Risk Factors And Complications Analysis Of Total Knee Arthroplasty In Patients With Metabolic Syndrome Associated Osteoarthritis: A 10-year Retrospective Study Of A National Inpatient Sample Database.

Risk Factors And Complications Analysis Of Total Knee Arthroplasty In Patients With Metabolic Syndrome Associated Osteoarthritis: A 10-year Retrospective Study Of A National Inpatient Sample Database. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk Factors And Complications Analysis Of Total Knee Arthroplasty In Patients With Metabolic Syndrome Associated Osteoarthritis: A 10-year Retrospective Study Of A National Inpatient Sample Database. Miaolan Yuan, Hao Xie, Yanjie He, Yinyin Qin, Jian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6591213/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to evaluate the trends in Metabolic Syndrome-associated Osteoarthritis (MetS-OA) among patients undergoing Total Knee Arthroplasty (TKA) and to identify key risk factors associated with this condition. Methods: A retrospective analysis was conducted using data from the Nationwide Inpatient Sample (NIS) from 2009 to 2019. The study examined patient demographics, hospital characteristics, length of stay (LOS), total hospitalization costs, in-hospital mortality rates, comorbid conditions, and perioperative complications. Multivariable logistic regression analysis was utilized to explore the relationship between MetS-OA and medical outcomes in TKA patients. Results: Out of 1,361,454 TKA procedures analyzed, 1,330,399 unique patients were included. The overall incidence of MetS-OA was 16.1%, with an upward trend from 2011 to 2019. Key risk factors forMetS-OA inTKA patients included advanced age, male gender, non-White racial backgrounds, and comorbidities such as chronic pulmonary disease, depression, and hypothyroidism. Patients with MetS-OA experienced longer hospital stays and incurred higher median hospitalization costs by $1,445.50, though no significant increase in mortality was observed. Besides, MetS-OA patients were more likely to experience postoperative complications, including acute myocardial infarction, severe malnutrition, acute cerebrovascular disease, postoperative delirium, acute respiratory distress syndrome (ARDS), prolonged mechanicalventilation, pneumonia, urinary tract infections, acute renal failure, and surgical complications such as lower limb nerve injury. Conclusion: The incidence of MetS-OA among TKA patients is increasing, with several patient- and hospital-related factors significantly influencing the risk of postoperative complications. Preoperative optimization of high-risk patients and the implementation of standardized treatment protocols for MetS-OA may help reduce adverse events, improve patient outcomes, and lower healthcare costs. Metabolic syndrome associated osteoarthritis (MetS-OA) total knee arthroplasty (TKA) Complications Database Risk factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Osteoarthritis (OA) is a prevalent joint disease, affecting approximately 250 million individuals globally, with a prevalence of 60% among adults aged 65 and above[ 1 , 2 ]. Clinically, osteoarthritis is categorized into several subtypes, including post-traumatic OA, age-related OA[ 3 ], and metabolic osteoarthritis (MetS-OA), which is recognized as one of the clinical phenotypes associated with metabolic syndrome (MetS) [ 4 , 5 ]. The link between osteoarthritis and metabolic syndrome has been well-documented across various populations and cultural settings[ 6 ]. As the global population continues to age, the prevalence of MetS-OA is increasing at a concerning rate, which is expected to impose a significant financial burden. Fortunately, total knee arthroplasty (TKA) has been demonstrated to be a highly effective and successful intervention for reducing pain, restoring function, and enhancing quality of life in patients with advanced knee diseases[ 7 – 10 ]. While the effects of post-traumatic OA on TKA outcomes are well-established[ 11 , 12 ], there is limited research focusing on MetS-OA. Therefore, this study was designed to achieve the following objectives: (i) assess the overall and annual incidence of MetS-OA patients undergoing TKA over the past decade; (ii) examine the adverse outcomes in these patients post-TKA; and (iii) identify risk factors related to MetS-OA that impact the TKA procedure. Utilizing a national database, the analysis included patient demographics, hospital characteristics, length of stay (LOS), total hospitalization costs, in-hospital mortality, comorbidities, and perioperative complications. 2. Material and Methods 2.1 Data Source The data utilized in this study were obtained from the Nationwide Inpatient Sample (NIS), a comprehensive database maintained by the Healthcare Cost and Utilization Project (HCUP) and supported by the Agency for Healthcare Research and Quality (AHRQ). As the largest all-payer inpatient database in the United States, the NIS provides a stratified sample derived from over 1,000 hospitals, encompassing approximately 20% of nationwide hospitalizations annually[ 13 ]. The database includes detailed information on patient demographics, hospital characteristics, LOS, total charges, payer type, in-hospital mortality, and diagnostic and procedural codes based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Since the study utilized publicly available anonymous data, ethics board approval was not required. 2.2 Cohort selection The study included patients aged 18 years or older who underwent TKA for OA between 2010 and 2019. These patients were identified using hospital discharge data, with procedure codes from both ICD-9 and ICD-10 systems (refer to the Supplemental data file) for details. Initially, a total of 1,361,454 patients were identified. However, after excluding individuals with incomplete data related to hospital characteristics and patient demographics—such as age, mortality, elective admission status, gender, LOS, insurance type, race, total charges, and hospital bed size—the final analysis comprised 1,330,399 patients (Fig. 1). The study population was categorized into two groups based on the diagnosis of METS-OA. We analyzed patient demographics, hospital characteristics, and outcome measures such as LOS, economic indicators, and in-hospital mortality. Preoperative comorbidities and perioperative complications were identified using ICD-9-CM and ICD-10-CM codes, as outlined in Table 1 . Perioperative complications included acute myocardial infarction, electrolyte imbalances, severe malnutrition, cerebrovascular events, pulmonary embolism, gastrointestinal bleeding, heart failure, renal insufficiency, pneumonia, ARDS, prolonged mechanical ventilation, urinary tract infections, acute renal failure, postoperative delirium, blood transfusions, and hemorrhagic complications such as seromas and hematomas. Table 1 Variables used in binary logistic regression analysis Variable Category Specific Variables Patient demographics Age (< 65 years and ≥65 years), sex (male and female), race (White, Black, Hispanic, Asian or Pacific Islander, Native American and Other) Hospital characteristics Type of admission (non-elective, elective), bed size of hospital (small, medium, large), teaching status of hospital (nonteaching, teaching), location of hospital (rural, urban), type of insurance (Medicare, Medicaid, private insurance, self-pay, no charge, other), location of the hospital (northeast, Midwest or north central, south, west) Comorbidities AIDS, alcohol abuse, deficiency anemia, rheumatoid diseases, chronic blood loss anemia, congestive heart failure, chronic pulmonary disease, coagulopathy, depression, drug abuse, hypothyroidism, liver disease, fluid and electrolyte disorders, other neurological disorders, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, peptic ulcer disease and valvular disease AIDS: Acquired immunodeficiency syndrome 2.3 Statistical analysis Statistical analyses were conducted using SPSS version 25.0. Continuous variables were evaluated using independent t-tests, while categorical variables were analyzed with chi-square tests (Tables 2 and 3 ). To identify potential risk factors associated with blood transfusion, logistic regression was employed. The regression model incorporated all relevant variables available in the NIS database, including patient demographics, hospital characteristics, and comorbidities (Table 1 ). Odds ratios (OR), along with their corresponding 95% confidence intervals (CI), were calculated. Given the large sample size, statistical significance was set at p < 0.05. Table 2 Patient characteristics and outcomes after total knee arthroplasty (2010–2019) Continue Characteristics Metabolic syndrome associated osteoarthritis (MetS-OA) No Metabolic syndrome associated osteoarthritis (no MetS-OA) p Characteristics MetS-OA No MetS-OA P Total (n = count) 214,448 1,115,651 Total incidence (%) 16.1 Age (mean ± MD) 66.74 ± 8.575 66.21 ± 9.983 < 0.05 Age group (%) 18–44 0.6 1.6 < 0.05 45–64 38.0 40.7 65–74 42.7 36.3 ≥75 18.8 21.4 Gender (%) Male 39.4 37.8 < 0.05 Female 60.6 62.2 Race (%) White 73.2 77.4 < 0.05 Black 10.2 7.0 Hispanic 6.2 5.1 Asian or Pacific Islander 1.5 1.2 Native American 0.5 0.4 Other 8.4 8.8 CCI (%) 1 33.5 31.8 < 0.05 2 18.0 14.5 ≥3 10.9 7.5 LOS (median, d) 3.0(2–3) 3.0 (2–3) < 0.05 TOTCHG (median, $) 50649.5 (37277.0-71285.8) 49204.0 (36002.0-69695.0) < 0.05 Type of insurance (%) Medicare 60.0 55.1 < 0.05 Medicaid 4.0 3.8 Private insurance 32.7 37.3 Self-pay 0.4 0.5 No charge 0.0 0.1 Other 2.9 3.3 Bed size of hospital (%) Small 25.7 26.8 < 0.05 Medium 27.8 27.2 Large 46.5 46.0 Elective admission (%) 96.0 95.5 < 0.05 Type of hospital (teaching %) 57.7 52.8 < 0.05 Location of hospital (urban, %) 89.7 88.7 < 0.05 Region of hospital (%) Northeast 18.0 17.8 < 0.05 Midwest or North Central 31.1 26.2 South 36.6 37.0 West 14.4 19.0 Died (%) 0.1 0.0 0.08 LOS: Length of stay, TOTCHE: Total charge Table 3 Relationship between Mets-OA and preoperative comorbidities Comorbidities Univariate Analysis Multivariate Logistic Regression No MetS-OA MetS-OA p OR 95% CI p Preoperative comorbidities Acquired immune deficiency syndrome 763 (0.1%) 142 (0.1%) 0.72 0.65 0.54–0.78 < 0.05 Alcohol abuse 10017 (0.9%) 1665 (0.8%) < 0.05 0.73 0.69–0.77 < 0.05 Deficiency anemia 75358 (6.8%) 16757 (7.6%) < 0.05 1.06 1.04–1.07 < 0.05 Rheumatoid arthritis/collagen vascular diseases 46199 (4.1%) 7394 (3.4%) < 0.05 0.77 0.75–0.78 < 0.05 Chronic blood loss anemia 10516 (0.9%) 2153 (1.0%) < 0.05 1.00 0.95–1.05 < 0.05 Congestive heart failure 24695 (2.2%) 11035 (5.1%) < 0.05 1.75 1.71–1.80 < 0.05 Chronic pulmonary disease 161892 (14.5%) 41164 (19.2%) < 0.05 1.28 1.27–1.30 < 0.05 Coagulopathy 20140 (1.8%) 4666 (2.2%) < 0.05 0.99 0.96–1.03 0.72 Depression 148103 (13.3%) 37550 (17.5%) < 0.05 1.37 1.35–1.39 < 0.05 Drug abuse 6506 (0.6%) 1244 (0.6%) 0.86 0.87 0.82–0.93 < 0.05 Hypothyroidism 180969 (16.2%) 41073 (19.2%) < 0.05 1.22 1.20–1.23 < 0.05 Liver disease 13631 (1.2%) 4239 (2.0%) < 0.05 1.43 1.38–1.49 < 0.05 Lymphoma 2369 (0.2%) 408 (0.2%) < 0.05 0.77 0.69–0.86 < 0.05 Fluid and electrolyte disorders 75509 (6.8%) 205650 (9.6%) < 0.05 1.28 1.26–1.30 < 0.05 Other neurological disorders 29780 (2.7%) 5865 (2.7%) 0.09 0.92 0.89–0.94 < 0.05 Paralysis 2083 (0.2%) 465 (0.2%) < 0.05 1.01 0.91–1.12 0.81 Peripheral vascular disorders 20531 (1.8%) 6905 (3.2%) < 0.05 1.48 1.43–1.52 < 0.05 Psychoses 21937 (2.0%) 5416 (2.5%) < 0.05 1.22 1.18–1.26 < 0.05 Pulmonary circulation disorders 9006 (0.8%) 2927 (1.4%) < 0.05 1.21 1.16–1.27 < 0.05 Renal failure 46811 (4.2%) 23612 (11.0%) < 0.05 2.46 2.42–2.51 < 0.05 Solid tumor without metastasis 5144 (0.5%) 1056 (0.5%) 0.05 0.99 0.93–1.06 0.87 Peptic ulcer disease Excluding bleeding 1542 (0.1%) 390 (0.2%) < 0.05 1.19 1.06–1.33 < 0.05 Valvular disease 35499 (3.2%) 8624 (4.0%) < 0.05 1.10 1.07–1.13 < 0.05 OR: Odds ratio, CI: Confidence interval 3. Results 3.1 Incidence of MetS-OA in patients undergoing TKA According to data from the NIS database, approximately 1,330,399 patients in the United States underwent TKA between 2010 and 2019. Among these patients, MetS-OA was identified in 214,448 cases, reflecting an estimated incidence rate of 16.1% (Table 2 ). Over the past decade, the prevalence of MetS-OA among patients undergoing TKA demonstrated a gradual upward trend (Fig. 2). 3.2 Patient demographics between the two groups A comparison of patient demographics between the two groups revealed that the mean age of individuals with MetS-OA was one year greater than those without MetS-OA (67 years vs. 66 years) ( p < 0.05 ). Besides, the MetS-OA group comprised a slightly higher proportion of male patients (39.4% vs. 37.8%) ( p < 0.05 ) (Table 2 ). Furthermore, an analysis of age distribution demonstrated a notable disparity, as the prevalence of MetS-OA was 6.4% higher among patients aged 65 to 74 years who underwent TKA (42.7% vs. 36.3%; p < 0.05 ) (Table 2 and Fig. 3a&b). In terms of racial composition, the prevalence of MetS-OA was lower among White patients compared to those without MetS-OA (73.2% vs. 77.4%; p < 0.05 )(Table 2 and Fig. 3e&f). 