Obesity and its influence on liver dysfunction, morbidity and mortality after liver resection

Introduction

The rates of obese and morbidly obese patients are increasing worldwide (1). Obesity is associated with hyperlipidemia, arterial hypertension and diabetes, thus representing a significant risk factor for an increased all-cause mortality (2). In liver surgery, morbidly obese patients have been found to be at higher risk for a dismal outcome including surgical-site infections and major complications (3). Obesity was identified as one of the main causes of hepatic steatosis, which has become the most common chronic liver disease in the Western world (4). In line with that, the global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25% with the highest prevalence in the Middle East and South America and the lowest in Africa (5). The presence of NAFLD is associated with an increased risk for developing liver-related complications and hepatocellular cancer (6).

In liver surgery, post-hepatectomy liver failure (PHLF) remains a major cause of mortality (7). Its rate ranges between 0% to 43.1%, depending on its definition (8). There is experimental and clinical data showing that hepatic regeneration is impaired in steatotic livers (9-11). However, the influence of obesity and NASH on PHLF and morbidity is unclear (3,12-14). This study investigated postoperative outcomes after liver resection (LR) in a large cohort of patients. We present this article in accordance with the STROBE reporting checklist (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-22-291/rc).

Methods

This was a retrospective single-center study approved by the institutional review board of the Medical University of Vienna (IRB number: 1368/2021) and performed in accordance with the principles of the Declaration of Helsinki (as revised in 2013). Informed consent has not been achieved due to the retrospective character and anonymous data collection.

Consecutive patients ≥18 years who underwent elective LR at the Medical University of Vienna between December 2004 and May 2017 were included in this study. Relevant data was collected from the in-hospital database registry.

Patients divided into three BMI groups according to the World Health Organization definition of obesity (normal weight: BMI 18.5–24.9 kg/m², overweight: BMI 25.0–29.9 kg/m², obese: BMI ≥30 kg/m²) (15). LRs were classified as minor (<3 liver segments) and major (≥3 liver segments) (16).

All patients were further divided into “healthy” without comorbidities and “unhealthy” with comorbidities, such as diabetes, arterial hypertension, coronary artery disease (CAD) and chronic obstructive pulmonary disease (COPD) (17).

To evaluate preoperative liver function, indocyanine green (ICG) clearance was used. ICG clearance testing [plasma disappearance rate (PDR) and ICG retention rate at 15 minutes (R15)] was performed on the day of administration. Patients received 0.25 mg/kg ICG intravenously. PDR and R15 were measured by pulse spectrometry.

Statistical analysis

SPSS^® version 26.0 software (IBM Inc., Armonk, NY, USA) was used for statistical analyses. Figures were created with GraphPad Prism 9 (Graph Pad Software Inc., La Jolla, CA, USA) and with BioRender.com.

Normally distributed values were expressed as mean ± standard deviation (SD) or otherwise as median with interquartile range (IQR). Comparative analyses were performed by Kruskal-Wallis test or analysis of variance (ANOVA), as appropriate. Significant P values by the Kruskal-Wallis test were checked pairwise for significance by Mann Whitney U, according to the closed testing principle. To assess risk stratification for obesity, multivariable analysis using logistic regression was performed to determine unadjusted and risk-adjusted complication and mortality rates. A P value <0.05 was considered statistically significant.

For power analysis, a logistic regression for one normal covariate was conducted with nQuery (nQuery Sample Size Software, Graph Pad Software DBA Statistical Solutions, San Diego CA 92108), according to our historically PHLF rate of 15%. When sample size is 883, the logistic regression test of β=0 (α=0.05 two-sided) will have 90% power to detect a β of 0.323 [an odds ratio (OR) of 1.381].

Endpoints

The primary study endpoint was PHLF as defined by the International Study Group of Liver Surgery (ISGLS) classification (abnormal bilirubin levels and prothrombin time on or after postoperative day 5) (18).

The secondary endpoints were morbidity and mortality according to the Clavien-Dindo Classification (19) and grade of fibrosis and steatosis according to the nonalcoholic steatohepatitis (NASH) Clinical Research Network (CRN) scoring system (20). Specimens were rendered according to the scoring system into “no steatosis” (0 points), “not-NASH” (1–2 points), “borderline NASH” (3–4 points) or “NASH” (≥5 points). Fibrosis grading according to the NASH CRN was based on a 4-staged system. Stage 1 is perisinusoidal/periportal fibrosis. Stage 2 is stage 1 with additional portal/periportal fibrosis, stage 3 is bridging fibrosis and stage 4 is cirrhosis.

