Body Mass Index and Outcomes After Pilon Fracture Fixation: A Retrospective Cohort Study

Abstract

Background:

Pilon fractures of the distal tibial plafond account for 1% to 10% of all tibial fractures and are often associated with serious complications. As body mass index (BMI) is known to impact post-operative outcomes, particularly infection and nonunion, after trauma, the goal of this study is to explore these trends after pilon fractures.

Methods:

A single-center retrospective review of patients undergoing surgical fixation for pilon fractures between January 2013 and June 2023 was conducted. Only patients with at least a 6-month follow-up period were included. Demographic data and injury characteristics were extracted. Post-operative outcomes and complications were computed. Bivariate analysis via t test were applied, followed by multivariate analysis using primary and reduced models to evaluate for poor outcomes. Various BMI cutoffs (≥30, ≥35, ≥40, ≥45) were also employed to evaluate the relationship between outcomes and various obesity categories. Nonunion was assessed at ≥6 months postoperatively.

Results:

There were 132 patients included. There was no significant relationship between BMI and infection rates, nonunion rates, and development of post-traumatic osteoarthritis (PTOA) on bivariate analysis (P > .05). On multivariate analysis, diabetes mellitus was a risk factor for infection (P = .01), but BMI was not predictive of any outcomes in the primary or reduced models (P > .05). There were no significant differences in infection, nonunion, and PTOA rates when employing various BMI cutoffs (P > .05).

Discussion:

BMI was not found to be an independent predictor of post-operative complications in patients with pilon fractures in this cohort. Our study suggests that pilon fractures are unique and counter historic lower-extremity injury postoperative care protocols that consider weight, although further investigation in larger cohorts with long-term follow-up is required to define this trend.

Level of Evidence:

Level III, retrospective cohort series.

Keywords

pilon distal tibial plafond obesity body mass index infection

Introduction

Fractures of the distal tibial plafond, or pilon fractures, account for approximately 1% to 10% of tibial fractures and less than 1% of all lower-extremity fractures, and are a result of high-energy axial loading mechanisms.^18,28,38 The complex anatomy of the distal tibia and the limited surrounding soft tissue makes these injuries particularly complicated with regard to fracture reduction and soft tissue management.^23,28 Open reduction and internal fixation (ORIF) remains the standard surgical management of these fractures, although pilon ORIF continues to be associated with substantial rates of poor wound healing, infection, and post-traumatic osteoarthritis (PTOA), ultimately necessitating arthrodesis and amputation in some patients.^7,12,18,34

Although higher-severity pilon fractures may pose unavoidable postoperative complications, it has been postulated that certain patient-specific factors might be an independent predictor of poor prognosis.^25,29,36 Although body mass index (BMI) often influences complication rates and functional outcomes after orthopaedic procedures, specifically non-union and infection rates,^1,17 the literature lacks concordance on the relationship of BMI and outcomes of pilon fracture. Although some have hypothesized that increased soft tissue in obese patients may provide greater area for energy distribution and optimized skin coverage and lead to fewer complications, Graves et al¹⁰ reported that obesity does not appear to be protective. Others have described no relationship between BMI and clinical or radiologic outcomes,² although Kinder et al¹⁷ found non-union rates to be increased in patients with higher BMI after tibial shaft fractures. The limited literature describing the relationship between BMI and postoperative outcomes after surgical fixation of pilon fractures is further constrained by small sample sizes and a lack of adjustment for injury severity.¹⁷

This study aims to explore the impact of BMI on pilon fracture outcomes regarding infection rates, nonunion rates, and post-traumatic osteoarthritis.

