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Abstract

Background

The utilization of total ankle arthroplasty (TAA) continues to increase. Discharge to a post-acute care (PAC) facility can increase patient morbidity and postoperative costs. The purpose of this study is to investigate the effects of age and body mass index (BMI) on discharge to a PAC facility and hospital length of stay (LOS) following TAA.

Methods

A retrospective review of patients who underwent TAA from the National Surgical Quality Improvement Program (NSQIP) database was performed. Using overweight patients as the reference BMI group, sex- and age-adjusted log-binomial regression models were utilized to estimate risk ratios of BMI categories for being discharged to a PAC facility. A linear regression was utilized to estimate the effect of BMI category on hospital LOS.

Results

Obese patients had 1.36 times the risk of overweight patients (P = .040), and morbidly obese patients had 2 times risk of overweight patients (P = .001) of being discharged to a PAC facility after TAA. Men had 0.48 times the risk of women (P < .001). Compared with patients aged 18 to 44 years, patients aged ≥65 years had 4.13 times the risk (P = .012) of being discharged to a PAC facility after TAA. Relative to overweight patients, on average there was no difference in hospital LOS for underweight patients, but healthy weight patients stayed an additional 0.30 days (P=.003), obese patients stayed an additional 0.18 days (P = .011), and morbidly obese patients stayed an additional 0.33 days (P = .009). Men stayed 0.29 fewer hospital days than women (P < .001) on average.

Conclusion

Women and patients who are obese or morbidly obese have a longer hospital LOS and an increased chance of being discharged to a PAC facility. Increasing age is also associated with an increased risk of being discharged to a PAC. These may be important factors when developing and discussing the postoperative plan with patients prior to TAA.

Levels of Evidence:

Level III

Keywords

obesity ankle injuries arthritis and joint disease joint replacement developing a treatment algorithm

“The results of our study demonstrated that, compared with the overweight BMI group, obese and morbidly obese patients had a significantly increased length of hospital stay and a greater risk of being discharged to a PAC facility after TAA.”

Introduction

The ankle joint is subject to more pressure and is injured more commonly than any other joint in the body.^1,2 An increasing number of young and older adults in their working lives and beyond are developing ankle arthritis.³ Total ankle arthroplasty (TAA) is a safe and effective treatment for various ankle pathologies, including osteoarthritis, osteonecrosis, inflammatory joint diseases, and posttraumatic arthropathies.^1,4
-8 Total ankle arthroplasty preserves motion at the ankle joint while still achieving the primary goal of pain relief.⁹ With an increasing number of patients undergoing TAA, identifying modifiable preoperative factors that influence postoperative outcomes after TAA may be valuable.¹⁰

Body mass index (BMI) plays a role in postoperative joint outcomes and previous literature has cited various postoperative complications associated with a very high and/or low BMI.^11
-22 However, to our knowledge, no other study has evaluated the impact of BMI on discharge to a post-acute care (PAC) facility for TAA. As patient safety and decreasing cost of care are emphasized topics in medicine and discharge to a PAC facility has been shown to increase patient morbidity and long-term cost through postoperative adverse events and readmissions, it may be important to identify risk factors for discharge to a PAC facility.^23
-25

The purpose of this study is to investigate the associations of age, sex, and BMI on discharge to a PAC facility and hospital length of stay following TAA. It was hypothesized that outcomes would be worse for underweight as well as morbidly obese patients, and that patients in these BMI categories along with those of increasing age would be more likely to be discharged to a PAC facility and have a longer total length of hospital stay.

Methods

After IRB approval from the Prisma Health Institutional Review Board [1954940], a retrospective review was performed to identify a large cohort of patients who underwent TAA from the American College of Surgeons–National Surgical Quality Improvement Program (ACS-NSQIP) database. This database is a robust, generalizable database from more than 700 participating institutions, which is collected by trained clinical reviewers and includes preoperative, demographic, comorbidity, and postoperative data.^26
-28 It has been widely used in orthopaedic surgery to track the clinical course of patients.^29,30

Adult patients (aged ≥ 18 years) who underwent TAA between 2006 and 2020 were identified by Current Procedural Terminology (CPT) code 27702. Patients were excluded from this study if they underwent hemiarthroplasty, revision TAA, or had an outpatient procedure. An individual patient’s outpatient/ambulatory surgical procedure status was defined by the institution in which they had the surgery, following that respective hospital’s criteria of being an outpatient. A total of 1653 patients were included in this study.