3.3 Hospital characteristics between the two groups As anticipated, patients without MetS-OA were 0.5% less likely to choose admission compared to those with MetS-OA (95.5% vs. 96.0%; p < 0.05 ) (Table 2 ). Furthermore, patients with MetS-OA were more frequently treated in hospitals with larger bed capacities (46.5% vs. 46.0%; p < 0.05 ) (Table 2 and Fig. 3g&h). Besides, the prevalence of MetS-OA was higher among patients in urban hospitals (89.7% vs. 88.7%) and teaching hospitals (57.7% vs. 52.8%) ( p < 0.05 ) (Table 2 ). Moreover, regional disparities were observed, with hospitals in the northeast (18.0% vs. 17.8%) and midwest/north central regions (31.1% vs. 26.2%) reporting a higher proportion of MetS-OA patients ( p < 0.05 ) (Table 2 and Fig. 3c&d). 3.4 Comorbidities Associated with MetS-OA during TKA MetS-OA was significantly associated with several preoperative comorbidities during hospitalization. Conditions such as deficiency anemia (7.6%), congestive heart failure (5.1%), chronic pulmonary disease (19.2%), depression (17.5%), hypothyroidism (19.2%), fluid and electrolyte disorders (9.6%), renal failure (11.0%), and valvular disease (4.0%) were observed to have a higher likelihood of being complicated by MetS-OA ( p < 0.05 ) (Table 3 and Fig. 4). 3.5 Risk factors associated with MetS-OA during TKA Logistic regression analysis identified several key risk factors associated with the co-occurrence of MetS-OA following TKA (Table 5 ). Advanced age emerged as a notable predictor, with OR increasing progressively across age groups: 45–64 years ( OR = 2.57; 95% CI = 2.42–2.72), 65–74 years ( OR = 2.93; 95% CI = 2.76–3.11), and ≥75 years ( OR = 1.99; 95% CI = 1.88–2.12) (all p -values < 0.05 ). Moreover, elective admission ( OR = 1.20; 95% CI = 1.17–1.23; p < 0.05 ), treatment at a teaching hospital ( OR = 1.15; 95% CI = 1.14–1.16; p < 0.05 ), and care at an urban hospital ( OR = 1.03; 95% CI = 1.01–1.05; p < 0.05 ) were identified as significant predictors. Several protective factors were identified, including female gender ( OR = 0.85; 95% CI = 0.84–0.86; p < 0.05 ) and geographic location, specifically hospitals situated in the southern and western regions (South: OR = 0.96; 95% CI = 0.95–0.98; West: OR = 0.75; 95% CI = 0.74–0.76; p < 0.05 ) (Table 5 ). 3.6 Clinical Outcomes associated with MetS-OA in TKA Patients with MetS-OA undergoing TKA exhibited a higher prevalence of two or more comorbidities compared to those without MetS-OA (38.9% vs. 23.0%; p < 0.05 ) (Table 2 ). However, this did not translate into a significant increase in mortality rates (0.1% vs. 0.0%; p = 0.08) (Table 2 ). Although both groups shared the same median LOS, patients with MetS-OA experienced extended LOS compared to their non-MetS-OA counterparts (2–3 days vs. 2–3 days; p < 0.05 ) (Table 2 ). Furthermore, total hospital charges were significantly elevated for patients with MetS-OA, with a median difference of $ 1445.5 ( $ 50,649.5 vs. $ 49,204.0; p < 0.05 ) (Table 2 ). In terms of insurance coverage, Medicare was utilized by 4.9% more MetS-OA patients than non-MetS-OA patients (60.0% vs. 55.1%). Conversely, private insurance coverage was 4.6% lower among MetS-OA patients compared to non-MetS-OA patients (32.7% vs. 37.3%; p < 0.05 ) (Table 2 and Fig. 3i&j). 3.7 Complications Associated with MetS-OA after TKA Patients with MetS-OA exhibited a significantly higher likelihood of experiencing a range of postoperative complications. These complications included acute myocardial infarction (0.4%), heart failure (3.6%), electrolyte imbalances (8.8%), severe malnutrition (1.9%), acute cerebrovascular disease (0.8%), postoperative delirium (0.8%), ARDS (0.5%), prolonged mechanical ventilation due to trauma (0.8%), pulmonary embolism (0.4%), pneumonia (0.4%), urinary tract infections (2.1%), and acute renal failure (3.9%). Moreover, surgical complications such as lower limb nerve injury (2.1%) were significantly more prevalent in this patient group ( p < 0.05 ) (Table 4 and Fig. 5m&n). Table 4 Relationship between Mets-OA and postoperative complications Complications Univariate Analysis Multivariate Logistic Regression No MetS-OA MetS-OA p OR 95% CI p Medical complications Acute myocardial infarction 3776 (0.3%) 960 (0.4%) < 0.05 1.12 1.04–1.21 < 0.05 Heart failure 18735 (1.7%) 7751 (3.6%) < 0.05 0.79 0.75–0.83 < 0.05 Electrolyte imbalance 69363 (6.2%) 18766 (8.8%) < 0.05 0.93 0.88–0.99 < 0.05 Severe malnutrition 13819 (1.2%) 3970 (1.9%) < 0.05 1.36 1.31–1.41 < 0.05 Acute cerebrovascular disease 4762 (0.4%) 1642 (0.8%) < 0.05 1.52 1.44–1.62 < 0.05 Postoperative delirium 6763 (0.6%) 1640 (0.8%) < 0.05 1.15 1.09–1.22 < 0.05 Postoperative GI hematoma 2485 (0.2%) 397 (0.2%) < 0.05 0.72 0.59–0.87 < 0.05 ARDS 3284 (0.3%) 1146 (0.5%) < 0.05 1.32 1.23–1.41 < 0.05 Mechanical ventilation 3208 (0.3%) 1681 (0.8%) < 0.05 2.09 1.96–2.22 < 0.05 Pulmonary embolism 3288 (0.3%) 849 (0.4%) < 0.05 0.97 0.89–1.06 0.55 Pneumonia 3295 (0.3%) 875 (0.4%) < 0.05 1.06 0.98–1.14 0.14 Urinary tract infection 18471 (1.7%) 4531 (2.1%) < 0.05 1.16 1.12–1.20 < 0.05 Acute renal failure 17395 (1.6%) 8360 (3.9%) < 0.05 1.69 1.65–1.74 < 0.05 Surgical complications Blood transfusion 75122 (6.7%) 13073 (6.1%) < 0.05 0.82 0.81–0.84 < 0.05 Hemorrhage/seroma/hematoma 3605 (0.3%) 610 (0.3%) < 0.05 0.82 0.75–0.90 < 0.05 Lower limb nerve injury 15516 (1.4%) 4570 (2.1%) < 0.05 1.43 1.38–1.47 < 0.05 Prosthesis related complications (included joint infection/ fracture/ dislocation) 32037 (2.9%) 1581 (0.7%) < 0.05 0.21 0.20–0.23 < 0.05 OR: Odds ratio, CI: Confidence interval Multiple regression analysis revealed that patients with MetS-OA exhibited a higher likelihood of experiencing several adverse outcomes. Specifically, these patients were at an increased risk of acute myocardial infarction ( OR = 1.12; 95% CI = 1.04–1.21), severe malnutrition ( OR = 1.36; 95% CI = 1.31–1.41), acute cerebrovascular disease ( OR = 1.52; 95% CI = 1.44–1.62), postoperative delirium ( OR = 1.15; 95% CI = 1.09–1.22), ARDS ( OR = 1.32; 95% CI = 1.23–1.41), and the need for prolonged mechanical ventilation ( OR = 2.09; 95% CI = 1.96–2.22). Besides, while the association with pneumonia was not statistically significant ( OR = 1.06; 95% CI = 0.98–1.14), patients with MetS-OA were more likely to develop urinary tract infections (OR = 1.16; 95% CI = 1.12–1.20) and acute renal failure ( OR = 1.69; 95% CI = 1.65–1.74) (Table 4 ). Furthermore, the analysis revealed a higher incidence of surgical complications among these patients, such as lower limb nerve injury ( OR = 1.43; 95% CI = 1.38–1.47)(Table 4 and Fig. 5m&n). Table 5 Risk factors associated with Mets after total knee arthroplasty Variable Multivariate Logistic Regression OR 95% CI p Age 18–44 Ref —— —— 45–64 2.57 2.42–2.72 < 0.05 65–74 2.93 2.76–3.11 < 0.05 ≥75 1.99 1.88–2.12 < 0.05 Female 0.85 0.84–0.86 < 0.05 Race White Ref —— —— Black 1.53 1.50–1.55 < 0.05 Hispanic 1.47 1.44–1.50 < 0.05 Asian or Pacific Islander 1.48 1.42–1.54 < 0.05 Native American 1.38 1.29–1.48 < 0.05 Other 0.97 0.96–0.99 < 0.05 Type of insurance Medicare Ref —— —— Medicaid 0.98 0.96–1.01 0.17 Private insurance 0.87 0.85–0.88 < 0.05 Self-pay 0.81 0.75–0.87 < 0.05 No charge 0.72 0.58–0.89 < 0.05 Other 0.86 0.83–0.88 < 0.05 Bed size of hospital Small Ref —— —— Medium 1.07 1.06–1.09 < 0.05 Large 1.06 1.05–1.07 < 0.05 Elective admission 1.20 1.17–1.23 < 0.05 Teaching hospital 1.15 1.14–1.16 < 0.05 Urban hospital 1.03 1.01–1.05 < 0.05 Region of hospital Northeast Ref —— —— Midwest or North Central 1.20 1.19–1.22 < 0.05 South 0.96 0.95–0.98 < 0.05 West 0.75 0.74–0.76 < 0.05 AIDS: Acquired immunodeficiency syndrome, OR: Odds ratio, CI: Confidence interval 4. Discussion With the aging of the global population, the incidence of osteoarthritis is on the rise. It has been reported that over 25% of the world’s population has developed metabolic syndrome in recent decades, a figure that continues to increase[ 2 , 14 , 15 ]. This trend has resulted in the emergence of a new osteoarthritis subtype, MetS-OA. Unlike other forms of the condition, MetS-OA is emerging as a significant public health concern, progressively intensifying and presenting challenges to both clinical and public health globally. This trend is closely linked to factors such as rapid urbanization, increased calorie intake, rising obesity rates, and increasingly sedentary lifestyles[ 16 ]. The significant association between obesity, related comorbidities, and the increased risk of perioperative complications in patients undergoing TKA and total hip arthroplasty (THA) prompted the American Association of Hip and Knee Surgeons (AAHKS) to release a recommendation in 2013, suggesting that arthroplasty procedures should be delayed for patients classified as morbidly obese[ 17 ]. TKA, a surgical procedure known to provide substantial relief from joint pain, is performed in more than one million cases each year in the United States. Projections estimate that this number will rise to 3.48 million by 2030[ 18 , 19 ]. However, the effect of MetS-OA on clinical outcomes following TKA, when compared to other subtypes of OA, has not been extensively studied. By analyzing a decade's worth of data from the NIS database, we identified several factors that could serve as targets for intervention. In terms of demographic characteristics, the mean age of TKA patients with MetS-OA was approximately one year higher than that of patients without MetS-OA (67 years vs. 66 years). This finding contrasts with a previous study, which reported that the growth rate peaks around the ages of 40–50 years, after which it plateaus or even decreases among the elderly. This discrepancy may be explained by age-related physiological changes, including a slower metabolic rate, shifts in sex hormone levels, cognitive decline, heightened oxidative stress, and lipid metabolism dysregulation [ 20 ]. Furthermore, a greater proportion of men with MetS-OA underwent TKA compared to their counterparts (39.4% vs. 37.8%). A large prospective cohort study, The Rotterdam Study, found that MetS-OA was linked to an increased risk of developing chronic knee pain (CKP) in men. Specifically, abdominal obesity and elevated triglyceride levels were associated with a higher CKP risk in men, though this was not observed in women[ 21 ]. In contrast, research on a Japanese population revealed that women had a higher prevalence of central obesity and lower HDL-C levels, while men were more likely to experience hypertension and elevated fasting glucose levels [ 22 ]. When these findings are considered together, it suggests that men may be more susceptible to MetS-OA. However, further large-scale, targeted studies are needed to confirm this observation, particularly given the ethnic diversity of the studied populations and the lack of clarity regarding the underlying mechanisms. Our analysis identified significant differences in healthcare payment methods between the two groups. Specifically, TKA patients with MetS-OA were more frequently covered by Medicare and Medicaid, whereas patients without MetS-OA predominantly relied on private insurance or self-payment options (Table 2 and Fig. 3i&j). Furthermore, logistic regression analysis revealed that TKA patients were less likely to have MetS-OA when choosing a payment option other than Medicare (Table 5 ). Patients with MetS-OA presented a higher likelihood of being hospitalized and were more often treated at teaching hospitals. This trend may be attributed to the advanced medical technologies and specialized care available at these institutions, which allow MetS-OA TKA patients to receive more expert care during both the perioperative and postoperative phases (Table 2 )[ 23 ]. Furthermore, patients with MetS-OA were more frequently treated at larger hospitals than smaller ones, possibly due to the greater complexity and higher volume of procedures typically performed at larger institutions (Table 2 ). As expected, the presence of MetS-OA was associated with an increase in the average length of stay by 0.5 days and a rise in total costs by $ 1,445.50 (Table 2 ). This may be attributed to the higher prevalence of perioperative comorbidities among MetS-OA patients. Specifically, patients with two comorbidities were 1.24 times more likely to experience complications, while those with three or more comorbidities faced a 1.45 times greater likelihood compared to non-MetS-OA patients (Table 5 ). These findings are consistent with prior research[ 24 ]. The additional comorbidities likely play a role in the increased likelihood of hospital admission and the preference for treatment at more experienced hospitals, ultimately leading to longer hospitalization and higher costs[ 25 ]. However, the presence of MetS-OA did not result in an increase in mortality rates. A total of 23 comorbidities were analyzed during logistic regression, including conditions such as drug abuse, hypothyroidism, and congestive heart failure (Table 3 ). The analysis revealed several comorbidities as significant risk factors, including deficiency anemia, congestive heart failure, chronic pulmonary disease, depression, hypothyroidism, liver disease, fluid and electrolyte imbalances, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, peptic ulcer disease (excluding bleeding), and valvular disease (Table 3 and Fig. 4). Furthermore, specific conditions such as drug abuse, congestive heart failure, depression, paralysis, psychoses, pulmonary circulation disorders, peptic ulcer disease (excluding bleeding), and valvular disease were also recognized as risk factors. These