Methods

This was a retrospective review of patients presenting with a pilon fracture and undergoing treatment from January 1, 2013, to June 1, 2023, by 10 fellowship-trained surgeons at a single academic institution. Patients were excluded if they had less than 6 months’ follow-up. Patients were identified using Current Procedural Terminology (CPT) code 27827 using institutional software.¹³

Demographic data, including age, sex, BMI, and medical comorbidities, were extracted. Injury characteristics, such as poly-trauma (defined by clinical discretion) and Orthopaedic Trauma Association (AO/OTA) classification (defined by imaging findings) were also included, determined by fellowship-trained orthopaedic surgeons. Treatment information, including irrigation and debridement, and external fixation, were recorded. Infection was defined by cultures positive for bacterial organisms for deep cultures. Non-union was defined by evidence of non-union on imaging (greater than 3 months postoperatively) determined by fellowship-trained orthopaedic surgeons.

Post-operative outcomes and complications, including infection, nonunion, and development of PTOA, were computed. BMI was evaluated as a continuous metric to explore development of outcomes. Patients were also categorized into 4 BMI groups: Underweight (BMI < 18.5), Normal Weight (BMI 18.5-24.9), Overweight (BMI 25-29.9), and Obese (BMI > 29.9).^17,20 Various BMI cutoffs were also used to further explore these trends: BMI < 30.0, BMI < 35.0, BMI < 40.0, and BMI < 45.0.

Bivariate analyses compared BMI as a continuous variable with and without each primary outcome (infection, nonunion, PTOA) using independent Student t tests. We also examined BMI thresholds at 30, 35, 40, and 45 by stratifying patients as <cutoff vs ≥cutoff and comparing outcomes with Fisher exact tests. Results were presented as odds ratios (ORs) with 95% CIs. Multivariable analyses used logistic regression to evaluate predictors of primary outcomes. Two models were fit for each outcome. The primary model was an a priori parsimonious model including only key covariates that might modulate primary outcome risk. This model included BMI, age, diabetes mellitus (DM), smoking status (current smoker vs not) and AO/OTA injury grade, and open fracture. Continuous variables were standardized using z-score transformation to allow for direct coefficient comparison. Injury grade was encoded ordinally. The reduced model was derived from the primary model, prioritizing only the most important predictors. We employed a change-in-estimate rule, as described by Talbot et al,³¹ to mitigate the risk of biased coefficients given the rarity of primary outcomes. Covariates were removed iteratively only when their P value in the current model exceeded 0.20 and their exclusion changed the BMI log-odds coefficient by <10%. This approach was applied separately for each outcome, so the retained covariates may differ across models. All models used complete-case analysis. All significance testing was 2-tailed with a threshold of P <.05. Analyses were performed in MATLAB (MathWorks).

Of 244 patients reviewed, 112 need not have sufficient follow-up and were excluded from analysis.

Results

Demographics

There were 132 patients who met inclusion criteria. There were 75 male and 57 female patients. The mean age at the time of treatment was 45.8 ± 15.9 (range, 15-81) years. Follow-up time for the cohort ranged from 6.0 to 107 months (mean: 17.6 months, SD: 18.2 months). Of 132 patients, 3 were underweight, 37 were normal weight, 37 were overweight, and 55 were obese. In this study, 11.4% (n = 15) of patients experienced infections, with 12.9% (n = 17) experiencing nonunion. Also, 15 patients had the diagnosis of DM prior to the time of presentation. In terms of injury characteristics, 37.9% (n = 50) were poly-trauma patients and 26.5% (n = 35) were open fractures.

Bivariate Analysis

Of 15 patients experiencing infections in this cohort, 2 were underweight, 3 were normal-weight, 2 were overweight, and 8 were obese. There was no significant difference between BMI in patients with infection vs no infection (P = .73). Of 17 patients experiencing nonunion, 6 were normal weight, 2 were overweight, and 9 were obese. There was no significant difference between BMI in patients experiencing union vs nonunion (P = .46). Of note, 1 patient lacked data for evidence of union because they had a below-knee amputation shortly after their procedure, so there were 114 patients in the union group. Of 36 patients experiencing PTOA, there was no significant difference between BMI between these cohorts (P = .81) (Table 1).

Table 1.