The BMI groups were defined in accordance with the World Health Organization’s definitions as underweight (BMI <18.5; n = 7), healthy weight (BMI 18.5 to <25; n = 213), overweight (BMI 25 to <30; n = 594), obese (BMI 30 to <40; n = 714), morbidly obese (BMI ≤40; n = 125).³¹ A sex- and age-adjusted logistic regression model was utilized on BMI categories to estimate odds ratios and then a sex- and age-adjusted log-binomial regression model was utilized on BMI categories to estimate risk ratios. Using overweight patients as the reference BMI group allowed for investigation of whether other BMI groups had an increased risk (relative to the reference group) of being discharged somewhere other than home, as in a PAC facility. Similarly, using the 18 to 44 years age category as the reference age group allowed for analysis of whether the middle and older age groups had an increased risk of being discharged to a PAC facility (relative to the youngest age group). Furthermore, selecting these respective reference groups allowed risk ratios to be reported as uniformly positive values.

In the case of BMI and age categories, indicator variables were included for all non-reference groups into the regression models. In so doing, the estimated coefficients represented the difference between the indicated group and the excluded reference group. In linear regression models, the estimated coefficient then represented the average outcome associated with the indicated group minus the average response of the reference group. If the coefficient was positive, the average outcome of the indicated group was larger than the average response of the reference group; if the coefficient was negative, the average outcome of the indicated group was smaller than the average response of the reference group. In either case, whether that difference was statistically significant is related to the associated P value of the test.

In logit-binomial regression models (logistic), the estimated coefficients are related through odds ratios. That is, the exponentiated coefficient of the indicated variable is the estimated odds ratio of the indicated group relative to the reference group. Similarly, in log-binomial regression models, the estimated coefficients are related through risk ratios. That is, the exponentiated coefficient of the indicated variable is the estimated risk ratio of the indicated group relative to the reference group.³²

Results

More than 80% of patients who underwent TAA provided a race identity, with the majority (77.6%) being white (Table 1). This trend is consistent with that of previous studies documenting racial disparities for the utilization of TAA in the United States.^33,34 Obese patients had 1.36 times the risk of overweight patients (P = .079), and morbidly obese patients had 2 times the risk of overweight patients (P = .001) of being discharged to a PAC facility (Table 2). The unadjusted differences in rates illustrated in Table 1 show that 10.3% of overweight patients, compared with 20% of obese patients, were discharged to a PAC facility. This risk ratio was the strongest clinical signal in our analysis (based on the size of the adjusted risk ratios).

Table 1.

Patient Demographics.

Demographic	Level	Discharged home	Discharged to a PAC facility	Total
Demographic	Level	Count (%)	Count (%)	Count (% N)
Age in years	18-44	53 (94.64)	3 (5.36)	56 (3.39)
	45-64	632 (91.59)	58 (8.41)	690 (41.74)
	≥65	747 (82.36)	160 (17.64)	907 (54.87)
Sex	Female	626 (81.62)	141 (18.38)	767 (46.40)
Sex	Male	806 (90.97)	80 (9.03)	886 (53.60)
BMI	Underweight	6 (85.71)	1 (14.29)	7 (0.42)
	Healthy weight	179 (84.04)	34 (15.96)	213 (12.89)
	Overweight	533 (89.73)	61 (10.27)	594 (35.93)
	Obese	614 (85.99)	100 (14.01)	714 (43.19)
	Morbidly obese	100 (80.00)	25 (20.00)	125 (7.56)
Race	American Indian/Alaska Native	1 (100.00)	0 (0.00)	1 (0.06)
	Asian	15 (78.95)	4 (21.05)	19 (1.15)
	Black or African American	37 (80.43)	9 (19.57)	46 (2.78)
	Native Hawaiian/Pacific Islander	4 (100.00)	0 (0.00)	4 (0.24)
	White	1,107 (85.95)	181 (14.05)	1,288 (77.92)
	Unknown	271 (91.86)	24 (8.14)	295 (17.85)

Abbreviations: BMI, body mass index; PAC, post-acute care.

Table 2.

Comparison of the Risk Ratios of Being Discharged to a PAC Facility for Different Preoperative Variables.

