Association Between Metabolic Syndrome and Outcomes in Complex Head and Neck Surgery

Abstract

Objectives:

We aim to evaluate the impact of MetS on the short-term postoperative outcomes of complex head and neck surgery patients.

Methods:

This is a retrospective cohort analysis of the 2005 to 2017 National Surgical Quality Improvement Program (NSQIP) database. NSQIP database was queried for 30-day outcomes of patients undergoing complex head and neck surgeries, defined as laryngectomy or mucosal resection followed by free tissue transfer, similar to prior NSQIP studies. Patients with hypertension, diabetes, and body mass index (BMI) >30 kg/m² were defined as having MetS. Adverse events were defined as experiencing readmission, reoperation, surgical/medical complications, or mortality.

Results:

A total of 2764 patients (27.0% female) with a mean age of 62.0 ± 11.7 years were included. Patients with MetS (n = 108, 3.9%) were more likely to be female (P = .017) and have high ASA classification (P = .030). On univariate analysis, patients with MetS were more likely to require reoperation (25.9% vs 16.7%, P = .013) and experience medical complications (26.9% vs 15.4% P = .001) or any adverse events (61.1% vs 48.7%, P = .011) compared to patients without MetS. On multivariate logistic regression after adjusting for age, sex, race, ASA classification, and complex head and neck surgery type, MetS was an independent predictor of medical complications (odds ratio 2.34, 95% CI 1.28-4.27, P = .006).

Conclusion:

Patients with MetS undergoing complex head and neck surgery are at increased risk of experiencing medical complications. Identifying patients with MetS can therefore aid surgeons in preoperative risk assessment and help improve postoperative management.

Level of Evidence:

N/A

Keywords

metabolic syndrome head and neck complications NSQIP

Introduction

Metabolic Syndrome (MetS) is defined as a constellation of hypertension, diabetes, dyslipidemia, and obesity.¹ The prevalence of MetS has been reported to be approximately one-third in the United States, in a continuously rising trend, and with a higher prevalence among women.² It is reported that patients with MetS have an increased risk for coronary heart disease, stroke, and cardiovascular mortality.³ It is also believed that MetS is associated with an increased risk for cancers including non-melanoma skin cancer,⁴ laryngeal cancer,⁵ and prostate cancer.⁶ Extensive literature has reported an association between MetS and negative outcomes following various surgical procedures, including laparoscopic adrenalectomy,⁷ bariatric GI procedures,⁸ operations for hip fracture,⁹ and thyroidectomy.¹⁰ However, there is limited research on the impact of MetS on outcomes following complex head and neck surgeries (HNS). According to the latest report from the American Cancer Society, over 66 600 cases of head and neck cancer are expected in 2021,¹¹ and recent literature has also suggested an increase incidence of late-stage head and neck cancer in the United States.¹² As the complexity of head and neck anatomy is associated with intraoperative and postoperative complications,¹³ an understanding of which patient populations are at increased risk for adverse events following head and neck operations is critical and can aid with surgical planning and management.

Therefore, utilizing the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database, our present study aims to assess the association between MetS on short-term adverse events following complex head and neck surgery. It is hypothesized that compared to patients without MetS, patients with MetS have an increased risk of short-term postoperative adverse events.

Methods

Data from the ACS-NSQIP database from 2005 to 2017 were retrospectively collected and analyzed. The NSQIP database is a nationwide, multi-institutional dataset reporting several preoperative, intraoperative, and 30-day morbidity and mortality information for patients undergoing several surgical operations. Routine audits are performed of the NSQIP database to ensure its accuracy. Because the data is de-identified with restricted availability, this study did not require Institutional Review Board approval. This study focused on patients undergoing complex head and neck surgeries, which was defined as patients undergoing laryngectomy or mucosal tissue excision, including composite resection, followed by free tissue transfer, as similarly defined by prior NSQIP studies.^10,14,15 Laryngectomies commonly do not require free tissue transfer, but were included regardless based on their general complexity.¹⁶ Current Procedural Terminology (CPT) codes were used to identify these patients. CPT codes 31360, 31365, 31368, 31390, and 31395 were used to identify patients undergoing laryngectomy. The other sub-cohort of patients were included based on the occurrence of mucosal or composite resection (CPT codes: 21034, 21044, 21045, 21047, 31230, 31225, 40814, 40816, 41116, 41120, 41130, 41135, 41140, 41145, 42120, 41150, 41153, 41155, 42845, 42894) and subsequent free tissue transfer (CPT codes: 15756 15757, 15758, 20955, 20956, 20962, 20969, and 20970).