BMI Distribution by Infection, Nonunion, and PTOA Status.

	n	Mean BMI, Mean ± SD	P Value
Infection	15	30.43 ± 7.88
			.73
No infection	117	29.75 ± 7.02
Union	114	29.60 ± 6.88
			.46
Nonunion	17	30.97 ± 8.54
PTOA	36	29.74 ± 7.52
			.81
No PTOA	96	30.08 ± 5.88

Abbreviations: BMI, body mass index; PTOA, post-traumatic osteoarthritis.

Multivariate Analysis

On multivariate analysis, patients with DM had significantly higher infection rates than those without (OR: 7.22 [1.59-32.76], P = .01). Looking at the reduced model, DM was an independent predictor of infection in our cohort (OR: 7.12 [1.76-28.87], P = .006). BMI was not found to be predictive of any outcomes in either the primary or reduced models (Tables 2 and 3).

Table 2.

Primary Multivariate Model.

	OR [95% CI]	P Value
Infection
BMI	1.15 [0.63-2.12]	.64
Age	0.98 [0.51-1.91]	.96
Diabetes	7.22 [1.59-32.76]	.01*
Smoking	3.00 [0.64-14.1]	.16
AO OTA	0.45 [0.18-1.12]	.86
Open fracture	2.39 [0.70-8.16]	.16
Nonunion
BMI	1.17 [0.70-1.95]	.56
Age	0.77 [0.42-1.42]	.41
Diabetes	2.99 [0.62-14.35]	.17
Smoking	1.21 [0.29-5.04]	.8
AO OTA	1.70 [0.56-5.16]	.35
Open fracture	2.20 [0.73-6.63]	.16
PTOA
BMI	0.99 [0.66-1.48]	.95
Age	1.15 [0.76-1.75]	.52
Diabetes	1.44 [0.42-4.87]	.56
Smoking	1.69 [0.58-4.90]	.34
AO OTA	1.69 [0.81-3.50]	.16
Open fracture	0.82 [0.33-2.03]	.67

Abbreviations: AO OTA, Orthopaedic Trauma Association classification; BMI, body mass index; OR, odds ratio; PTOA, post-traumatic osteoarthritis.

Statistically significant.

Table 3.

Reduced Multivariate Model.

	OR [95% CI]	P Value
Infection
BMI	1.15 [0.63-2.09]	.64
Diabetes	7.12 [1.76-28.87]	.006*
Smoking	3.00 [0.64-14.14]	.16
AO OTA	0.45 [0.18-1.11]	.08
Open fracture	2.40 [0.70-8.16]	.16
Nonunion
BMI	1.16 [0.69-1.94]	.57
Diabetes	0.77 [0.42-1.42]	.4
Smoking	2.90 [0.62 -13.61]	.18
AO OTA	1.73 [0.57-5.20]	.33
Open fracture	2.23 [0.74-6.68]	.15
PTOA
BMI	0.99 [0.66-1.48]	.95
Diabetes	1.18 [0.79-1.77]	.42
Smoking	1.62 [0.56-4.65]	.37
AO OTA	1.68 [0.81-3.47]	.16
Open fracture	0.81 [0.33-2.00]	.65

Abbreviations: AO OTA, Orthopaedic Trauma Association classification; BMI, body mass index; OR, odds ratio; PTOA, post-traumatic osteoarthritis.

Statistically significant.

BMI Cutoffs

There were 55 patients with a BMI ≥30.0, with a 14.5% infection rate, 16.7% nonunion rate, and 29.1% PTOA rate. None of these were statistically significant (P > .05).

There were 31 patients with a BMI ≥35.0, with a 12.9% infection rate, 16.7% nonunion rate, and 22.6% PTOA rate. None of these were statistically significant (P > .05).

There were 10 patients with a BMI ≥40.0, with a 20.0% infection rate, 30.0% nonunion rate, and 20.0% PTOA rate. None of these were statistically significant (P > .05).