Results

The database consisted of 1,052 patients. Twenty-one patients with a BMI <18.5 kg/m² and 143 patients with singular atypical resections were excluded. A total of 888 patients were finally included in the study.

Patient characteristics

Median age was 62.5 years (IQR, 54–69 years). The main indications for LR were colorectal liver metastases (CLM) (n=366; 41.2%), cholangiocarcinoma (n=158; 17.8%) and hepatocellular carcinoma (HCC) (n=137; 15.4%). Median BMI was 26 (IQR, 23–29) kg/m². Normal body weight was found in 361 (40.7%) patients, 360 were overweight (40.5%) and 167 were obese (18.8%). According to the WHO subdivision of obesity, 126 patients could be classified for obese class I (BMI: 30–34.9 kg/m²), 31 patients obese class II (BMI: 35–39.9 kg/m²) and 10 patients obese class III (BMI: ≥40 kg/m²). Patient characteristics, surgical indications and procedures are reported in Table 1. Overweight and obese patients were predominantly male [normal weight: n=175 (48.5%), overweight: n=240 (66.7%), obese: n=97 (58.1%); P<0.001], had a significantly higher rate of diabetes [normal weight: n=19 (5.3%), overweight: n=51 (14.2%), obese: n=38 (22.8%); P<0.001], and arterial hypertension [normal weight: n=95 (26.3%), overweight: n=105 (29.2%), obese: n=67 (40.1%); P=0.005]. One patient of the obese group underwent omega loop gastric bypass previous to liver surgery.

Preoperative evaluation of liver function and surgical technique

ICG clearance testing (PDR, R15) was performed in 633/888 patients. The median PDR in the normal weight group was 22%/min (18.0–26.2), median R15 was 3.7% (2.0–7.0%). For the overweight group median PDR was 20.2%/min (17.5–24.0), median R15 was 4.9% (2.5–7.0%). For the obese group median PDR was 21.0%/min (18.0–24.7), median R15 was 4.4% (2.5–7.0%) (PDR: P=0.079, R15: P=0.086).

Laparoscopic liver surgery was performed in 9/361 (2.5%) patients in the normal weight group, 3/360 (0.8%) patients of the overweight group and 9/167 (5.4%) of the obese group. Cavitron Ultrasonic Surgical Aspirator (CUSA Clarity, Integra Life Science Corp., USA) and Thunderbeat^TM (Olympus Medical Systems Corp., Tokyo, Japan) are used as standard technique for LR at our department.

Intermittent Pringle maneuver was used in 54/361 (15.0%) patients of the normal weight group, 52/360 (14.4%) patients of the overweight group and 18/167 (10.8%) patients of the obese group. Portal vein embolization was used in 28/361 (7.8%) patients in the normal weight group, 34/360 (9.4%) patients of the overweight group and 10/167 (6.0%) patients in the obese group. Multi-Step approach [mini-associating liver partition and portal vein ligation for staged hepatectomy (ALPPS)] was performed in 4/361 (1.1%) patients in the normal weight group, 3/360 (0.8%) patients of the overweight group and 1/167 (0.6%) patients of the obese group.

Steatosis and fibrosis

The rates of NASH and borderline NASH were significantly higher in the obese and overweight group compared to normal weight patients [NASH: normal weight: n=58 (16.1%), overweight: n=84 (23.3%), obese: n=69 (41.3%); P<0.001] (Table 2, Figure 1A), this was verified by pairwise testing [normal weight vs. overweight (P=0.001), normal weight vs. obese (P<0.001), overweight vs. obese (P=0.001)]. Furthermore, there was a significantly higher fibrosis grading in the overweight and obese cohort compared to normal weight patients (P=0.014) (Table 2, Figure 1B).

Figure 1 Histopathological features of the resected liver. (A) The rate of NASH was 16.1% in normal weight patients, 23.3% in the overweight cohort and 41.3% in obese patients (P<0.001). (B) In line with that, there was a significant increase in fibrosis grading in higher weight patients compared to normal weight patients (P=0.014). NASH, nonalcoholic steatohepatitis.

PHLF

PHLF occurred in 133 (15.0%) of all patients and was comparable between groups [normal weight: n=59 (16.3%), overweight: n=55 (15.3%), obese: n=19 (11.4%); P=0.32] (Table 3 and Figure 2).

