), normal weight (aHR = 0.4, 95% CI = 0.2–0.5), or overweight (aHR = 1.7, 95% CI = 1.1–2.7), but not compared to obesity.
Discussion
Obesity poses an increased risk of mortality in the general population [6,7,8,9,10], with higher mortality paralleling higher BMI [11]. It is to be expected then, that similar pattern will characterize the surgical population. However, the ‘obesity paradox’ has been described for overweight and obese individuals admitted to the ICU or undergoing vascular surgery [35,36,37], showing a reverse J-shaped relationship, in which the underweight and severely obese individuals tend to have worse outcome [37]. We found that BMI increase has a step-wise protection from death after surgical procedures, both short and long-term, in patients with and without diabetes.
We, too, found that short-term mortality in our cohort was maximal for underweight patients. Being underweight was previously associated with an increased risk for mortality in certain post-surgical conditions [35]2936, but not in others [38]24,26. Underweight patients might suffer from a chronic condition that renders them with lower overall reserve and hence prone to post-operative complications and death.
There is ambiguity in the literature regarding the association between increased BMI and short-term mortality after surgery. In line with our results, a meta-analysis including more than 400,000 patients undergoing cardiac surgery, found that overweight and obese patients have lower short-term mortality rates in comparison to normal weight individuals [25]. The same was shown in patients with surgical peritonitis [39], after hip surgery [29], post-vascular surgery [36] and among patients in the surgical ICU [35]. However, overweight and obesity were also shown to increase short-term mortality after multiple types of elective surgery [24] and surgical intervention for gastric, colorectal and pancreatic cancer [40,41,42]. We found a lower short-term mortality as BMI increases and the difference was more pronounced in patients with diabetes. We speculate that this finding can be explained by improved perioperative care, but a thorough literature search did not yield any objective evidence on the subject. Since obese patients are prone to complications and mortality, the medical staff takes extra-care in this critical period and this may have an important effect on complication and survival.
Unlike the debate in the literature regarding underweight, overweight and obese patients, there is a general consensus that severe obesity increase LOHS, short-term mortality and morbidity after surgical procedures [20, 35, 36, 43,44,45]. We found that severely obese patients had the best short- and long-term mortality rates and LOHS that was similar to overweight and obese patients. Perhaps the reason for this improved outcome lies in better general care of all CV risk factors in these patients, with vigilant attention and preventive measures of surgical complication.
The association between long-term mortality and BMI is less reported. Our data show that in subjects with diabetes (and a similar trend was seen in those without diabetes), a higher BMI is associated with lower mortality risk. It is not surprising that cancer patients undergoing surgery will have better long-term prognosis if they are not underweight [19, 28, 30, 31, 46, 47]. In this condition, low weight may represent catabolism due to extensive disease and lower reserve. Nevertheless, in our cohort only 8.1% of the patients (2249 patients) had history of malignancy, hence, this cannot explain our results. In line with our results, other reports found increased long term death in underweight patient after surgery [48] [27] [39]. Additionally, another study from the general surgery ward, found that long term mortality is increased in underweight patients and decreased in overweight and obese patients, but no difference was demonstrated across the different obesity categories [46]. Long term mortality is affected by many parameters. First, it is related to the baseline illness that brought the patient to require surgery. Second, it is related to the success of the surgical procedure and the ability of the patient to recuperate after the operation. But most importantly, it is affected from an innate condition of the patient. Could obesity and even morbid obesity change the baseline condition of the patients in a way that actually extends life? Could there be another hormone or mediator that we are yet to reveal?
We found that hospital LOHS in patients with or without diabetes was longer in underweight patients in comparison to normal weight patients. In addition, for patients with diabetes, overweight, obese and severely obese patients had shorter LOHS in comparison to normal weight individuals. These results are in agreement with others who found longer LOHS for underweight patients [28, 32, 44] or shorter LOHS for overweight/obese patients, but are contradictive to others who found increased LOHS for overweight/obese [32, 45, 49].
Finally, we found that diabetes was a risk factor for extended hospital stay, as well as short- and long-term mortality after surgery. This is in line with others, who found that diabetes was shown to be related to increased LOHS, ICU admission, perioperative morbidity and mortality after several types of surgery [24] 33, 34]. Diabetes causes micro- and macrovascular complications and despite improved hospital care, these patients have several underlying conditions that may affect their LOHS and short-term mortality. As for the long term, the increased mortality in patients with diabetes might not even be related to the surgical procedure per se.