There were 5 patients with a BMI ≥45.0, with a 20.0% infection rate, 40.0% nonunion rate, and 0.0% PTOA rate. None of these were statistically significant (P > .05). Full breakdown can be found in Table 4.

Table 4.

Infection, Nonunion, and PTOA by BMI cutoff.

	n (%)	OR [95% CI]	P Value
BMI > 30
Infection	8 (14.5)	1.70 [0.58-5.01]	.41
Nonunion	9 (16.7)	1.73 [0.62-4.80]	.31
PTOA	16 (29.1)	1.17 [0.54-2.53]	.7
BMI > 35
Infection	4 (12.9)	1.21 [0.36-4.12]	.75
Nonunion	5 (16.7)	1.48 [0.48-4.61]	.54
PTOA	7 (22.6)	0.72 [0.28-1.86]	.65
BMI > 40
Infection	2 (20.0)	2.1 [0.40-10.94]	.32
Nonunion	3 (30.0)	3.28 [0.76-14.14]	.12
PTOA	2 (20.0)	0.65 [0.13-3.20]	.73
BMI > 45
Infection	1 (20.0)	2.02 [0.21-19.35]	.46
Nonunion	2 (40.0)	4.93 [0.76-31.97]	.13
PTOA	0 (0.0)	0	.32

Abbreviations: BMI, body mass index; OR, odds ratio; PTOA, post-traumatic osteoarthritis.

Discussion

This study explores the relationship between BMI and poor outcomes after surgical management of pilon fractures. The most important finding was that BMI was not an independent predictor of infection, nonunion, or PTOA in this cohort, consistent with the complexity and severity of these injuries in the setting of high-impact trauma.

Obesity has been recognized to play a pivotal role in poor fracture healing and development of osteoarthritis.^8,21 Specifically, higher BMI is associated with poor bone quality.¹⁹ Previous literature has described obesity as a chronic state of inflammation,⁵ suggesting that an increase in proinflammatory cytokines due to macrophage infiltration into adipose tissue may mediate catabolic processes in chondrocytes that ultimately contribute to extracellular matrix degradation.^9,22,33 Louer et al²¹ found that mice on high-fat diets with an intraarticular fracture sustained more osteoarthritis degeneration and synovial inflammation than those on low-fat diets. Specific cytokines, such as IL-6 and IL-8, have been significantly associated with obesity in numerous human and animal studies.^{9,11,16,24,26} Additionally, patients with higher BMI have increased risk of post-operative infections.^35,37 Because the prevalence of obesity in adults in the United States has increased steadily in recent years, with 86.3% of adults projected to be overweight or obese by 2030,³² it is crucial to understand the relationship between BMI and pilon fractures to optimize clinical outcomes in patients experiencing these complex injuries.

This study found no difference in infection rates with regard to patient BMI. Previous literature has reported various associations between BMI and infection rates in patients with pilon fractures. Ceçen noted increased rates of superficial infections in patients with higher BMI,² and Duckworth et al⁷ demonstrated a similar trend in patients with 43C pilon fractures. However, Spitler et al³⁰ found that BMI is not associated with deep infections in their review of 150 patients. Yeramosu et al³⁶ similarly described no significant association between infection rate and BMI in patients with operatively treated pilon fractures. Rather, diabetes mellitus was predictive of increased infection rates in our cohort, which is congruent with previous studies.^15,36 Other previously reported risk factors for infection after surgical management of pilon fractures included preoperative albumin, preoperative white blood cell count, and hospital length of stay.⁴ These results, in the context of discordant findings in prior literature, suggest that BMI does not have a definitive impact on infection rates, and optimizing patient’s infection risk should focus on injury severity and complexity instead.