Figure 2 PHLF: the rate of PHLF did not show a statistically relevant difference in the analyzed weight groups (normal weight: 16.3%, overweight: 15.3% and obese: 11.4%) (P=0.32). PHLF, post-hepatectomy liver failure.

At univariate binary logistic regression analysis, BMI (kg/m²) was no significant predictor of PHLF [OR: 0.99, 95% confidence interval (CI): 0.95–1.04; P=0.73] (Table 4). No relevant sensitivity or specificity could be shown with an AUC (area under the curve) 0.48 (95% CI: 0.42–0.53) (Figure S1).

At multivariable logistic regression analysis of age, gender, BMI, neoadjuvant chemotherapy, diabetes, hypertension, COPD, CAD, fibrosis, NAFLD activity score, Clavien-Dindo classification and resection group (major/minor), only major resections remained a significant predictor for PHLF (OR: 2.8, 95% CI: 1.87–4.27; P<0.001) (Table 5).

When PHLF was compared in “obese” patients vs. “rest”, PHLF occurrence was not significantly different [normal weight + overweight patients: n=114/721 (15.8%) vs. obese patients n=19/167 (11.4%), P=0.15]. For the multivariable analysis, none of the tested factors (gender, age, comorbidities, Pringle maneuver, NASH activity score, fibrosis, neoadjuvant chemotherapy, type of LR) remained a significant predictor of PHLF.

In patients undergoing major LR, the occurrence of PHLF was also similar between groups [normal weight: n=8 (13.6%), overweight: n=14 (16.7%), obese: n=4 (5.8%); P=0.11].

For the HCC only cohort (n=137), multivariable regression analysis showed no remaining significant predictors of PHLF, not even resection group (major/minor) remained a significant predictor. In regression analysis of the CCC only cohort (n=158), NAFLD activity score remained a significant predictor of PHLF (OR: 0.58, 95% CI: 0.363–0.953, P=0.03).

Patients with NASH did not show higher rates of PHLF compared to patients without steatosis [NASH: n=26 (12.3%) vs. no steatosis: n=77 (18%), P=0.36]. These non-significant results were similar for the not NASH and borderline NASH group, compared to patients without steatosis [not NASH vs. no steatosis: n=13 (11.8%) vs. n=77 (18%), P=0.25; borderline NASH vs. no steatosis: n=17 (12.1%) vs. n=77 (18%), P=0.29]. In a subgroup analysis of major LRs in NASH patients, no significant difference in the occurrence of PHLF between the three weight groups could be observed [normal weight: n=6/29 (20.7%) vs. overweight: n=10/44 (22.7%) vs. obese: n=3/29 (10.3%); P=0.39]. These non-significant results were similar for the not NASH and borderline NASH group [not NASH: normal weight: n=4 (22.2%) vs. overweight: n=5 (19.2%) vs. obese: n=1 (11.1%); P=0.78); borderline NASH: normal weight: n=3 (18.8%) vs. overweight: n=3 (12%) vs. obese: n=2 (25%); P=0.66].

In logistic regression analysis, fibrosis was also no significant predictor of PHLF (OR: 1.2, 95% CI: 0.98–1.60, P=0.07), only major resection in fibrotic patients showed a significant prediction of PHLF (OR: 3.0, 95% CI: 1.69–5.42, P<0.001).

Binary logistic regression analysis of patients without fibrosis (fibrosis grading 0, n=430) did not show a significant correlation of BMI or morbidity and PHLF (morbidity, OR: 0.98, 95% CI: 0.865–1.120, P=0.81; BMI, OR: 0.82, 95% CI: 0.519–1.307, P=0.41).

Morbidity and mortality

No significant difference between BMI groups with respect to morbidity and mortality was observed (P=0.45). Severe complications (Clavien-Dindo ≥ IIIb) occurred in 101 (11.3%) of all patients and were similar between all groups [normal weight: n=41 (11.4%), overweight: n=43 (11.9%), obese: n=17 (10.2%); P=0.87].

At multivariable analysis of comorbidities, fibrosis, NAFLD activity score and resection group (major/minor), none of the factors remained statistically significant (comorbidities, OR: 0.87, 95% CI: −0.51 to 0.23, P=0.48; fibrosis, OR: 1.95, 95% CI: −0.96 to 0.22, P=0.42; NAFLD activity score, OR: 1.06, 95% CI: −0.13 to 0.15, P=0.93; resection group, OR: 0.84, 95% CI: −0.49 to 0.15, P=0.30).