findings aligned with prior research [ 26 , 27 ], although the underlying mechanisms remain incompletely understood. Nonetheless, these comorbidities can provide a valuable basis for health assessments prior to patient admission. In conclusion, key risk factors related to MetS-OA that influence the TKA procedure include advanced age, male gender, and non-white ethnicity. MetS-OA is associated with a higher number of preoperative comorbidities, increased medical resource utilization, higher hospital charges, and extended hospital stays, although it does not impact mortality rates. Our study results indicated that TKA patients with MetS-OA experienced a higher incidence of postoperative complications, including acute myocardial infarction, severe malnutrition, acute cerebrovascular events, postoperative delirium, ARDS, prolonged mechanical ventilation, pneumonia, urinary tract infections, and acute renal failure. Moreover, the incidence of surgical complications, such as lower limb nerve injury, was significantly higher in MetS-OA patients compared to those without MetS-OA (Table 4 ). MetS-OA is a complex and multifaceted condition involving various biochemical and physiological pathways, often linked to the development of cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), and an increased risk of mortality. Several factors contribute to the onset of MetS-OA, including insulin resistance, visceral fat accumulation, atherogenic dyslipidemia, endothelial dysfunction, genetic predisposition, hypertension, a hypercoagulable state, and chronic stress [ 28 , 29 ]. In patients that undergo TKA, MetS-OA is often accompanied by a higher prevalence of fluid and electrolyte imbalances, as well as peripheral vascular disorders. These complications can manifest as severe and potentially life-threatening conditions, such as diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS), which are among the most critical hyperglycemic emergencies in diabetes[ 30 ]. Peripheral vascular disorders, in particular, are commonly linked to both diabetes and hypertension, further complicating the clinical picture [ 31 ]. Besides, hypothyroidism has been identified as a significant risk factor in TKA patients with MetS-OA, with evidence suggesting a potential bidirectional relationship between the two conditions[ 32 , 33 ]. Numerous studies suggest that patients with MetS-OA experience a chronic inflammatory state. The interplay between MetS and OA involves complex mechanisms, including the exacerbation of systemic inflammation and the release of adipokines such as adiponectin[ 34 ], leptin[ 35 ], IL-6[ 36 ], lipocalin-2[ 37 ], and other related factors that accelerate chondrocyte aging. These interactions not only modulate systemic and local autoimmune and inflammatory processes but also drive macrophage polarization and increase apoptosis of articular chondrocytes[ 38 , 39 ]. Emerging research further highlights that persistent, low-grade systemic inflammation is a crucial factor in the pathogenesis of the disease[ 40 ]. Consequently, MetS-OA is associated with a higher incidence of complications across multiple organ systems, including the heart, kidneys, and lungs, ultimately leading to poorer clinical outcomes. Surgeons should remain mindful of the increased risk of complications in MetS-OA patients. Preoperative protocols should be carefully tailored to address these specific risks[ 24 ]. The guidelines provided by the AAHKS appear to align with the broader recognition of these challenges and the advancements in managing such patients effectively. This study has inherent limitations stemming from its retrospective design and reliance on extensive administrative datasets. Notably, most hospitals within the NIS database documented patient data solely until discharge. Consequently, post-discharge complications were not systematically captured, potentially leading to an underestimation of the true incidence of MetS-OA complications. Furthermore, the absence of specific personal information, such as body mass index (BMI), hindered a comprehensive analysis of critical risk factors, including the duration of surgical procedures and the depth of sedation employed during post-anesthesia recovery. It is also crucial to acknowledge the potential influence of coding and reporting biases on data accuracy, which may have inadvertently resulted in an underrepresentation of patients with MetS-OA. Despite these limitations, this study offers valuable insights into the identification of risk factors among TKA patients with MetS-OA. Moving forward, prospective studies are necessary to better characterize the risks and trends associated with various components and phenotypes of MetS-OA. Moreover, future research should meticulously examine the interactions between these components and other preoperative risk factors that may contribute to complications following TKA. 5. Conclusion Our investigation observed an increasing prevalence of patients with MetS-OA undergoing TKA between 2010 and 2019, with an overall incidence of 16.1%. Key risk factors identified include advanced age (≥65 years), male gender, treatment at teaching and urban hospitals, and the presence of comorbidities such as chronic pulmonary disease and hypothyroidism. Patients with MetS-OA were associated with increased complications, longer hospital stays, and higher costs. To mitigate these adverse outcomes, we propose the implementation of preoperative optimization strategies and standardized intervention protocols specifically tailored to address the unique challenges presented by MetS-OA in the TKA population. These measures have the potential to significantly reduce the incidence of complications and improve overall patient outcomes following total knee arthroplasty. Declarations Ethics approval and consent to participate Per institutional policies at Xiaolan People's Hospital of Zhongshan and U.S. regulation 45 CFR 46.104(d)(4) (https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html), retrospective studies using de-identified NIS data do not require ethics approval. HCUP, the data source, obtained patient consent during primary data collection and provides data under HIPAA-compliant protocols. Funding This study received no specific grant from funding agencies in the public, commercial, or non-profit sectors. Clinical trial registration Clinical trial number: not applicable. This study is a retrospective analysis of existing database records and does not constitute a clinical trial. Author Contributions Miaolan Yuan andHao Xie contributed equally to this work, are co-first authors of this manuscript. M.Y. designed the study, performed statistical analysis, and drafted the manuscript. H.X. curated the data, interpreted results, and revised the manuscript. Yanjie He and Yinyin Qin helped manuscript preparation and production of tables and charts. Jian Wang supervised the project and provided critical feedback. All authors approved the final version. Corresponding Author For correspondence: Miaolan Yuan , Email: [email protected] Competing interests The authors declare that they have no competing interests. Provenance and peer review Not commissioned; externally peer reviewed. Acknowledgements The authors gratefully acknowledge the [National Inpatient Sample (NIS) database] for providing the data essential to this study. We extend our sincere thanks to the statisticians and research assistants at Xiaolan People’s Hospital of Zhongshan for their invaluable support in data curation and analysis. We also thank Dr. Xiaohua Zhu, Prof. Haichao Wei, Prof. Zhen Lin and Prof. Xianming Pu for their critical review of the manuscript and insightful feedback. Finally, we acknowledge the editorial team and anonymous reviewers for their dedication to advancing research in arthroplasty. The National (Nationwide) Inpatient Sample has been deemed to be publicly available datasets does not involve “human subjects", thus not requiring lRB per 45 CFR46.101( https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html ). The data contained within these data sets are neither identifiable nor private and thus do not meet the federal definition of “human subject" as defined in 45 CFR 46.102. Therefore, these research projects do not need to be reviewed and approved by the Institutional Review Board. 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Outcomes of Total Knee Arthroplasty Revisions in Obese and Morbidly Obese Patient Populations. J Arthroplasty. 2023;38(9):1822–6. Rodriguez-Merchan EC, Delgado-Martinez AD. Risk Factors for Periprosthetic Joint Infection after Primary Total Knee Arthroplasty. J Clin Med, 2022. 11(20). Tarazi JM, et al. The Epidemiology of Revision Total Knee Arthroplasty. J Knee Surg. 2021;34(13):1396–401. Conrad T, Siewert N, Hofmann GO. [Primary total knee arthroplasty following trauma]. Unfallchirurgie (Heidelb). 2022;125(12):936–45. Putman S, et al. Ten-year survival and complications of total knee arthroplasty for osteoarthritis secondary to trauma or surgery: A French multicentre study of 263 patients. Orthop Traumatol Surg Res. 2018;104(2):161–4. Fineberg SJ, et al. Incidence and risk factors for postoperative delirium after lumbar spine surgery. Spine (Phila Pa 1976). 2013;38(20):1790–6. Beltran-Sanchez H, et al. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999–2010. J Am Coll Cardiol. 2013;62(8):697–703. Palaniappan LP, et al. Asian Americans have greater prevalence of metabolic syndrome despite lower body mass index. Int J Obes (Lond). 2011;35(3):393–400. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome–a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med. 2006;23(5):469–80. Lan RH, Kamath AF. American Association of Hip and Knee Surgeons–Endorsed Comorbidity Coding for Total Joint Arthroplasty: How Often Did We Hit the Mark With International Classification of Diseases, Ninth Revision? J Arthroplast. 2016;31(12):2692–5. Shichman I, et al. Projections and Epidemiology of Revision Hip and Knee Arthroplasty in the United States to 2040–2060. Arthroplast Today. 2023;21:101152. Schwartz AM, et al. Projections and Epidemiology of Revision Hip and Knee Arthroplasty in the United States to 2030. J Arthroplasty. 2020;35(6S):S79–85. Cai C et al. Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations. Nutrients, 2024. 16(11). Szilagyi IA, et al. Metabolic syndrome, radiographic osteoarthritis progression and chronic pain of the knee among men and women from the general population: The Rotterdam study. Semin Arthritis Rheum. 2024;69:152544. Y YHH, H.I.I. H, Y YFF. Differences in the components of metabolic syndrome by age and sex: a cross-sectional and longitudinal analysis of a cohort of middle-aged and older Japanese adults. BMC Geriatr., 2023(1): p. 438. T TKK et al. The Impact of Metabolic Syndrome and Obesity on Perioperative Total Joint Arthroplasty Outcomes: The Obesity Paradox and Risk Assessment in Total Joint Arthroplasty. Arthroplasty today., 2023: p. 101139. C CGG et al. Patients with metabolic syndrome have a greater rate of complications after arthroplasty: A systematic review and meta-analysis. Bone joint Res, 2020(3): pp. 120–9. LA LAPP et al. Perioperative morbidity and mortality of same-admission staged bilateral TKA. Clin Orthop Relat Res, 2015(1): pp. 190–7. SJP SJPS et al. Obesity, Metabolic Syndrome, and Osteoarthritis-An Updated Review. Curr Obes Rep, 2023(3): pp. 308–31. LL LLNN et al. Decreasing Trend in Complications for Patients With Obesity and Metabolic Syndrome Undergoing Total Knee Arthroplasty From 2006 to 2017. J Arthroplast, 2022(6S): pp. S159–64. Cicekli I, Saglam D, Takar N. A New Perspective on Metabolic Syndrome with Osteopontin: A Comprehensive Review. Life (Basel), 2023. 13(7). Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009;2(5–6):231–7. M MFF, FJ FJPP, E.U.U. G, GE. Management of Hyperglycemic Crises: Diabetic Ketoacidosis and Hyperglycemic Hyperosmolar State. The Medical clinics of North America., 2017(3): pp. 587–606. XQ XHH, L LZZ. Oxidative Regulation of Vascular Ca(v)1.2 Channels Triggers Vascular Dysfunction in Hypertension-Related Disorders. Antioxidants, 2022(12). L LZZ et al. Metabolic syndrome and risk of subclinical hypothyroidism: a systematic review and meta-analysis. Front Endocrinol., 2024: p. 1399236. M MGG, R RJJ. The effect of hypothyroidism occurring in patients with metabolic syndrome. Endokrynologia Polska, 2015(4): pp. 288–94. King LK, March L, Anandacoomarasamy A. Obesity & osteoarthritis. Indian J Med Res. 2013;138(2):185–93. Gomez R, et al. Adiponectin and leptin increase IL-8 production in human chondrocytes. Ann Rheum Dis. 2011;70(11):2052–4. Zhang L, et al. Yeast Cell wall Particle mediated Nanotube-RNA delivery system loaded with miR365 Antagomir for Post-traumatic Osteoarthritis Therapy via Oral Route. Theranostics. 2020;10(19):8479–93. Abella V, et al. The potential of lipocalin-2/NGAL as biomarker for inflammatory and metabolic diseases. Biomarkers. 2015;20(8):565–71. BM BMDD et al. The burden of metabolic syndrome on osteoarthritic joints. Arthritis Res therapy, 2019(1): p. 289. Bondeson J, et al. The role of synovial macrophages and macrophage-produced mediators in driving inflammatory and destructive responses in osteoarthritis. Arthritis Rheum. 2010;62(3):647–57. Robinson WH, et al. Low-grade inflammation as a key mediator of the pathogenesis of osteoarthritis. Nat Rev Rheumatol. 