The main objective of this study was to determine whether MetS was associated with greater rates of morbidity or short-term mortality following complex HNS. MetS is defined as the occurrence of multiple factors including hypertension, diabetes mellitus, dyslipidemia, and increased abdominal circumference.^17,18 In this study, patients were defined as having MetS if the following 3 conditions were all met: (1) having a body mass index (BMI) ≥30 kg/m², (2) having hypertension requiring medication, and (3) having diabetes mellitus requiring medical intervention (both insulin and non-insulin dependent). Available data elements and definitions can be found in the NSQIP User File Guide.¹⁹ Additionally, dyslipidemia was excluded from our definition of MetS as it is not documented in the NSQIP database, which is consistent with other studies evaluating its effect on surgical outcomes.²⁰

Demographic information, perioperative clinical data, and postoperative outcomes were collected and analyzed. The American Society of Anesthesiologists (ASA) classification was binarized as low (ASA 1 or 2) and high (ASA 3 or 4). Discharge to non-home facilities included rehabilitation centers, skilled or unskilled care facilities, and separate acute care centers. All short-term outcomes were assessed within the 30-day postoperative scope of the NSQIP database. Following other NSQIP studies, postoperative complications were classified as either surgical complications, medical complications, or any complications.^10,15 Surgical complications were defined as having any of the following variables: superficial, deep, or organ space infections, wound disruption, and blood transfusions due to excessive bleeding. Medical complications consisted of the following: pneumonia, urinary tract infection, deep vein thrombosis, renal insufficiency, unplanned reintubation, pulmonary embolism, ventilator >48 hours, cerebrovascular accident, acute renal failure, myocardial infarction, cardiac arrest requiring cardiopulmonary resuscitation, sepsis, and septic shock. Any complication was defined as the presence of a surgical complication, medical complication, readmission, and/or reoperation.

All statistical analysis was performed using SPSS Statistics version 27 (SPSS Inc., Chicago, Ill., USA). Univariate analyses were performed via 2-tailed unpaired t test and Chi-square tests for continuous and categorical variables respectively. Multivariate analysis was performed via binary logistic regression to determine the effect of MetS on surgical outcomes while adjusting for clinical confounders. Confounders were determined by senior authors’ judgment and included important clinical/demographic variables or variables with P < .2 on univariate analysis. Odds ratios (ORs) and 95% confidence interval were reported. P values <.05 were considered statistically significant.

Results

2764 patients were included in the study with 1610 (60.4%) of patients undergoing a laryngectomy and 1154 (39.6%) receiving a mucosal tissue resection with free flap reconstruction. Of this entire cohort, 27% were female and 67.3% were white with a mean age of 62.1 ± 11.5 years. Of these patients, 108 (4.1%) were identified as having MetS through our definition. Clinical presentation was compared between the MetS cohort (n = 108) to the non-MetS cohort (n = 2656) and results are demonstrated in Table 1. Patients in the MetS cohort were more likely to be female (37% vs 26.6%, P = .017) and have a high-class ASA (92.6% vs 85%, P = .030). Additionally, 23.1% (n = 25) of MetS patients were smokers within the data collection year compared to 42.6% (n = 1131) in the non-MetS cohort (P < .001). The average age in the MetS cohort was 62.4 ± 10.1 years compared to 62.0 ± 11.7 in the non-MetS cohort (P = .744). Several post-operative surgical outcomes were compared between the 2 cohorts via univariate analysis. (Table 1) Of note, patients in the MetS cohort were more likely to have a reoperation (25.9% vs 16.7%, P = .013), medical complication (26.9% vs 15.4%, P = .001), and any complication (61.1% vs 48.7%, P = .011).