We acknowledge several study limitations, including the retrospective design, potential unmeasured confounders, along with limitations associated with the use of self-report data or information from the medical records. Furthermore, the analysis is based on all-cause mortality and the specific cause of death is not available. Additional limitation of the study is the lack of data on weight changes during time of follow-up, medication taken, glycemic control, functional status and overall fitness. In addition, the patients reviewed here were admitted to the surgical wards with a variety of medical conditions, which might affect our results. Our analysis only focused on the first admission and not the time-varying effect of multiple admissions for some patients, as well as other time-varying variables including BMI, length of stay. Due to the large amount of missing data for smoking and alcohol status, we also completed a separate analysis for patients with and without available data on these habits. Our results showed that mortality risk was identical for those with and without available data on smoking and alcohol. Another limitation was the potential misclassification bias caused by using BMI as a measure of obesity. Additionally, reverse causation is an important factor, as for example, underweight can be caused by a number of chronic conditions that may put the patient at increased risk for mortality and complications. However, our patients underwent several types of surgical interventions, implying that not all of them had serious operations due to severe baseline condition, only 8.1% of our patients (2249 patients) had cancer (at any stage) and 1.53% (430 patients) had chronic renal failure.
Our study also has several strengths. First, the large number of patients and the multiple types of surgical procedures, validates our results. Second, the length of the follow-up adds important information regarding the long-term effects of obesity and severe obesity on surgical outcomes. Third, since there are a few surgical wards and several surgeons in our hospital, the results cannot be attributed to one distinct mode of action of a certain ward, but can be generalized. Moreover, we adjusted our results for key comorbities. In addition, the fact that our data was collected in a single-center limits the risk for heterogeneity.
In summary, this study suggests a protective effect of severe obesity in patients admitted to general surgery wards, regardless of their diabetes status. Short- and long-term mortality risks were greatest in underweight patients (diabetic or non-diabetic). Unsurprisingly, for each BMI category, mortality at the end of follow-up was higher for diabetic patients versus non-diabetic patients. Our results indicate that surgical procedures are not only safe in the obese and severely obese population, but the short and long term outcome tends to be improved in comparison to normal weight individuals.
References
Obesity: preventing and managing the global epidemic (2000) Report of a WHO consultation. World Health Organ Tech Rep Ser 894:1–253.
Gurwitz AA (2014) The spreading worldwide obesity epidemic. J Community Health 39(5):827
Imes CC, Burke LE (2014) The obesity epidemic: the united states as a cautionary tale for the rest of the world. Curr Epidemiol Rep 1(2):82–88
Misra A, Khurana L (2008) Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metab 93(11 Suppl 1):S9-30
Must A et al (1999) The disease burden associated with overweight and obesity. JAMA 282(16):1523–1529
Flegal KM et al (2007) Cause-specific excess deaths associated with underweight, overweight and obesity. JAMA 298(17):2028–2037
Berrington de Gonzalez A (2010) Body-mass index and mortality among 1.46 million white adults. N Engl J Med. 363(23):2211–2219
Calle EE et al (1999) Body-mass index and mortality in a prospective cohort of US adults. N Engl J Med 341(15):1097–1105
Khan SS et al (2018) Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA Cardiol 3(4):280–287
Calle EE et al (2003) Overweight, obesity and mortality from cancer in a prospectively studied cohort of US adults. N Engl J Med 348(17):1625–1638
Bhaskaran K et al (2018) Association of BMI with overall and cause-specific mortality: a population-based cohort study of 36 million adults in the UK. Lancet Diabetes Endocrinol 6(12):944–953
Docimo S Jr et al (2018) Increased incidence of surgical site infection with a body mass index >/= 35 kg/m(2) following abdominal wall reconstruction with open component separation. Surg Endosc 19:356
Nelson JA et al (2014) A population-level analysis of abdominal wall reconstruction by component separation in the morbidly obese patient: can it be performed safely? J Plast Surg Hand Surg 48(5):305–311
Ghanta RK et al (2017) Obesity increases risk-adjusted morbidity, mortality and cost following cardiac surgery. J Am Heart Assoc 6(3):365
Soderback H et al (2019) Incidence of wound dehiscence after colorectal cancer surgery: results from a national population-based register for colorectal cancer. Int J Colorectal Dis 34(10):1757–1762
Sood RF et al (2019) Respiratory failure following abdominal wall reconstruction: an analysis of the nationwide inpatient sample. Plast Reconstr Surg 143(1):165e–171e
Meijs AP et al (2019) The effect of body mass index on the risk of surgical site infection. Infect Control Hosp Epidemiol 40(9):991–996
Pedersen AB et al (2017) Impact of body mass index on risk of acute kidney injury and mortality in elderly patients undergoing hip fracture surgery. Osteoporos Int 28(3):1087–1097