2016;12(10):580–92. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6591213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481691222,"identity":"56ad7f5b-bee4-459f-9777-0b52c84e082d","order_by":0,"name":"Miaolan Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACfvnDxz8k/rFhludvPkCcFskZbGkMDxvS2A1nHEsgTovBDR4zxocNh/kZDuQYEOmy221pDxJ3pEkzNpz5eOMNg52cbgMBHYxzDh83SDxjY8zO3LvZcg5DsrHZAQJamBnSEiQS2NKSGRvObpPmYTiQuI2QFjaGHAOglsP1DQdynhGnhUcix0wise0wM9D7bMRpkeA5lmyQcCaNGRjIxpZzDIjwi/3x5oMPf1SAo/LhjTcVdnIEtaBZSWzUIGkhVccoGAWjYBSMCAAAWgxHKnXfBugAAAAASUVORK5CYII=","orcid":"","institution":"Xiaolan People’s Hospital of Zhongshan","correspondingAuthor":true,"prefix":"","firstName":"Miaolan","middleName":"","lastName":"Yuan","suffix":""},{"id":481691223,"identity":"b8295dca-eb3f-4423-a186-234d29310a0e","order_by":1,"name":"Hao Xie","email":"","orcid":"","institution":"Nanfang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Xie","suffix":""},{"id":481691224,"identity":"818dbadd-633a-472a-8a62-629fad04903a","order_by":2,"name":"Yanjie He","email":"","orcid":"","institution":"Xiaolan People’s Hospital of Zhongshan","correspondingAuthor":false,"prefix":"","firstName":"Yanjie","middleName":"","lastName":"He","suffix":""},{"id":481691225,"identity":"37701a36-e04f-4127-8499-6bc657b74822","order_by":3,"name":"Yinyin Qin","email":"","orcid":"","institution":"Xiaolan People’s Hospital of Zhongshan","correspondingAuthor":false,"prefix":"","firstName":"Yinyin","middleName":"","lastName":"Qin","suffix":""},{"id":481691226,"identity":"ecda2cf4-52df-440e-80b2-cb276fb3e8f7","order_by":4,"name":"Jian Wang","email":"","orcid":"","institution":"Nanfang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-05 04:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6591213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6591213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86386919,"identity":"72a7d100-0e24-4505-92af-46f5483040c8","added_by":"auto","created_at":"2025-07-10 06:04:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of patient selection from the National Inpatient Sample (NIS) database (2010–2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial cohort included 1,361,454 patients undergoing revision total knee arthroplasty (RTKA), identified by ICD-9 (81.55, 00.80–00.84) and ICD-10 (0SWC, 0SWD, 0SWT–0SWV, 0SWW) procedure codes. Exclusion criteria were sequentially applied: missing data (n=31,355) and age \u0026lt;18 years (n=0). A final sample of 1,330,099 unique patients was included in the analysis of metabolic syndrome-associated osteoarthritis and TKA outcomes.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-6591213/v1/bdb8f75e79443817622b6229.png"},{"id":86386920,"identity":"df1b25b7-3bc0-4dea-9d60-ccade93de5aa","added_by":"auto","created_at":"2025-07-10 06:04:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24039,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trends in the prevalence of metabolic syndrome-associated osteoarthritis (MetS-OA) among total knee arthroplasty (TKA) patients (2010–2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnnual morbidity rates of MetS-OA were calculated from the National Inpatient Sample (NIS) database, with values ranging from 10% to 25% over the study period. The line graph illustrates fluctuations in prevalence, peaking 2019 and reaching the lowest value in 2010. Statistical analysis indicated a significant upward trend (p\u0026lt;0.05)].\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-6591213/v1/f2e4ee1b916cda5a7799e5c8.png"},{"id":86386924,"identity":"90d8b9f6-c033-42d7-90be-adcbaf6d42b3","added_by":"auto","created_at":"2025-07-10 06:04:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of demographics and hospital characteristics between MetS-OA and non-MetS-OA patients undergoing total knee arthroplasty\u003c/strong\u003e\u003cbr\u003e\n \u003cstrong\u003ea\u003c/strong\u003e Age distribution of MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb\u003c/strong\u003e Age distribution of non-MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec\u003c/strong\u003e Geographic distribution of hospitals treating MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed\u003c/strong\u003e Geographic distribution of hospitals treating non-MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee\u003c/strong\u003e Racial/ethnic composition of MetS-OA patients (White: 73.2%, Black: 13.5%, Hispanic: 10.2%, Asian/Pacific Islander: 6.2%, Native American: 8.4%, Other: 0.4%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef\u003c/strong\u003e Racial/ethnic composition of non-MetS-OA patients (White: 77.4%, Black: 7.0%, Hispanic: 3.1%, Asian/Pacific Islander: 1.2%, Native American: 0.4%, Other: 10.2%).\u003cbr\u003e\n\u003cstrong\u003eg\u003c/strong\u003e Hospital bed-size categories for MetS-OA patients (Small: 25.7%, Medium: 27.8%, Large: 46.5%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e Hospital bed-size categories for non-MetS-OA patients (Small: 27.2%, Medium: 26.8%, Large: 46.0%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei\u003c/strong\u003e Insurance type distribution for MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ej\u003c/strong\u003e Insurance type distribution for non-MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData derived from the National Inpatient Sample (2010–2019). Percentages may not sum to 100% due to rounding or unlisted categories.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-6591213/v1/8c805008a94db50b8875283c.png"},{"id":86387867,"identity":"8da72d0d-8c87-41e7-9ab0-82b659f9ac68","added_by":"auto","created_at":"2025-07-10 06:12:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":167055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of preoperative comorbidities between MetS-OA and non-MetS-OA patients undergoing total knee arthroplasty\u003c/strong\u003e\u003cbr\u003e\n\u003cstrong\u003ek\u003c/strong\u003e Prevalence of preoperative comorbidities in metabolic syndrome-associated osteoarthritis (MetS-OA) patients. \u0026nbsp;Key comorbidities include deficiency anemia (7.6%), congestive heart failure (5.1%), chronic pulmonary disease (19.2%), depression (17.5%), hypothyroidism (19.2%), fluid and electrolyte disorders (9.6%), renal failure (11.0%), and valvular disease (4.0%) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003el\u003c/strong\u003e Prevalence of preoperative comorbidities in non-MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData derived from the National Inpatient Sample (2010–2019). Percentages represent the proportion of patients with each comorbidity.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-6591213/v1/fad99ca3f17578f2f03c7174.png"},{"id":86386921,"identity":"72c627a7-61e7-46d9-96dc-b39a4b251859","added_by":"auto","created_at":"2025-07-10 06:04:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":63386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of postoperative complications between MetS-OA and non-MetS-OA patients undergoing total knee arthroplasty\u003c/strong\u003e\u003cbr\u003e\n\u003cstrong\u003em\u003c/strong\u003e Medical complications (e.g., deep vein thrombosis, pulmonary embolism, acute kidney injury) in MetS-OA versus non-MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e Surgical complications (e.g., prosthetic joint infection, periprosthetic fracture, implant dislocation) in MetS-OA versus non-MetS-OA patients.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData derived from the National Inpatient Sample (2010–2019). Bars represent incidence rates (%) with 95% confidence intervals. Significant differences between groups are indicated by asterisks (\u003c/em\u003ep\u0026lt;0.05\u003cem\u003e). Adjusted odds ratios (aOR) for complications were calculated using multivariate logistic regression, controlling for age, sex, and comorbidities.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Slide5.png","url":"https://assets-eu.researchsquare.com/files/rs-6591213/v1/96b1d07475ad034e5c7a59c9.png"},{"id":90642506,"identity":"90c23f98-8389-42f7-bca3-d602cacc267a","added_by":"auto","created_at":"2025-09-05 06:53:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2419974,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6591213/v1/a4d7382c-783e-479b-8b75-0a1fe10eb87d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors And Complications Analysis Of Total Knee Arthroplasty In Patients With Metabolic Syndrome Associated Osteoarthritis: A 10-year Retrospective Study Of A National Inpatient Sample Database.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOsteoarthritis (OA) is a prevalent joint disease, affecting approximately 250\u0026nbsp;million individuals globally, with a prevalence of 60% among adults aged 65 and above[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Clinically, osteoarthritis is categorized into several subtypes, including post-traumatic OA, age-related OA[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and metabolic osteoarthritis (MetS-OA), which is recognized as one of the clinical phenotypes associated with metabolic syndrome (MetS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The link between osteoarthritis and metabolic syndrome has been well-documented across various populations and cultural settings[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs the global population continues to age, the prevalence of MetS-OA is increasing at a concerning rate, which is expected to impose a significant financial burden. Fortunately, total knee arthroplasty (TKA) has been demonstrated to be a highly effective and successful intervention for reducing pain, restoring function, and enhancing quality of life in patients with advanced knee diseases[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While the effects of post-traumatic OA on TKA outcomes are well-established[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], there is limited research focusing on MetS-OA. Therefore, this study was designed to achieve the following objectives: (i) assess the overall and annual incidence of MetS-OA patients undergoing TKA over the past decade; (ii) examine the adverse outcomes in these patients post-TKA; and (iii) identify risk factors related to MetS-OA that impact the TKA procedure. Utilizing a national database, the analysis included patient demographics, hospital characteristics, length of stay (LOS), total hospitalization costs, in-hospital mortality, comorbidities, and perioperative complications.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Data Source\u003c/h2\u003e\n \u003cp\u003eThe data utilized in this study were obtained from the Nationwide Inpatient Sample (NIS), a comprehensive database maintained by the Healthcare Cost and Utilization Project (HCUP) and supported by the Agency for Healthcare Research and Quality (AHRQ). As the largest all-payer inpatient database in the United States, the NIS provides a stratified sample derived from over 1,000 hospitals, encompassing approximately 20% of nationwide hospitalizations annually[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The database includes detailed information on patient demographics, hospital characteristics, LOS, total charges, payer type, in-hospital mortality, and diagnostic and procedural codes based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). Since the study utilized publicly available anonymous data, ethics board approval was not required.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Cohort selection\u003c/h2\u003e\n \u003cp\u003eThe study included patients aged 18 years or older who underwent TKA for OA between 2010 and 2019. These patients were identified using hospital discharge data, with procedure codes from both ICD-9 and ICD-10 systems (refer to the Supplemental data file) for details. Initially, a total of 1,361,454 patients were identified. However, after excluding individuals with incomplete data related to hospital characteristics and patient demographics\u0026mdash;such as age, mortality, elective admission status, gender, LOS, insurance type, race, total charges, and hospital bed size\u0026mdash;the final analysis comprised 1,330,399 patients (Fig.\u0026nbsp;1).\u003c/p\u003e\n \u003cp\u003eThe study population was categorized into two groups based on the diagnosis of METS-OA. We analyzed patient demographics, hospital characteristics, and outcome measures such as LOS, economic indicators, and in-hospital mortality. Preoperative comorbidities and perioperative complications were identified using ICD-9-CM and ICD-10-CM codes, as outlined in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Perioperative complications included acute myocardial infarction, electrolyte imbalances, severe malnutrition, cerebrovascular events, pulmonary embolism, gastrointestinal bleeding, heart failure, renal insufficiency, pneumonia, ARDS, prolonged mechanical ventilation, urinary tract infections, acute renal failure, postoperative delirium, blood transfusions, and hemorrhagic complications such as seromas and hematomas.