Table 1.

Univariate Analysis of Clinical Presentation and Surgical Outcomes Between Patients with MetS and Those Without.

Variables	MetS (108)	No MetS (2656)	P value
Gender: Female	40 (37.0)	707 (26.6)	.017
Age, y	62.4 ± 10.1	62.0 ± 11.7	.744
Race: White^*	70 (65.4)	1790 (67.8)	.613
Current smoker within 1 y	25 (23.1)	1131 (42.6)	<.001
More recent operation (2014-17)	65 (60.2)	1533 (57.7)	.611
Transferred^*	3 (2.8)	110 (4.1)	.481
ASA: High^*	100 (92.6)	2252 (85)	.030
Length of operation, min	471.3 ± 202.4	483.7 ± 199.2	.530
Reoperation	28 (25.9)	444 (16.7)	.013
Readmission^*	13 (14.1)	228 (11)	.344
Surgical complication	45 (41.7)	940 (35.4)	.182
Medical complication	29 (26.9)	409 (15.4)	.001
Mortality	0 (0)	32 (1.2)	.251
Any complications	66 (61.1)	1293 (48.7)	.011
Length of hospitalization	10.6 ± 12.9	11.2 ± 11.8	.595
Discharge to non-home^*	16 (16.5)	398 (17.4)	.814

Note. Bold = P < .05.

denominator for % adjusted for missing values.

Multivariate binary logistic regression analysis was performed to determine the association between MetS and surgical outcomes while adjusting for age, gender, race, ASA classification, and surgery type. (Table 2) This analysis demonstrated that patients with MetS were more likely to experience a medical complication (OR 2.340; 95% CI, 1.280-4.277) compared to patients without MetS even after adjusted for confounding variables. Subsequently, multivariate logistic regressions were performed comparing patients with each component of MetS alone to their respective counterparts. When comparing individuals with BMI ≥ 30 to those with BMI < 30, those with obesity were more likely to have a medical complication (OR 1.549, 95% CI, 1.078-2.226), but were less likely to be discharged to a non-home facility (OR 0.71. 95% CI, 0.509-0.995) than patients who were not obese. (Table 3) Multivariate logistic regression analysis of cohorts with hypertension alone and diabetes alone showed no significant difference in any surgical complication or postoperative outcome compared to their counterparts after adjusting for confounders (Tables 4 and 5).

Table 2.

Multivariate Logistic Regression Analyzing the Association of Metabolic Syndrome On Complex Head and Neck Surgical Outcomes, While Adjusting for Age, Gender, Race, ASA Classification, and Surgery Type.

Outcome	No MetS; OR	MetS; OR (95% CI)	P value
Length of operation	1 [Reference]	0.998 (0.997-1.000)	.021
Reoperation	1 [Reference]	1.505 (0.834-2.714)	.175
Readmission	1 [Reference]	1.035 (0.519-2.063)	.923
Surgical complication	1 [Reference]	1.119 (0.595-2.103)	.728
Medical complication	1 [Reference]	2.340 (1.280-4.277)	.006
Any complications	1 [Reference]	1.089 (0.506-2.345)	.827
Length of hospitalization	1 [Reference]	0.983 (0.944-1.023)	.389
Discharge to non-home	1 [Reference]	0.680 (0.349-1.326)	.258

Note. Bold = P < .05.

Table 3.

Multivariate Logistic Regression Analyzing the Association of BMI ≥ 30 on Complex Head and Neck Surgical Outcomes, While Adjusting for Age, Gender, Race, ASA Classification, and Surgery Type.

Outcome	BMI < 30; OR	BMI ≥ 30; OR (95% CI)	P value
Length of operation	1 [Reference]	1.001 (1.000-1.001)	.065
Reoperation	1 [Reference]	1.328 (0.938-1.879)	.110
Readmission	1 [Reference]	1.128 (0.756-1.682)	.555
Surgical complication	1 [Reference]	0.716 (0.496-1.036)	.076
Medical complication	1 [Reference]	1.549 (1.078-2.226)	.018
Any complications	1 [Reference]	0.747 (0.487-1.146)	.182
Length of hospitalization	1 [Reference]	0.989 (0.967-1.011)	.312
Discharge to non-home	1 [Reference]	0.711 (0.509-0.995)	.046

Note. Bold = P < .05.