Intraoperative administration of red blood cell transfusions was not associated with BMI group. In the normal weight group 26 patients (7.2%) received red blood cell transfusion vs. 21 patients in the overweight group (5.8%) and 12 patients in the obese group (7.1%) (P=0.56).

The median length of stay was 11 days (IQR, 8–15 days) and was comparable between groups [normal weight: 11 days (IQR, 8–15 days), overweight: 10.5 days (IQR, 8–16 days), obese: 10 days (IQR, 8–14 days), P=0.56]. Of note, patients undergoing major resection showed a longer length of stay compared to patients with minor resections [minor LR: 9 days (IQR, 7–13 days) vs. major LR: 12 days (IQR, 9–18 days); P<0.001].

Postoperative death occurred in 27 patients (3%) and was comparable between the three groups [normal weight: n=7 (1.9%), overweight: n=12 (3.3%), obese: n=8 (4.8%); P=0.19].

Comorbidities

Patients were divided into “healthy” or “unhealthy” patients. The number of “unhealthy” patients was 114 in the normal weight group (31.6%), 141 in the overweight cohort (39.2%), and 84 in the obese group (50.2%) (P<0.001). Complications were comparable between the two subgroups (OR: 0.85, CI: −0.49 to 0.167, P=0.33). Health status was also no predictor of PHLF (OR: 0.82, CI: 0.56–1.19, P=0.31). There was no difference in PHLF in “unhealthy” patients [normal weight: n=22 (19.3%), overweight n=25 (17.7%), obese n=14 (16.7%), P=0.89]. Furthermore, morbidity and mortality were also comparable between “unhealthy” patients of the distinct weight groups (Table S1).

Discussion

Obesity has substantially increased worldwide during the last decades (1). LR in overweight and obese patients is challenging due to an increased anesthesiologic risk and a technically more demanding surgical access (21,22). Obese patients have an increased risk for prolonged operation times, severe complications and extended length of stay in hospital after different types of major abdominal surgery (23,24). Additionally, these patients have a higher risk of postoperative infections and more need of intensive care (21,25). Reports on postoperative outcome after LR in obese patients are contradictory and (12,14,26-28) direct evidence of the influence of obesity on PHLF is still lacking. Historically PHLF had no universal definition, therefore variable rates of PHLF have been described and comparison of data is difficult (11,29,30). In our study the ISGLS criteria was used to determine PHLF, to detect smaller differences in the incidence of liver regeneration-related problems.

To our knowledge, this is the largest study investigating PHLF and postoperative outcome in overweight and obese patients. We found no significant differences in PHLF, complications, and mortality between normal weight, overweight and obese patients, independent of comorbidities. Only the extent of the LR was associated with complications at multivariable analysis. Major LRs are known to be associated with PHLF, morbidity, and mortality (31-33). We assume the almost significantly lower number of major resections of our obese patients to be a possible explanation for the non-significant results of PHLF, as major resections were the only predictor for PHLF.

In the present study, obesity was associated with NASH and liver fibrosis, respectively. In our cohort, the rate of NASH was higher than expected in the general population. NASH was diagnosed in 16% of the normal weight vs. 23% in the overweight vs. 41% in the obese group. According to the literature, NASH is strongly related to diabetes and obesity and increases annually, which goes in line with our results. The prevalence of NASH among NAFLD patients who underwent a random biopsy is declared around 7%. The prevalence of NASH among NAFLD patients with a clinical indication for a liver biopsy is stated around 59% (34). Steatosis seems to be a predictor for morbidity and mortality in patients undergoing LR (35-37). A higher incidence of postoperative infections, perihepatic abscesses and postoperative bowel dysmotility was found in previous studies, whereas the frequency of major complications or length of stay was not associated with steatosis (36,37). NASH is known to be associated with an increased risk of fibrosis and cirrhosis (38,39). Severe fibrosis has been reported be associated with poorer early postoperative and late oncologic outcome, respectively (40). In contrast to that, Yang et al. found severe fibrosis not to be associated with overall morbidity in 2011 (41). This is in keeping with our results showing that fibrosis and NASH were not associated with PHLF and morbidity, while only major resections were found to be a significant predictor of PHLF.