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariables used in binary logistic regression analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecific Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient demographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (\u0026lt;\u0026thinsp;65 years and \u0026ge;65 years), sex (male and female), race (White, Black, Hispanic, Asian or Pacific Islander, Native American and Other)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eType of admission (non-elective, elective), bed size of hospital (small, medium, large), teaching status of hospital (nonteaching, teaching), location of hospital (rural, urban), type of insurance (Medicare, Medicaid, private insurance, self-pay, no charge, other), location of the hospital (northeast, Midwest or north central, south, west)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAIDS, alcohol abuse, deficiency anemia, rheumatoid diseases, chronic blood loss anemia, congestive heart failure, chronic pulmonary disease, coagulopathy, depression, drug abuse, hypothyroidism, liver disease, fluid and electrolyte disorders, other neurological disorders, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, peptic ulcer disease and valvular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eAIDS: Acquired immunodeficiency syndrome\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were conducted using SPSS version 25.0. Continuous variables were evaluated using independent t-tests, while categorical variables were analyzed with chi-square tests (Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). To identify potential risk factors associated with blood transfusion, logistic regression was employed. The regression model incorporated all relevant variables available in the NIS database, including patient demographics, hospital characteristics, and comorbidities (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Odds ratios (OR), along with their corresponding 95% confidence intervals (CI), were calculated. Given the large sample size, statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePatient characteristics and outcomes after total knee arthroplasty (2010\u0026ndash;2019) Continue\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetabolic syndrome associated osteoarthritis (MetS-OA)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo Metabolic syndrome associated osteoarthritis\u003c/p\u003e\n \u003cp\u003e(no MetS-OA)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetS-OA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo MetS-OA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n\u0026thinsp;=\u0026thinsp;count)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214,448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,115,651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal incidence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;MD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.74\u0026thinsp;\u0026plusmn;\u0026thinsp;8.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCCI (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLOS (median, d)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0(2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0 (2\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOTCHG (median, $)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50649.5\u003c/p\u003e\n \u003cp\u003e(37277.0-71285.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49204.0\u003c/p\u003e\n \u003cp\u003e(36002.0-69695.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of insurance (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo charge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBed size of hospital (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eElective admission (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of hospital (teaching %)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLocation of hospital (urban, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRegion of hospital (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMidwest or North Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDied (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eLOS: Length of stay, TOTCHE: Total charge\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship between Mets-OA and preoperative comorbidities\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate Logistic Regression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo MetS-OA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetS-OA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePreoperative comorbidities\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcquired immune deficiency syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e763 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u0026ndash;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlcohol abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10017 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1665 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u0026ndash;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeficiency anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75358 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16757 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026ndash;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRheumatoid arthritis/collagen vascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46199 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7394 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026ndash;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic blood loss anemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10516 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2153 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u0026ndash;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24695 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11035 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71\u0026ndash;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161892 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41164 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27\u0026ndash;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoagulopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20140 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4666 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u0026ndash;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148103 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37550 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u0026ndash;1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug abuse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6506 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1244 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u0026ndash;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypothyroidism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180969 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41073 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u0026ndash;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13631 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4239 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u0026ndash;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2369 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e408 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid and electrolyte disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75509 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205650 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u0026ndash;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther neurological disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29780 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5865 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026ndash;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParalysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2083 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e465 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeripheral vascular disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20531 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6905 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u0026ndash;1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychoses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21937 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5416 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u0026ndash;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulmonary circulation disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9006 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2927 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u0026ndash;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46811 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23612 (11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42\u0026ndash;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolid tumor without metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5144 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1056 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u0026ndash;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeptic ulcer disease\u003c/p\u003e\n \u003cp\u003eExcluding bleeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1542 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026ndash;1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValvular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35499 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8624 (4.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u0026ndash;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eOR: Odds ratio, CI: Confidence interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Incidence of MetS-OA in patients undergoing TKA\u003c/h2\u003e\n \u003cp\u003eAccording to data from the NIS database, approximately 1,330,399 patients in the United States underwent TKA between 2010 and 2019. Among these patients, MetS-OA was identified in 214,448 cases, reflecting an estimated incidence rate of 16.1% (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Over the past decade, the prevalence of MetS-OA among patients undergoing TKA demonstrated a gradual upward trend (Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Patient demographics between the two groups\u003c/h2\u003e\n \u003cp\u003eA comparison of patient demographics between the two groups revealed that the mean age of individuals with MetS-OA was one year greater than those without MetS-OA (67 years vs. 66 years) (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Besides, the MetS-OA group comprised a slightly higher proportion of male patients (39.4% \u003cem\u003evs.\u003c/em\u003e 37.8%) (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, an analysis of age distribution demonstrated a notable disparity, as the prevalence of MetS-OA was 6.4% higher among patients aged 65 to 74 years who underwent TKA (42.7% \u003cem\u003evs.\u003c/em\u003e 36.3%; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. 3a\u0026amp;b). In terms of racial composition, the prevalence of MetS-OA was lower among White patients compared to those without MetS-OA (73.2% \u003cem\u003evs.\u003c/em\u003e 77.4%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003e0.05\u003c/em\u003e)(Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. 3e\u0026amp;f).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Hospital characteristics between the two groups\u003c/h2\u003e\n \u003cp\u003eAs anticipated, patients without MetS-OA were 0.5% less likely to choose admission compared to those with MetS-OA (95.5% \u003cem\u003evs.\u003c/em\u003e 96.0%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003e0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, patients with MetS-OA were more frequently treated in hospitals with larger bed capacities (46.5% \u003cem\u003evs.\u003c/em\u003e 46.0%; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. 3g\u0026amp;h). Besides, the prevalence of MetS-OA was higher among patients in urban hospitals (89.7% \u003cem\u003evs.\u003c/em\u003e 88.7%) and teaching hospitals (57.7% \u003cem\u003evs.\u003c/em\u003e 52.8%) (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, regional disparities were observed, with hospitals in the northeast (18.0% \u003cem\u003evs.\u003c/em\u003e 17.8%) and midwest/north central regions (31.1% \u003cem\u003evs.\u003c/em\u003e 26.2%) reporting a higher proportion of MetS-OA patients (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. 3c\u0026amp;d).