This study also found no significant relationship between BMI and nonunion or PTOA rates. It has been well described in the literature that patients suffering pilon fractures are at high risk for development of PTOA despite advancements in soft tissue management and surgical technique in the past decades, with specific risk factors leading to PTOA including injury energy, degree of articular cartilage damage, and infection.^3,6,27 To our knowledge, no studies have previously explored the relationship between BMI and onset of PTOA after pilon fractures. Prior literature has looked at ankle fractures more generally and has reported obesity as a risk factor for development of PTOA.¹⁴ Further investigation with long-term follow-up is required to determine whether this trend applies to pilon fractures as well.

Limitations of this study include that it is retrospective in nature; therefore, our data analysis depends on the accuracy and extent of previous documentation in the electronic medical record. Because many trauma patients are frequently lost to follow-up, we implemented a 6-month follow-up period to preserve a robust cohort size, although we do recognize this is a shorter follow-up period. We recognize small event counts may limit model stability and widen CIs despite model reduction; therefore, results should be interpreted as exploratory. We also recognize that the relationship between BMI and outcomes after pilon fractures continues to be a key issue that merits further research in a larger cohort given a rarity of these outcomes at our institution. Thus, the generalizability of our study's findings are limited.

Conclusion

BMI was not an independent predictor of post-operative complications in patients with pilon fractures in this cohort. Our study suggests that pilon fractures are unique and counter historic lower-extremity injury postoperative care protocols that consider weight, although verification in larger multi-center cohorts with longer follow-up is needed.

Supplemental Material

sj-pdf-1-fao-10.1177_24730114251398761 – Supplemental material for Body Mass Index and Outcomes After Pilon Fracture Fixation: A Retrospective Cohort Study

Supplemental material, sj-pdf-1-fao-10.1177_24730114251398761 for Body Mass Index and Outcomes After Pilon Fracture Fixation: A Retrospective Cohort Study by Julia E. Ralph, Crystal Jing, Jackson Cathey, Kathleen Chang, Albert T. Anastasio, Joshua K. Helmkamp, Alexandra Krez, Kevin A. Wu, Jacob Torrey, Anna R. Bryniarski and Samuel B. Adams in Foot & Ankle Orthopaedics

Footnotes

ORCID iDs

Julia E. Ralph, BA,

Crystal Jing, BA,

Albert T. Anastasio, MD,

Kevin A. Wu, BS,

Ethical Considerations

Ethical approval for this study was obtained from the Duke Health Institutional Review Board (IRB No. Pro00114620)

Funding

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

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Samuel B. Adams, MD, reports general disclosures of American Orthopaedic Foot & Ankle Society: board or committee member; Conventus/Flower: paid consultant; DJ Orthopaedics: IP royalties; DJO: paid consultant; Exactech, Inc: paid consultant; Medshape: stock or stock options; Orthofix, Inc: paid consultant; Regeneration Technologies, Inc: paid consultant; Stryker: paid consultant. Disclosure forms for all authors are available online.

References

Cardoso

Paccaud

Dubois-Ferriere

, et al. The effect of BMI on long-term outcomes after operatively treated ankle fractures: a study with up to 16 years of follow-up. BMC Musculoskelet Disord. 2022;23(1):317. doi:10.1186/s12891-022-05247-3

Cecen

Gulabi

Yanik

Pehlivanoglu

Bekler

Elmali

Effect of BMI on the clinical and radiological outcomes of pilon fractures. Acta Orthop Traumatol Turc. 2014;48(5):570-575. doi:10.3944/AOTT.2014.14.0073

Chen

Lee

YS.

Long-term results of pilon fractures. Arch Orthop Trauma Surg. 2007;127(1):55-60. doi:10.1007/s00402-006-0225-3

Cohen

Kyin

Norman

, et al. Risk factors for postoperative infection in patients after pilon fracture fixation. J Foot Ankle Surg. 2025;64(4):397-401. doi:10.1053/j.jfas.2025.01.009

Das

UN.

Is obesity an inflammatory condition?

Nutrition. 2001;17(11-12):953-966. doi:10.1016/s0899-9007(01)00672-4

DeKeyser

Thorne

Olsen

Haller

JM.