PHLF occurred in 15.0% of our patients, which is in line with the literature in normal weight patients (8,42,43). Notably, the rate of PHLF was comparable amongst the different weight groups. Our findings stand in contrast to some recent studies showing an influence of obesity on PHLF (12,29).

The Oklahoma University carried out a retrospective database analysis of the national surgical quality improvement program data set from 2005 to 2017 (12). In this study, 36,969 patients who underwent LR were divided into two groups (BMI <35 kg/m² and BMI ≥35 kg/m²), which is different from the WHO-classification of obesity. Moreover, patients were subclassified into “healthy” (without comorbidities) and “unhealthy” patients (with comorbidities). Patients in the higher BMI group showed a 2.2 times higher risk of mortality. Although, no difference in mortality after adjustment of other covariates between “healthy” or “unhealthy” obese was reported, which goes in line with a comparison of “healthy” and “unhealthy” obese in our current analysis, where no differences of complications or mortality could be shown. This is despite of the different classification of weight groups in the Oklahoma University database (12). A recent large retrospective analysis with steatotic and normal livers of the American College of Surgeons National Surgical Quality Improvement Program database associated the metabolic syndrome with a worsened outcome after major LRs. Patients with a steatotic liver had an increased risk of overall and major complications, such as deep-surgical site infections with the need for invasive treatment (29). The observed difference to our study could be explained by the definition of liver steatosis. Whereas, in the previous study, steatosis was visually assessed by the surgeon during surgery, we performed a detailed histological grading according to NASH CRN scoring system (20). We assume that by macroscopical evaluation of the liver, lower grades of steatosis were not classified as steatotic in this analysis.

In our study obese patients had a significantly higher rate of minor LRs compared to the normal weight and overweight patients, which could be a potential bias. To address this issue, we performed a subgroup analysis studying only major LRs, which also did not show an influence of BMI on the occurrence of PHLF. Accordingly, NASH and NAFLD activity score gradings were not associated with PHLF in the overall cohort and in the major LR subgroup. Similar results to our findings were reported in 2011 showing that liver failure, biliary leakage and other severe complications occurred with equal frequency in normal weight and obese patients (44). Additionally, another study of obese patients undergoing LR in HCC also showed no difference in PHLF (13). In another study, super-obesity defined as a BMI >50 kg/m² was found to be one of the most relevant predictors for increased critical care complications such as pneumonia, septic shock and renal insufficiency in patients undergoing LR (3). According to the WHO subdivision of obesity, in our cohort, only 10 patients in our cohort had a BMI ≥40 kg/m² (obese class III), thus a subgroup analysis of this cohort would not be valuable.

One of the limitations of this study is the retrospective character and the assessment of obesity using the BMI, which does not represent the body composition (e.g., intraabdominal fat or waist-hip ratio). Also, the limited number of patients in our subgroup analysis of NASH and major resections could lead to false negative results due to the inadequate power.

However, this study states for the first time in a large cohort of patients with a detailed follow up that obesity is not associated with a higher risk for postoperative complications or PHLF in patients undergoing either minor or major LRs. We therefore conclude that LRs can be safely performed in obese patients. Future prospective studies with measurement of intraabdominal fat and the waist-hip ratio are required to identify patients, who might have an increased perioperative risk of PHLF and postoperative complications.

Conclusions

Postoperative complications and PHLF are comparable in normal weight, overweight and obese patients and LRs using modern techniques can be safely performed in these patients.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-22-291/rc

Data Sharing Statement: Available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-22-291/dss