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Comorbidities Associated with MetS-OA during TKA\u003c/h2\u003e\n \u003cp\u003eMetS-OA was significantly associated with several preoperative comorbidities during hospitalization. Conditions such as deficiency anemia (7.6%), congestive heart failure (5.1%), chronic pulmonary disease (19.2%), depression (17.5%), hypothyroidism (19.2%), fluid and electrolyte disorders (9.6%), renal failure (11.0%), and valvular disease (4.0%) were observed to have a higher likelihood of being complicated by MetS-OA (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig. 4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Risk factors associated with MetS-OA during TKA\u003c/h2\u003e\n \u003cp\u003eLogistic regression analysis identified several key risk factors associated with the co-occurrence of MetS-OA following TKA (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Advanced age emerged as a notable predictor, with OR increasing progressively across age groups: 45\u0026ndash;64 years (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.57; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.42\u0026ndash;2.72), 65\u0026ndash;74 years (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.93; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.76\u0026ndash;3.11), and \u0026ge;75 years (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.99; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.88\u0026ndash;2.12) (all \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). Moreover, elective admission (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.20; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17\u0026ndash;1.23; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), treatment at a teaching hospital (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.15; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.14\u0026ndash;1.16; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e), and care at an urban hospital (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.03; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;1.05; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) were identified as significant predictors.\u003c/p\u003e \u003cp\u003eSeveral protective factors were identified, including female gender (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.84\u0026ndash;0.86; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) and geographic location, specifically hospitals situated in the southern and western regions (South: \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95\u0026ndash;0.98; West: \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74\u0026ndash;0.76; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Clinical Outcomes associated with MetS-OA in TKA\u003c/h2\u003e\n \u003cp\u003ePatients with MetS-OA undergoing TKA exhibited a higher prevalence of two or more comorbidities compared to those without MetS-OA (38.9% \u003cem\u003evs.\u003c/em\u003e 23.0%; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). However, this did not translate into a significant increase in mortality rates (0.1% \u003cem\u003evs.\u003c/em\u003e 0.0%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Although both groups shared the same median LOS, patients with MetS-OA experienced extended LOS compared to their non-MetS-OA counterparts (2\u0026ndash;3 days \u003cem\u003evs.\u003c/em\u003e 2\u0026ndash;3 days; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, total hospital charges were significantly elevated for patients with MetS-OA, with a median difference of \u003cspan\u003e$\u003c/span\u003e1445.5 (\u003cspan\u003e$\u003c/span\u003e50,649.5 \u003cem\u003evs.\u003c/em\u003e \u003cspan\u003e$\u003c/span\u003e49,204.0; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In terms of insurance coverage, Medicare was utilized by 4.9% more MetS-OA patients than non-MetS-OA patients (60.0% \u003cem\u003evs.\u003c/em\u003e 55.1%). Conversely, private insurance coverage was 4.6% lower among MetS-OA patients compared to non-MetS-OA patients (32.7% \u003cem\u003evs.\u003c/em\u003e 37.3%; \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. 3i\u0026amp;j).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Complications Associated with MetS-OA after TKA\u003c/h2\u003e\n \u003cp\u003ePatients with MetS-OA exhibited a significantly higher likelihood of experiencing a range of postoperative complications. These complications included acute myocardial infarction (0.4%), heart failure (3.6%), electrolyte imbalances (8.8%), severe malnutrition (1.9%), acute cerebrovascular disease (0.8%), postoperative delirium (0.8%), ARDS (0.5%), prolonged mechanical ventilation due to trauma (0.8%), pulmonary embolism (0.4%), pneumonia (0.4%), urinary tract infections (2.1%), and acute renal failure (3.9%). Moreover, surgical complications such as lower limb nerve injury (2.1%) were significantly more prevalent in this patient group (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. 5m\u0026amp;n).\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship between Mets-OA and postoperative complications\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eComplications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate Logistic Regression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo MetS-OA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetS-OA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMedical complications\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3776 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e960 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u0026ndash;1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18735 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7751 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026ndash;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrolyte imbalance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69363 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18766 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u0026ndash;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevere malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13819 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3970 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u0026ndash;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute cerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4762 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1642 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u0026ndash;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostoperative delirium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6763 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1640 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u0026ndash;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePostoperative GI hematoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2485 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u0026ndash;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eARDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3284 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1146 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u0026ndash;1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMechanical ventilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3208 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1681 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96\u0026ndash;2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulmonary embolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3288 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e849 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u0026ndash;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3295 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e875 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u0026ndash;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrinary tract infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18471 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4531 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u0026ndash;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcute renal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17395 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8360 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u0026ndash;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical complications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75122 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13073 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u0026ndash;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemorrhage/seroma/hematoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3605 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e610 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026ndash;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower limb nerve injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15516 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4570 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u0026ndash;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProsthesis related complications (included joint infection/ fracture/ dislocation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32037 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1581 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u0026ndash;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eOR: Odds ratio, CI: Confidence interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eMultiple regression analysis revealed that patients with MetS-OA exhibited a higher likelihood of experiencing several adverse outcomes. Specifically, these patients were at an increased risk of acute myocardial infarction (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.12; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;1.21), severe malnutrition (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.36; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.31\u0026ndash;1.41), acute cerebrovascular disease (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.52; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.44\u0026ndash;1.62), postoperative delirium (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.15; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;1.22), ARDS (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.32; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.23\u0026ndash;1.41), and the need for prolonged mechanical ventilation (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.09; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.96\u0026ndash;2.22). Besides, while the association with pneumonia was not statistically significant (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.06; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98\u0026ndash;1.14), patients with MetS-OA were more likely to develop urinary tract infections (OR\u0026thinsp;=\u0026thinsp;1.16; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.12\u0026ndash;1.20) and acute renal failure (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.69; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.65\u0026ndash;1.74) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, the analysis revealed a higher incidence of surgical complications among these patients, such as lower limb nerve injury (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.43; \u003cem\u003e95% CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.38\u0026ndash;1.47)(Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig. 5m\u0026amp;n).\u003c/p\u003e\n \u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eRisk factors associated with Mets after total knee arthroplasty\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate Logistic Regression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u0026ndash;44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u0026ndash;64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42\u0026ndash;2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e65\u0026ndash;74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.76\u0026ndash;3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88\u0026ndash;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u0026ndash;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u0026ndash;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u0026ndash;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian or Pacific Islander\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u0026ndash;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNative American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u0026ndash;1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u0026ndash;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicaid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u0026ndash;1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u0026ndash;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-pay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026ndash;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo charge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u0026ndash;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u0026ndash;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBed size of hospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u0026ndash;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u0026ndash;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eElective admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u0026ndash;1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTeaching hospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u0026ndash;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban hospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026ndash;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion of hospital\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026mdash;\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMidwest or North Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u0026ndash;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u0026ndash;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u0026ndash;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAIDS: Acquired immunodeficiency syndrome, OR: Odds ratio, CI: Confidence interval\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith the aging of the global population, the incidence of osteoarthritis is on the rise. It has been reported that over 25% of the world\u0026rsquo;s population has developed metabolic syndrome in recent decades, a figure that continues to increase[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This trend has resulted in the emergence of a new osteoarthritis subtype, MetS-OA. Unlike other forms of the condition, MetS-OA is emerging as a significant public health concern, progressively intensifying and presenting challenges to both clinical and public health globally. This trend is closely linked to factors such as rapid urbanization, increased calorie intake, rising obesity rates, and increasingly sedentary lifestyles[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe significant association between obesity, related comorbidities, and the increased risk of perioperative complications in patients undergoing TKA and total hip arthroplasty (THA) prompted the American Association of Hip and Knee Surgeons (AAHKS) to release a recommendation in 2013, suggesting that arthroplasty procedures should be delayed for patients classified as morbidly obese[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTKA, a surgical procedure known to provide substantial relief from joint pain, is performed in more than one million cases each year in the United States. Projections estimate that this number will rise to 3.48\u0026nbsp;million by 2030[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the effect of MetS-OA on clinical outcomes following TKA, when compared to other subtypes of OA, has not been extensively studied. By analyzing a decade's worth of data from the NIS database, we identified several factors that could serve as targets for intervention.