Table 4.

Multivariate Logistic Regression Analyzing the Association of Diabetes on Complex Head and Neck Surgical Outcomes, While Adjusting for Age, Gender, Race, ASA Classification, and Surgery Type.

Outcome	No Diabetes; OR	Diabetes; OR (95% CI)	P value
Length of operation	1 [Reference]	0.999 (0.999-1.000)	.168
Reoperation	1 [Reference]	1.074 (0.740-1.558)	.709
Readmission	1 [Reference]	1.040 (0.671-1.612)	.862
Surgical complication	1 [Reference]	1.234 (0.812-1.877)	.325
Medical complication	1 [Reference]	1.100 (0.740-1.636)	.638
Any complications	1 [Reference]	1.076 (0.660-1.753)	.769
Length of hospitalization	1 [Reference]	1.008 (0.986-1.031)	.465
Discharge to non-home	1 [Reference]	0.933 (0.662-1.315)	.690

Table 5.

Multivariate Logistic Regression Analyzing the Association of Hypertension on Complex Head and Neck Surgical Outcomes, While Adjusting for Age, Gender, Race, ASA Classification and Surgery Type.

Outcome	No Hypertension; OR	Hypertension; OR (95% CI)	P value
Length of operation	1 [Reference]	1.000 (1.000-1.001)	.833
Reoperation	1 [Reference]	1.255 (0.950-1.659)	.109
Readmission	1 [Reference]	1.109 (0.803-1.531)	.530
Surgical complication	1 [Reference]	1.154 (0.848-1.571)	.361
Medical complication	1 [Reference]	1.333 (0.989-1.797)	.059
Any complications	1 [Reference]	0.901 (0.632-1.283)	.562
Length of hospitalization	1 [Reference]	0.992 (0.976-1.009)	.344
Discharge to non-home	1 [Reference]	0.954 (0.743-1.226)	.714

Discussion

Approximately 4% of all cancer in the United States are head and neck cancer, and over 14 000 deaths from head and neck cancer are estimated in 2021.²¹ Past research has called for the importance to carefully select patients for complex head and neck surgery.²² With the aging population and associated increase in the incidence of head and neck cancer among the elderly, management and planning of geriatric head and neck cases has also became more challenging.²³ Therefore, a thorough understanding of the risk factors leading to postoperative complications in head and neck surgery is critical. It has been reported that extended period of anesthesia (≥8 hours),²⁴ hepatitis,²⁴ prolonged operative time,²⁵ intraoperative fluid administration,^24,26 and ≥4 comorbidities,²⁷ are risk factors for adverse outcomes in head and neck patients. In geriatric population, a multi-institutional study conducted by Fancy et al²⁸ reported multiple risk factors for postoperative complication in major head and neck surgery, including old age (≥85 years old), Adult Comorbidity Evaluation-27 (ACE-27) score of 2 to 3, BMI of less than 25, and increased frailty. Despite the extensive literature on predictors of adverse events in head and neck patients, little is known regarding the postoperative complications associated with MetS, a risk factor for negative outcomes reported in multiple surgical procedures.^7
-10 This study sought to address this knowledge gap.

On univariate analysis, patients with MetS were more likely to have higher ASA classification, and more likely to require reoperation, experience medical complications, or any adverse events compared to patients without MetS, a finding in line with past research demonstrating ASA class as a predictor for adverse events following surgery.^29
-33 Univariate analysis also supported higher likelihood of MetS in women compared to men, although past research report conflicting findings in the gender disparity of MetS.^34
-38 More research is needed to uncover the prevalence of MetS among males and females, especially in specific surgical cohorts. In this study, after adjusting for age, sex, race, ASA classification and complex head and neck surgery type through multivariate logistic regression, MetS was an independent predictor of 30-day medical complications with an odds ratio of 2.34. These results are supported by a recent study published by Panayi et al,³⁹ where the authors likewise analyzed the ACS-NSQIP database and found that patient with MetS undergoing microvascular head and neck reconstruction are more at risk for medical complications. However, this study is the first to characterize the negative outcomes of MetS among the uniquely-defined complex head and neck surgery cohort.