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://hbsn.amegroups.com/article/view/10.21037/hbsn-22-291/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This was a retrospective single-center study approved by the institutional review board of the Medical University of Vienna (IRB number: 1368/2021) and performed in accordance with the principles of the Declaration of Helsinki (as revised in 2013). Informed consent has not been achieved due to the retrospective character and anonymous data collection.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Finucane MM, Stevens GA, Cowan MJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet 2011;377:557-67. [Crossref] [PubMed]
2. Moussa OM, Erridge S, Chidambaram S, et al. Mortality of the Severely Obese: A Population Study. Ann Surg 2019;269:1087-91. [Crossref] [PubMed]
3. Acosta LF, Garcia CR, Dugan A, et al. Impact of super obesity on perioperative outcomes after hepatectomy: The weight of the risk. Surgery 2017;162:1026-31. [Crossref] [PubMed]
4. Abd El-Kader SM, El-Den Ashmawy EM. Non-alcoholic fatty liver disease: The diagnosis and management. World J Hepatol 2015;7:846-58. [Crossref] [PubMed]
5. Younossi ZM, Koenig AB, Abdelatif D, et al. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64:73-84. [Crossref] [PubMed]
6. Sanyal AJ, Van Natta ML, Clark J, et al. Prospective Study of Outcomes in Adults with Nonalcoholic Fatty Liver Disease. N Engl J Med 2021;385:1559-69. [Crossref] [PubMed]
7. Skrzypczyk C, Truant S, Duhamel A, et al. Relevance of the ISGLS definition of posthepatectomy liver failure in early prediction of poor outcome after liver resection: study on 680 hepatectomies. Ann Surg 2014;260:865-70; discussion 870. [Crossref] [PubMed]
8. Mehrabi A, Golriz M, Khajeh E, et al. Meta-analysis of the prognostic role of perioperative platelet count in posthepatectomy liver failure and mortality. Br J Surg 2018;105:1254-61. [Crossref] [PubMed]
9. Taub R, Greenbaum LE, Peng Y. Transcriptional regulatory signals define cytokine-dependent and -independent pathways in liver regeneration. Semin Liver Dis 1999;19:117-27. [Crossref] [PubMed]
10. Cressman DE, Greenbaum LE, DeAngelis RA, et al. Liver failure and defective hepatocyte regeneration in interleukin-6-deficient mice. Science 1996;274:1379-83. [Crossref] [PubMed]
11. Amini N, Margonis GA, Buttner S, et al. Liver regeneration after major liver hepatectomy: Impact of body mass index. Surgery 2016;160:81-91. [Crossref] [PubMed]
12. Urdaneta Perez MG, Garwe T, Stewart K, et al. Obesity is an Independent Risk Factor for Mortality in Otherwise Healthy Patients After Hepatectomy. J Surg Res 2020;255:50-7. [Crossref] [PubMed]
13. Guo Z, Zhang J, Jiang JH, et al. Obesity Does Not Influence Outcomes in Hepatocellular Carcinoma Patients following Curative Hepatectomy. PLoS One 2015;10:e0125649. [Crossref] [PubMed]
14. He J, Liu H, Deng L, et al. Influence of obesity on in-hospital and postoperative outcomes of hepatic resection for malignancy: a 10-year retrospective analysis from the US National Inpatient Sample. BMJ Open 2019;9:e029823. [Crossref] [PubMed]
15. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894:i-xii, 1-253. [PubMed]
16. Strasberg SM. Nomenclature of hepatic anatomy and resections: a review of the Brisbane 2000 system. J Hepatobiliary Pancreat Surg 2005;12:351-5. [Crossref] [PubMed]
17. Iacobellis G, Ribaudo MC, Zappaterreno A, et al. Prevalence of uncomplicated obesity in an Italian obese population. Obes Res 2005;13:1116-22. [Crossref] [PubMed]
18. Rahbari NN, Garden OJ, Padbury R, et al. Posthepatectomy liver failure: a definition and grading by the International Study Group of Liver Surgery (ISGLS). Surgery 2011;149:713-24. [Crossref] [PubMed]
19. Clavien PA, Barkun J, de Oliveira ML, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg 2009;250:187-96. [Crossref] [PubMed]
20. Kleiner DE, Brunt EM, Van Natta M, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005;41:1313-21. [Crossref] [PubMed]
21. Mullen JT, Davenport DL, Hutter MM, et al. Impact of body mass index on perioperative outcomes in patients undergoing major intra-abdominal cancer surgery. Ann Surg Oncol 2008;15:2164-72. [Crossref] [PubMed]