\u003c/p\u003e\u003cp\u003eIn terms of demographic characteristics, the mean age of TKA patients with MetS-OA was approximately one year higher than that of patients without MetS-OA (67 years vs. 66 years). This finding contrasts with a previous study, which reported that the growth rate peaks around the ages of 40\u0026ndash;50 years, after which it plateaus or even decreases among the elderly. This discrepancy may be explained by age-related physiological changes, including a slower metabolic rate, shifts in sex hormone levels, cognitive decline, heightened oxidative stress, and lipid metabolism dysregulation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFurthermore, a greater proportion of men with MetS-OA underwent TKA compared to their counterparts (39.4% vs. 37.8%). A large prospective cohort study, The Rotterdam Study, found that MetS-OA was linked to an increased risk of developing chronic knee pain (CKP) in men. Specifically, abdominal obesity and elevated triglyceride levels were associated with a higher CKP risk in men, though this was not observed in women[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In contrast, research on a Japanese population revealed that women had a higher prevalence of central obesity and lower HDL-C levels, while men were more likely to experience hypertension and elevated fasting glucose levels [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. When these findings are considered together, it suggests that men may be more susceptible to MetS-OA. However, further large-scale, targeted studies are needed to confirm this observation, particularly given the ethnic diversity of the studied populations and the lack of clarity regarding the underlying mechanisms.\u003c/p\u003e\u003cp\u003eOur analysis identified significant differences in healthcare payment methods between the two groups. Specifically, TKA patients with MetS-OA were more frequently covered by Medicare and Medicaid, whereas patients without MetS-OA predominantly relied on private insurance or self-payment options (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;3i\u0026amp;j). Furthermore, logistic regression analysis revealed that TKA patients were less likely to have MetS-OA when choosing a payment option other than Medicare (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePatients with MetS-OA presented a higher likelihood of being hospitalized and were more often treated at teaching hospitals. This trend may be attributed to the advanced medical technologies and specialized care available at these institutions, which allow MetS-OA TKA patients to receive more expert care during both the perioperative and postoperative phases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, patients with MetS-OA were more frequently treated at larger hospitals than smaller ones, possibly due to the greater complexity and higher volume of procedures typically performed at larger institutions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As expected, the presence of MetS-OA was associated with an increase in the average length of stay by 0.5 days and a rise in total costs by \u003cspan\u003e$\u003c/span\u003e1,445.50 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This may be attributed to the higher prevalence of perioperative comorbidities among MetS-OA patients. Specifically, patients with two comorbidities were 1.24 times more likely to experience complications, while those with three or more comorbidities faced a 1.45 times greater likelihood compared to non-MetS-OA patients (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These findings are consistent with prior research[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The additional comorbidities likely play a role in the increased likelihood of hospital admission and the preference for treatment at more experienced hospitals, ultimately leading to longer hospitalization and higher costs[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the presence of MetS-OA did not result in an increase in mortality rates.\u003c/p\u003e\u003cp\u003eA total of 23 comorbidities were analyzed during logistic regression, including conditions such as drug abuse, hypothyroidism, and congestive heart failure (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis revealed several comorbidities as significant risk factors, including deficiency anemia, congestive heart failure, chronic pulmonary disease, depression, hypothyroidism, liver disease, fluid and electrolyte imbalances, paralysis, peripheral vascular disorders, psychoses, pulmonary circulation disorders, renal failure, peptic ulcer disease (excluding bleeding), and valvular disease (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;4). Furthermore, specific conditions such as drug abuse, congestive heart failure, depression, paralysis, psychoses, pulmonary circulation disorders, peptic ulcer disease (excluding bleeding), and valvular disease were also recognized as risk factors. These findings aligned with prior research [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], although the underlying mechanisms remain incompletely understood. Nonetheless, these comorbidities can provide a valuable basis for health assessments prior to patient admission.\u003c/p\u003e\u003cp\u003eIn conclusion, key risk factors related to MetS-OA that influence the TKA procedure include advanced age, male gender, and non-white ethnicity. MetS-OA is associated with a higher number of preoperative comorbidities, increased medical resource utilization, higher hospital charges, and extended hospital stays, although it does not impact mortality rates.\u003c/p\u003e\u003cp\u003eOur study results indicated that TKA patients with MetS-OA experienced a higher incidence of postoperative complications, including acute myocardial infarction, severe malnutrition, acute cerebrovascular events, postoperative delirium, ARDS, prolonged mechanical ventilation, pneumonia, urinary tract infections, and acute renal failure. Moreover, the incidence of surgical complications, such as lower limb nerve injury, was significantly higher in MetS-OA patients compared to those without MetS-OA (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMetS-OA is a complex and multifaceted condition involving various biochemical and physiological pathways, often linked to the development of cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), and an increased risk of mortality. Several factors contribute to the onset of MetS-OA, including insulin resistance, visceral fat accumulation, atherogenic dyslipidemia, endothelial dysfunction, genetic predisposition, hypertension, a hypercoagulable state, and chronic stress [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In patients that undergo TKA, MetS-OA is often accompanied by a higher prevalence of fluid and electrolyte imbalances, as well as peripheral vascular disorders. These complications can manifest as severe and potentially life-threatening conditions, such as diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS), which are among the most critical hyperglycemic emergencies in diabetes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Peripheral vascular disorders, in particular, are commonly linked to both diabetes and hypertension, further complicating the clinical picture [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Besides, hypothyroidism has been identified as a significant risk factor in TKA patients with MetS-OA, with evidence suggesting a potential bidirectional relationship between the two conditions[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNumerous studies suggest that patients with MetS-OA experience a chronic inflammatory state. The interplay between MetS and OA involves complex mechanisms, including the exacerbation of systemic inflammation and the release of adipokines such as adiponectin[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], leptin[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], IL-6[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], lipocalin-2[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and other related factors that accelerate chondrocyte aging. These interactions not only modulate systemic and local autoimmune and inflammatory processes but also drive macrophage polarization and increase apoptosis of articular chondrocytes[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Emerging research further highlights that persistent, low-grade systemic inflammation is a crucial factor in the pathogenesis of the disease[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Consequently, MetS-OA is associated with a higher incidence of complications across multiple organ systems, including the heart, kidneys, and lungs, ultimately leading to poorer clinical outcomes.\u003c/p\u003e\u003cp\u003eSurgeons should remain mindful of the increased risk of complications in MetS-OA patients. Preoperative protocols should be carefully tailored to address these specific risks[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The guidelines provided by the AAHKS appear to align with the broader recognition of these challenges and the advancements in managing such patients effectively.\u003c/p\u003e\u003cp\u003eThis study has inherent limitations stemming from its retrospective design and reliance on extensive administrative datasets. Notably, most hospitals within the NIS database documented patient data solely until discharge. Consequently, post-discharge complications were not systematically captured, potentially leading to an underestimation of the true incidence of MetS-OA complications. Furthermore, the absence of specific personal information, such as body mass index (BMI), hindered a comprehensive analysis of critical risk factors, including the duration of surgical procedures and the depth of sedation employed during post-anesthesia recovery. It is also crucial to acknowledge the potential influence of coding and reporting biases on data accuracy, which may have inadvertently resulted in an underrepresentation of patients with MetS-OA. Despite these limitations, this study offers valuable insights into the identification of risk factors among TKA patients with MetS-OA. Moving forward, prospective studies are necessary to better characterize the risks and trends associated with various components and phenotypes of MetS-OA. Moreover, future research should meticulously examine the interactions between these components and other preoperative risk factors that may contribute to complications following TKA.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur investigation observed an increasing prevalence of patients with MetS-OA undergoing TKA between 2010 and 2019, with an overall incidence of 16.1%. Key risk factors identified include advanced age (\u0026ge;65 years), male gender, treatment at teaching and urban hospitals, and the presence of comorbidities such as chronic pulmonary disease and hypothyroidism. Patients with MetS-OA were associated with increased complications, longer hospital stays, and higher costs. To mitigate these adverse outcomes, we propose the implementation of preoperative optimization strategies and standardized intervention protocols specifically tailored to address the unique challenges presented by MetS-OA in the TKA population. These measures have the potential to significantly reduce the incidence of complications and improve overall patient outcomes following total knee arthroplasty.