Porcine pilon fractures result in significant chondrocyte cell death on both sides of the tibiotalar joint. Foot Ankle Int. 2025;46(8):827-835. doi:10.1177/10711007251341310

Duckworth

Jefferies

Clement

White

TO.

Type C tibial pilon fractures: short- and long-term outcome following operative intervention. Bone Joint J. 2016;98-B(8):1106-1111. doi:10.1302/0301-620x.98b8.36400

Felson

Anderson

Naimark

Walker

Meenan

RF.

Obesity and knee osteoarthritis. The Framingham study. Ann Intern Med. 1988;109(1):18-24. doi:10.7326/0003-4819-109-1-18

Fernandes

Martel-Pelletier

Pelletier

JP.

The role of cytokines in osteoarthritis pathophysiology. Biorheology. 2002;39(1-2):237-246.

10.

Graves

Porter

Fagan

, et al. Is obesity protective against wound healing complications in pilon surgery? Soft tissue envelope and pilon fractures in the obese. Orthopedics. 2010;33(8). doi:10.3928/01477447-20100625-27

11.

Griffin

Huebner

Kraus

Guilak

Extreme obesity due to impaired leptin signaling in mice does not cause knee osteoarthritis. Arthritis Rheum. 2009;60(10):2935-2944. doi:10.1002/art.24854

12.

Harris

Patterson

Sontich

Vallier

HA.

Results and outcomes after operative treatment of high-energy tibial plafond fractures. Foot Ankle Int. 2006;27(4):256-265. doi:10.1177/107110070602700406

13.

Horvath

Rusincovitch

Brinson

Shang

Evans

Ferranti

JM.

Modular design, application architecture, and usage of a self-service model for enterprise data delivery: the Duke Enterprise Data Unified Content Explorer (DEDUCE). J Biomed Inform. 2014;52:231-242. doi:10.1016/j.jbi.2014.07.006

14.

Janghala

Niknam

Freshman

Cogan

Zhang

Lansdown

Effect of obesity on short- and long-term complications after ankle fracture fixation. J Orthop Trauma. 2024;38(9):e312-e317. doi:10.1097/BOT.0000000000002849

15.

Jing

Ralph

Chang

, et al. Risk factors for postoperative infection and associated outcomes after pilon fracture fixation: a propensity-matched cohort study. Foot Ankle Int. 2025;46(9):1049-1058. doi:10.1177/10711007251344250

16.

Kim

Park

Kawada

, et al. Circulating levels of MCP-1 and IL-8 are elevated in human obese subjects and associated with obesity-related parameters. Int J Obes (Lond). 2006;30(9):1347-1355. doi:10.1038/sj.ijo.0803259

17.

Kinder

Giannoudis

Boddice

Howard

The effect of an abnormal BMI on orthopaedic trauma patients: a systematic review and meta-analysis. J Clin Med. 2020;9(5):1302. doi:10.3390/jcm9051302

18.

Kugach

Leong

Clements

JR.

Management of pilon fractures. Clin Podiatr Med Surg. 2024;41(3):503-518. doi:10.1016/j.cpm.2024.01.007

19.

Lanzetti

Lupariello

Venditto

, et al. The role of diabetes mellitus and BMI in the surgical treatment of ankle fractures. Diabetes Metab Res Rev. 2018;34: 1-5. doi:10.1002/dmrr.2954

20.

Lazar

Plocher

Egol

KA.

Obesity and its relationship with pelvic and lower-extremity orthopedic trauma. Am J Orthop (Belle Mead NJ). 2010;39(4):175-182.

21.

Louer

Furman

Huebner

Kraus

Olson

Guilak

Diet-induced obesity significantly increases the severity of posttraumatic arthritis in mice. Arthritis Rheum. 2012;64(10):3220-3230. doi:10.1002/art.34533

22.

Lumeng

Deyoung

Bodzin

Saltiel

AR.