This study’s multivariate logistic regression analysis also revealed that diabetes and hypertension were not significant predictors for 30-day adverse events following complex head and neck surgery, but found MetS to have a greater OR for medical complications compared to obesity alone (OR: 2.340 vs OR: 1.549, respectively). Our finding that MetS is a stronger prognostic indicator compared to its individual constituents correlates with studies on the clinical outcomes of patients with SARS-CoV-2,⁴⁰ risk of atrial fibrillation,⁴¹ colorectal cancer surgery,⁴² hip and knee arthroplasty,⁴³ and operative diverticulitis.⁴⁴ Our study supplements the existing literature by reporting an increased risk for short-term medical complication among patient with MetS undergoing complex head and neck surgery. Importantly, this MetS cohort had a reduced rate of smoking compared to the non-MetS counterpart. Smoking has been associated with worse postoperative outcomes in head and neck cancer populations⁴⁵ and the reduced rates may underestimate the negative impact of MetS on postoperative morbidity. We suggest the importance of recognizing MetS as an independent prognostic indicator for adverse outcomes which warrant the early identification and risk management for patients with multiple comorbidities including hypertension, diabetes and obesity. Appropriate identification of patient with MetS can also help with timely implementation of lifestyle changes. Medications have been shown to reduce the negative impact of MetS,⁴⁶ and short-term decreases in the prevalence of MetS have been observed following a 10-week tobacco cessation for smokers.⁴⁷ Future research should identify methods to effectively manage MetS in patients undergoing complex head and neck surgery and evaluate the impact of early intervention in perioperative and postoperative outcomes.

Despite the significant associations observed in our study, it does not come without limitations. As a retrospective study evaluating the ACS-NSQIP database, we were only able to report correlations not causation. More research on the mechanistic relationship between metabolic syndrome and adverse events following head and neck surgery is needed. Several other limitations also exist due to the intrinsic limitation of the ACS-NSQIP database. First, the ACS-NSQIP database only reported 30-day outcomes, therefore we were unable to evaluate the long-term impact of MetS. Secondly, as the ACS-NSQIP database does not include the reporting of dyslipidemia, we could not consider dyslipidemia while identifying patient with MetS. However, this is the standard utilized by past research on MetS utilizing the ACS-NSQIP database.^10,44 Finally, significant confounding variables may exist in the ACS-NSQIP database due to different institutional reporting and coding. Further institutional studies are needed to address these confounders.

Conclusion

Management and outcomes of patients undergoing complex head and neck surgery appear to be dependent on the presence of MetS. On univariate analysis, patients with MetS were more likely to require reoperation, have a medical complication, or experience any adverse event. Multivariate analysis indicated that MetS was an independent predictor of a medical complication. These findings support the early identification and management of MetS in patients undergoing complex head and neck surgery and for further research into the impact of early intervention.

Footnotes

Acknowledgements

The authors have no acknowledgments.

Authors’ Note

Portions of this work were submitted to the 2022 Triological Combined Sections Meeting

Author Contributions

Milind Vasudev: Conceived and designed research, analyzed data, interpreted results of data, prepared figures, drafted manuscript, and edited and revised manuscript.

Khodayar Goshtasbi: Conceived and designed research, analyzed data, interpreted results of data, and edited and revised manuscript.

Leo L. T. Meller: Interpreted results of data, prepared figures, drafted manuscript, and edited and revised manuscript.

Tjoson Tjoa: Conceived and designed research, interpreted results of data, drafted manuscript, and edited and revised manuscript.

Edward C. Kuan: Conceived and designed research, interpreted results of data, drafted manuscript, and edited and revised manuscript.

Yarah M. Haidar: Conceived and designed research, interpreted results of data, drafted manuscript, and edited and revised manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Milind Vasudev

Edward C. Kuan

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