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePer institutional policies at Xiaolan People\u0026apos;s Hospital of Zhongshan and U.S. regulation \u003cem\u003e45 CFR 46.104(d)(4)\u003c/em\u003e(https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html), retrospective studies using de-identified NIS data do not require ethics approval. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHCUP, the data source, obtained patient consent during primary data collection and provides data under HIPAA-compliant protocols. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no specific grant from funding agencies in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable. This study is a retrospective analysis of existing database records and does not constitute a clinical trial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMiaolan Yuan andHao Xie contributed equally to this work, are co-first authors of this manuscript. M.Y. designed the study, performed statistical analysis, and drafted the manuscript. H.X. curated the data, interpreted results, and revised the manuscript. Yanjie He and Yinyin Qin helped manuscript preparation and production of tables and charts. Jian Wang supervised the project and provided critical feedback. All authors approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFor correspondence:\u0026nbsp;\u003c/em\u003e\u003cem\u003eMiaolan Yuan\u003c/em\u003e\u003cem\u003e, Email:\u003c/em\u003e\u003cem\[email protected]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProvenance and peer review\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot commissioned; externally peer reviewed.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors gratefully acknowledge the [National Inpatient Sample (NIS) database] for providing the data essential to this study. We extend our sincere thanks to the statisticians and research assistants at Xiaolan People\u0026rsquo;s Hospital of Zhongshan for their invaluable support in data curation and analysis.\u003c/p\u003e\n\u003cp\u003eWe also thank Dr. Xiaohua Zhu, Prof. Haichao Wei, Prof. Zhen Lin and Prof. Xianming Pu for their critical review of the manuscript and insightful feedback. Finally, we acknowledge the editorial team and anonymous reviewers for their dedication to advancing research in arthroplasty.\u003c/p\u003e\n\u003cp\u003eThe National (Nationwide) Inpatient Sample has been deemed to be publicly available datasets does not involve \u0026ldquo;human subjects\", thus not requiring lRB per 45 CFR46.101( https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html ). The data contained within these data sets are neither identifiable nor private and thus do not meet the federal definition of \u0026ldquo;human subject\" as defined in 45 CFR 46.102. Therefore, these research projects do not need to be reviewed and approved by the Institutional Review Board.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMuyskens JB et al. Essential amino acid supplementation alters the p53 transcriptional response and cytokine gene expression following total knee arthroplasty. J Appl Physiol (1985), 2020. 129(4): pp. 980\u0026ndash;991.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHunter DJ, Bierma-Zeinstra S. Osteoarthr Lancet. 2019;393(10182):1745\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eR RCC. Revealed aspect of metabolic osteoarthritis. J Orthop, 2016(4): pp. 347\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJansen NEJ, et al. Metabolic syndrome and the progression of knee osteoarthritis on MRI. Osteoarthr Cartil. 2023;31(5):647\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eA ACC, J.S.S. 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J Clin Med, 2022. 11(20).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTarazi JM, et al. The Epidemiology of Revision Total Knee Arthroplasty. J Knee Surg. 2021;34(13):1396\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConrad T, Siewert N, Hofmann GO. [Primary total knee arthroplasty following trauma]. Unfallchirurgie (Heidelb). 2022;125(12):936\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePutman S, et al. Ten-year survival and complications of total knee arthroplasty for osteoarthritis secondary to trauma or surgery: A French multicentre study of 263 patients. Orthop Traumatol Surg Res. 2018;104(2):161\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFineberg SJ, et al. Incidence and risk factors for postoperative delirium after lumbar spine surgery. Spine (Phila Pa 1976). 2013;38(20):1790\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeltran-Sanchez H, et al. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999\u0026ndash;2010. J Am Coll Cardiol. 2013;62(8):697\u0026ndash;703.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalaniappan LP, et al. Asian Americans have greater prevalence of metabolic syndrome despite lower body mass index. Int J Obes (Lond). 2011;35(3):393\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlberti KG, Zimmet P, Shaw J. Metabolic syndrome\u0026ndash;a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med. 2006;23(5):469\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLan RH, Kamath AF. American Association of Hip and Knee Surgeons\u0026ndash;Endorsed Comorbidity Coding for Total Joint Arthroplasty: How Often Did We Hit the Mark With International Classification of Diseases, Ninth Revision? J Arthroplast. 2016;31(12):2692\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShichman I, et al. Projections and Epidemiology of Revision Hip and Knee Arthroplasty in the United States to 2040\u0026ndash;2060. Arthroplast Today. 2023;21:101152.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwartz AM, et al. Projections and Epidemiology of Revision Hip and Knee Arthroplasty in the United States to 2030. J Arthroplasty. 2020;35(6S):S79\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai C et al. Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations. Nutrients, 2024. 16(11).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSzilagyi IA, et al. Metabolic syndrome, radiographic osteoarthritis progression and chronic pain of the knee among men and women from the general population: The Rotterdam study. Semin Arthritis Rheum. 2024;69:152544.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eY YHH, H.I.I. H, Y YFF. Differences in the components of metabolic syndrome by age and sex: a cross-sectional and longitudinal analysis of a cohort of middle-aged and older Japanese adults. BMC Geriatr., 2023(1): p. 438.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eT TKK et al. The Impact of Metabolic Syndrome and Obesity on Perioperative Total Joint Arthroplasty Outcomes: The Obesity Paradox and Risk Assessment in Total Joint Arthroplasty. Arthroplasty today., 2023: p. 101139.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eC CGG et al. Patients with metabolic syndrome have a greater rate of complications after arthroplasty: A systematic review and meta-analysis. Bone joint Res, 2020(3): pp. 120\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLA LAPP et al. Perioperative morbidity and mortality of same-admission staged bilateral TKA. Clin Orthop Relat Res, 2015(1): pp. 190\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSJP SJPS et al. Obesity, Metabolic Syndrome, and Osteoarthritis-An Updated Review. Curr Obes Rep, 2023(3): pp. 308\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLL LLNN et al. Decreasing Trend in Complications for Patients With Obesity and Metabolic Syndrome Undergoing Total Knee Arthroplasty From 2006 to 2017. J Arthroplast, 2022(6S): pp. S159\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCicekli I, Saglam D, Takar N. A New Perspective on Metabolic Syndrome with Osteopontin: A Comprehensive Review. Life (Basel), 2023. 13(7).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009;2(5\u0026ndash;6):231\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM MFF, FJ FJPP, E.U.U. G, GE. Management of Hyperglycemic Crises: Diabetic Ketoacidosis and Hyperglycemic Hyperosmolar State. The Medical clinics of North America., 2017(3): pp. 587\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXQ XHH, L LZZ. Oxidative Regulation of Vascular Ca(v)1.2 Channels Triggers Vascular Dysfunction in Hypertension-Related Disorders. Antioxidants, 2022(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL LZZ et al. Metabolic syndrome and risk of subclinical hypothyroidism: a systematic review and meta-analysis. Front Endocrinol., 2024: p. 1399236.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eM MGG, R RJJ. The effect of hypothyroidism occurring in patients with metabolic syndrome. Endokrynologia Polska, 2015(4): pp. 288\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKing LK, March L, Anandacoomarasamy A. Obesity \u0026amp; osteoarthritis. Indian J Med Res. 2013;138(2):185\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGomez R, et al. Adiponectin and leptin increase IL-8 production in human chondrocytes. Ann Rheum Dis. 2011;70(11):2052\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang L, et al. Yeast Cell wall Particle mediated Nanotube-RNA delivery system loaded with miR365 Antagomir for Post-traumatic Osteoarthritis Therapy via Oral Route. Theranostics. 2020;10(19):8479\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbella V, et al. The potential of lipocalin-2/NGAL as biomarker for inflammatory and metabolic diseases. Biomarkers. 2015;20(8):565\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBM BMDD et al. The burden of metabolic syndrome on osteoarthritic joints. Arthritis Res therapy, 2019(1): p. 289.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBondeson J, et al. The role of synovial macrophages and macrophage-produced mediators in driving inflammatory and destructive responses in osteoarthritis. Arthritis Rheum. 2010;62(3):647\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobinson WH, et al. Low-grade inflammation as a key mediator of the pathogenesis of osteoarthritis. Nat Rev Rheumatol. 2016;12(10):580\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome associated osteoarthritis (MetS-OA), total knee arthroplasty (TKA), Complications, Database, Risk factor","lastPublishedDoi":"10.21203/rs.3.rs-6591213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6591213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aimed to evaluate the trends in Metabolic Syndrome-associated Osteoarthritis (MetS-OA) among patients undergoing Total Knee Arthroplasty (TKA) and to identify key risk factors associated with this condition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective analysis was conducted using data from the Nationwide Inpatient Sample (NIS) from 2009 to 2019. The study examined patient demographics, hospital characteristics, length of stay (LOS), total hospitalization costs, in-hospital mortality rates, comorbid conditions, and perioperative complications. Multivariable logistic regression analysis was utilized to explore the relationship between MetS-OA and medical outcomes in TKA patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Out of 1,361,454 TKA procedures analyzed, 1,330,399 unique patients were included. The overall incidence of MetS-OA was 16.1%, with an upward trend from 2011 to 2019. Key risk factors forMetS-OA inTKA patients included advanced age, male gender, non-White racial backgrounds, and comorbidities such as chronic pulmonary disease, depression, and hypothyroidism. Patients with MetS-OA experienced longer hospital stays and incurred higher median hospitalization costs by $1,445.50, though no significant increase in mortality was observed. Besides, MetS-OA patients were more likely to experience postoperative complications, including acute myocardial infarction, severe malnutrition, acute cerebrovascular disease, postoperative delirium, acute respiratory distress syndrome (ARDS), prolonged mechanicalventilation, pneumonia, urinary tract infections, acute renal failure, and surgical complications such as lower limb nerve injury.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The incidence of MetS-OA among TKA patients is increasing, with several patient- and hospital-related factors significantly influencing the risk of postoperative complications. Preoperative optimization of high-risk patients and the implementation of standardized treatment protocols for MetS-OA may help reduce adverse events, improve patient outcomes, and lower healthcare costs.\u003c/p\u003e","manuscriptTitle":"Risk Factors And Complications Analysis Of Total Knee Arthroplasty In Patients With Metabolic Syndrome Associated Osteoarthritis: A 10-year Retrospective Study Of A National Inpatient Sample Database.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-10 06:04:28","doi":"10.21203/rs.3.rs-6591213/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"679c77ba-850f-408d-9318-25a0c74aa7b5","owner":[],"postedDate":"July 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T19:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-10 06:04:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6591213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6591213","identity":"rs-6591213","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}