Increased inflammatory properties of adipose tissue macrophages recruited during diet-induced obesity. Diabetes. 2007;56(1):16-23. doi:10.2337/db06-1076

23.

Mauffrey

Vasario

Battiston

Lewis

Beazley

Seligson

Tibial pilon fractures: a review of incidence, diagnosis, treatment, and complications. Acta Orthop Belg. 2011;77(4):432-440.

24.

Neels

Badeanlou

Hester

Samad

Keratinocyte-derived chemokine in obesity: expression, regulation, and role in adipose macrophage infiltration and glucose homeostasis. J Biol Chem. 2009;284(31):20692-20698. doi:10.1074/jbc.M109.018556

25.

Olson

Anand

Esposito

, et al. Complications and soft-tissue coverage after complete articular, open tibial plafond fractures. J Orthop Trauma. 2021;35(10):e371-e376. doi:10.1097/bot.0000000000002074

26.

Pou

Massaro

Hoffmann

, et al. Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: the Framingham Heart Study. Circulation. 2007;116(11):1234-1241. doi:10.1161/circulationaha.107.710509

27.

Rubio-Suarez

Carbonell-Escobar

Rodriguez-Merchan

Ibarzabal-Gil

Gil-Garay

Fractures of the tibial pilon treated by open reduction and internal fixation (locking compression plate-less invasive stabilising system): complications and sequelae. Injury. 2018;49:S60-S64. doi:10.1016/j.injury.2018.06.043

28.

Scolaro

Ahn

In Brief: Pilon fractures. Clin Orthop Relat Res. 2011;469(2):621-623. doi:10.1007/s11999-010-1509-z

29.

Shafiq

Zhang

Zhu

, et al. Reducing complications in pilon fracture surgery: surgical time matters. J Orthop Trauma. 2023;37(10):532-538. doi:10.1097/bot.0000000000002637

30.

Spitler

Hulick

Weldy

Howell

Bergin

Graves

ML.

What are the risk factors for deep infection in OTA/AO 43C pilon fractures?

J Orthop Trauma. 2020;34(6):e189-e194. doi:10.1097/bot.0000000000001726

31.

Talbot

Diop

Lavigne-Robichaud

Brisson

The change in estimate method for selecting confounders: a simulation study. Stat Methods Med Res. 2021;30(9):2032-2044. doi:10.1177/09622802211034219

32.

Wang

Beydoun

Liang

Caballero

Kumanyika

SK.

Will all Americans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity. 2008;16(10):2323-2330. doi:10.1038/oby.2008.351

33.

Weisberg

McCann

Desai

Rosenbaum

Leibel

Ferrante

Jr.

Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest. 2003;112(12):1796-1808. doi:10.1172/jci19246

34.

Wheelwright

Christensen

Cizik

Zhang

Marchand

Haller

JM.

Significant factors of high performance outcomes for tibial plafond fractures. Foot Ankle Int. 2022;43(10):1261-1268. doi:10.1177/10711007221112924

35.

Whiting

White-Dzuro

Avilucea

, et al. Body mass index predicts perioperative complications following orthopaedic trauma surgery: an ACS-NSQIP analysis. Eur J Trauma Emerg Surg. 2017;43(2):255-264. doi:10.1007/s00068-016-0642-0

36.

Yeramosu

Satpathy

Perdue

Jr , et al. Risk factors for infection and subsequent adverse clinical results in the setting of operatively treated pilon fractures. J Orthop Trauma. 2022;36(8):406-412. doi:10.1097/bot.0000000000002339

37.

Yuan

Chen

H-L.

Obesity and surgical site infections risk in orthopedics: a meta-analysis. Int J Surg. 2013;11(5):383-388. doi:10.1016/j.ijsu.2013.02.018

38.

Zelle

Dang

Ornell

SS.

High-energy tibial pilon fractures: an instructional review. Int Orthop. 2019;43(8):1939-1950. doi:10.1007/s00264-019-04344-8

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