22. Dindo D, Muller MK, Weber M, et al. Obesity in general elective surgery. Lancet 2003;361:2032-5. [Crossref] [PubMed]
23. Gendall KA, Raniga S, Kennedy R, et al. The impact of obesity on outcome after major colorectal surgery. Dis Colon Rectum 2007;50:2223-37. [Crossref] [PubMed]
24. Hughes TM, Shah K, Noria S, et al. Is BMI associated with post-operative complication risk among patients undergoing major abdominal surgery for cancer? A systematic review. J Surg Oncol 2018;117:1009-19. [Crossref] [PubMed]
25. Winfield RD, Reese S, Bochicchio K, et al. Obesity and the Risk for Surgical Site Infection in Abdominal Surgery. Am Surg 2016;82:331-6. [Crossref] [PubMed]
26. Langella S, Russolillo N, Forchino F, et al. Impact of obesity on postoperative outcome of hepatic resection for colorectal metastases. Surgery 2015;158:1521-9. [Crossref] [PubMed]
27. Fischer A, Fuchs J, Stravodimos C, et al. Influence of diabetes on short-term outcome after major hepatectomy: an underestimated risk? BMC Surg 2020;20:305. [Crossref] [PubMed]
28. Zogg CK, Mungo B, Lidor AO, et al. Influence of body mass index on outcomes after major resection for cancer. Surgery 2015;158:472-85. [Crossref] [PubMed]
29. Fagenson AM, Pitt HA, Moten AS, et al. Fatty liver: The metabolic syndrome increases major hepatectomy mortality. Surgery 2021;169:1054-60. [Crossref] [PubMed]
30. Cucchetti A, Cescon M, Ercolani G, et al. Safety of hepatic resection in overweight and obese patients with cirrhosis. Br J Surg 2011;98:1147-54. [Crossref] [PubMed]
31. Farges O, Goutte N, Bendersky N, et al. Incidence and risks of liver resection: an all-inclusive French nationwide study. Ann Surg 2012;256:697-704; discussion 704-5. [Crossref] [PubMed]
32. Riediger C, Mueller MW, Geismann F, et al. Comparative analysis of different transection techniques in minor and major hepatic resections: a prospective cohort study. Int J Surg 2013;11:826-33. [Crossref] [PubMed]
33. Vibert E, Pittau G, Gelli M, et al. Actual incidence and long-term consequences of posthepatectomy liver failure after hepatectomy for colorectal liver metastases. Surgery 2014;155:94-105. [Crossref] [PubMed]
34. Hamid O, Eltelbany A, Mohammed A, et al. The epidemiology of non-alcoholic steatohepatitis (NASH) in the United States between 2010-2020: a population-based study. Ann Hepatol 2022;27:100727. [Crossref] [PubMed]
35. Gomez D, Malik HZ, Bonney GK, et al. Steatosis predicts postoperative morbidity following hepatic resection for colorectal metastasis. Br J Surg 2007;94:1395-402. [Crossref] [PubMed]
36. Kooby DA, Fong Y, Suriawinata A, et al. Impact of steatosis on perioperative outcome following hepatic resection. J Gastrointest Surg 2003;7:1034-44. [Crossref] [PubMed]
37. Behrns KE, Tsiotos GG, DeSouza NF, et al. Hepatic steatosis as a potential risk factor for major hepatic resection. J Gastrointest Surg 1998;2:292-8. [Crossref] [PubMed]
38. Siddiqui MS, Vuppalanchi R, Van Natta ML, et al. Vibration-Controlled Transient Elastography to Assess Fibrosis and Steatosis in Patients With Nonalcoholic Fatty Liver Disease. Clin Gastroenterol Hepatol 2019;17:156-63.e2. [Crossref] [PubMed]
39. Ekstedt M, Franzén LE, Mathiesen UL, et al. Long-term follow-up of patients with NAFLD and elevated liver enzymes. Hepatology 2006;44:865-73. [Crossref] [PubMed]
40. Suh SW, Choi YS. Influence of liver fibrosis on prognosis after surgical resection for resectable single hepatocellular carcinoma. ANZ J Surg 2019;89:211-5. [Crossref] [PubMed]
41. Yang T, Zhang J, Lu JH, et al. Risk factors influencing postoperative outcomes of major hepatic resection of hepatocellular carcinoma for patients with underlying liver diseases. World J Surg 2011;35:2073-82. [Crossref] [PubMed]
42. Andreatos N, Amini N, Gani F, et al. Albumin-Bilirubin Score: Predicting Short-Term Outcomes Including Bile Leak and Post-hepatectomy Liver Failure Following Hepatic Resection. J Gastrointest Surg 2017;21:238-48. [Crossref] [PubMed]
43. Herbert GS, Prussing KB, Simpson AL, et al. Early trends in serum phosphate and creatinine levels are associated with mortality following major hepatectomy. HPB (Oxford) 2015;17:1058-65. [Crossref] [PubMed]
44. Viganò L, Kluger MD, Laurent A, et al. Liver resection in obese patients: results of a case-control study. HPB (Oxford) 2011;13:103-11. [Crossref] [PubMed]