Smith RK et al (2014) Impact of BMI on postoperative outcomes in patients undergoing proctectomy for rectal cancer: a national surgical quality improvement program analysis. Dis Colon Rectum 57(6):687–693
Thornqvist C et al (2014) Body mass index and risk of perioperative cardiovascular adverse events and mortality in 34,744 Danish patients undergoing hip or knee replacement. Acta Orthop 85(5):456–462
Nasraway SA Jr et al (2006) Morbid obesity is an independent determinant of death among surgical critically ill patients. Crit Care Med 34(4):964–970
Visscher TL et al (2000) Underweight and overweight in relation to mortality among men aged 40–59 and 50–69 years: the Seven Countries Study. Am J Epidemiol 151(7):660–666
Flegal KM et al (2005) Excess deaths associated with underweight, overweight and obesity. JAMA 293(15):1861–1867
Mathis MR et al (2013) Patient selection for day case-eligible surgery: identifying those at high risk for major complications. Anesthesiology 119(6):1310–1321
Mariscalco G et al (2017) Body mass index and mortality among adults undergoing cardiac surgery: a nationwide study with a systematic review and meta-analysis. Circulation 135(9):850–863
Lee Y et al (2018) Relationship of body mass index and waist circumference with clinical outcomes following percutaneous coronary intervention. PLoS ONE 13(12):e0208817
Dowsey MM et al (2018) Body mass index is associated with all-cause mortality after THA and TKA. Clin Orthop Relat Res 476(6):1139–1148
Wang C et al (2018) Association of body mass index and outcomes following lobectomy for non-small-cell lung cancer. World J Surg Oncol 16(1):90
Pedersen AB et al (2016) Body mass index, risk of allogeneic red blood cell transfusion and mortality in elderly patients undergoing hip fracture surgery. Osteoporos Int 27(9):2765–2775
Mullen JT et al (2008) Impact of body mass index on perioperative outcomes in patients undergoing major intra-abdominal cancer surgery. Ann Surg Oncol 15(8):2164–2172
Ishizuka M et al (2011) Underweight patients show an increased rate of postoperative death after surgery for hepatocellular carcinoma. J Surg Oncol 104(7):809–813
Zizza C et al (2004) Length of hospital stays among obese individuals. Am J Public Health 94(9):1587–1591
Carson JL et al (2002) Diabetes mellitus increases short-term mortality and morbidity in patients undergoing coronary artery bypass graft surgery. J Am Coll Cardiol 40(3):418–423
Yong PH et al (2018) The presence of diabetes and higher HbA1c are independently associated with adverse outcomes after surgery. Diabetes Care 41(6):1172–1179
Hutagalung R et al (2011) The obesity paradox in surgical intensive care unit patients. Intensive Care Med 37(11):1793–1799
Nafiu OO et al (2011) The association of body mass index to postoperative outcomes in elderly vascular surgery patients: a reverse J-curve phenomenon. Anesth Analg 112(1):23–29
Mullen JT, Moorman DW, Davenport DL (2009) The obesity paradox: body mass index and outcomes in patients undergoing nonbariatric general surgery. Ann Surg 250(1):166–172
Buck DL, Moller MH, Danish S (2014) Clinical Register of Emergency, Influence of body mass index on mortality after surgery for perforated peptic ulcer. Br J Surg. 101(8):993–999
Utzolino S et al (2014) The obesity paradox in surgical intensive care patients with peritonitis. J Crit Care 29(5):887
Tsujinaka T et al (2007) Influence of overweight on surgical complications for gastric cancer: results from a randomized control trial comparing D2 and extended para-aortic D3 lymphadenectomy (JCOG9501). Ann Surg Oncol 14(2):355–361
Fleming JB et al (2009) Influence of obesity on cancer-related outcomes after pancreatectomy to treat pancreatic adenocarcinoma. Arch Surg 144(3):216–221
Hussan H et al (2016) Morbid obesity is associated with increased mortality, surgical complications and incremental health care utilization in the peri-operative period of colorectal cancer surgery. World J Surg 40(4):987–994
Jackson RS et al (2012) Obesity is an independent risk factor for death and cardiac complications after carotid endarterectomy. J Am Coll Surg 214(2):148–155
Alizadeh RF et al (2016) Body mass index significantly impacts outcomes of colorectal surgery. Am Surg 82(10):930–935
Hakeem AR et al (2013) Increased morbidity in overweight and obese liver transplant recipients: a single-center experience of 1325 patients from the United Kingdom. Liver Transpl 19(5):551–562
Valentijn TM et al (2013) Impact of obesity on postoperative and long-term outcomes in a general surgery population: a retrospective cohort study. World J Surg 37(11):2561–2568
Okura T et al (2018) Impact of body mass index on survival of pancreatic cancer patients in Japan. Acta Med Okayama 72(2):129–135
Upala S et al (2016) Underweight and obesity increase the risk of mortality after lung transplantation: a systematic review and meta-analysis. Transpl Int 29(3):285–296
Malik AT et al (2019) The impact of metabolic syndrome on 30-day outcomes following elective anterior cervical discectomy and fusions. Spine Phila Pa 2019 44(5):E282–E287
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Dotan, I., Shochat, T., Shimon, I. et al. The Association Between BMI and Mortality in Surgical Patients. World J Surg 45, 1390–1399 (2021). https://doi.org/10.1007/s00268-021-05961-4
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DOI: https://doi.org/10.1007/s00268-021-05961-4