Preoperative variables	Risk ratio	SE	95% CI	P value
Age 18-44^a	—	—	—	—
Age 45-64	1.72	0.99	[0.56-5.29]	.344
Age ≥65	4.13	2.33	[1.37-12.47]	.012
Female^a	—	—	—	—
Male	0.48	0.06	[0.37-0.62]	<.001
Overweight^a	—	—	—	—
Underweight	1.38	1.26	[0.23-8.24]	.725
Healthy weight	1.41	0.27	[0.96-2.06]	.079
Obese	1.36	0.20	[1.01-1.83]	.040
Morbidly obese	2.00	0.42	[1.32-3.02]	.001

Confidence intervals that contain “1” indicate no significant difference in risk of discharge to a PAC facility for the indicated categorical level and the referent level.

Abbreviations: BMI, body mass index; PAC, post-acute care.

BMI categories are relative to the overweight group. The male group is relative to the female group. Age categories are relative to the 18 to 44 years age group.

Men stayed 0.29 fewer days in the hospital compared with women (P < .001; Table 3). Furthermore, 18% of women were discharged to a PAC facility versus just 9% of men (Table 1), demonstrating that men had only 0.48 times the risk of women (P < .001) of being discharged to a PAC facility (Table 2).

Table 3.

Comparison of Total Length of Hospital Stay for Different Preoperative Variables.

Preoperative variables	Days	SE	95% CI	P value
Age 18-44^a	—	—	—	—
Age 45-64	–0.21	0.17	[–0.56-0.13]	.222
Age ≥65	–0.04	0.17	[–0.38-0.30]	.812
Female^a	—	—	—	—
Male	–0.29	0.06	[–0.42 to –0.17]	<.001
Overweight^a	—	—	—	—
Underweight	0.16	0.48	[–0.78-1.09]	.739
Healthy weight	0.30	0.10	[0.10-0.50]	.003
Obese	0.18	0.07	[0.04-0.31]	.011
Morbidly obese	0.33	0.13	[0.08-0.58]	.009

Confidence intervals that contain “0” indicate no significant difference between total length of stay between the indicated categorical level and the referent level.

Abbreviation: BMI, body mass index.

BMI categories are relative to the overweight group. The male group is relative to the female group. Age categories are relative to the 18 to 44 years age group.

Age was not found to have a significant impact on hospital length of stay (age 45-64 years: P = .222, age ≥65 years: P = .812; Table 3). However, 17.6% of patients aged 65+ years were discharged to a PAC facility versus just 5.4% of patients aged 18 to 44 years (Table 1), representing a 4-fold increased risk (P = .012) of being discharged to a PAC facility for the older adults relative to the 18 to 44 years age group (Table 2).

Finally, relative to overweight patients, there was no difference in average length of hospital stay for underweight patients, but healthy weight patients stayed an additional 0.30 days (P = .003), obese patients stayed an additional 0.18 days (P = .011), and morbidly obese patients stayed an additional 0.33 days (P = .009) on average.

Discussion

Some potential factors, such as age, sex, socioeconomic status, and preoperative diagnosis are not acutely modifiable.³⁵ However, modifiable risk factors such as BMI could be addressed preoperatively to maximize the patient’s chances of postoperative morbidity associated with PAC utilization and to limit unnecessary health care costs. The results of our study demonstrated that, compared with the overweight BMI group, obese and morbidly obese patients had a significantly increased length of hospital stay and a greater risk of being discharged to a PAC facility after TAA. In addition, our results noted that men had a decreased risk of being discharged to a PAC facility and a shorter total length of hospital stay when compared with women. In addition, patients aged ≥65 years had an increased risk of being discharged to a PAC facility compared with the 18 to 44 years age group after TAA.

Several studies have analyzed BMI and length of hospital stay; whereas some studies found that a higher BMI is associated with a longer hospital stay, other studies found no evidence of a statistically significant association between BMI and length of hospital stay.^36
-39 Our results showed that, compared with the overweight BMI group, all BMI categories, except for underweight patients, had longer hospital stays on average (Table 3). Overall, our results indicated that the overweight BMI group had the shortest length of hospital stay although this was not statistically different from the normal weight group. This more favorable outcome for the overweight group could be explained by inaccuracy with the cutoffs when using BMI as a measuring method. This system overpredicts the weight of muscular individuals and underpredicts the weight of those with less muscle, which could skew results.

Previous studies have described the association between obesity and medical complications, such as thrombotic events, postoperative falls, and revision/failures.^{11,12,17,18,20} In addition, the chronic, low-grade systemic inflammation and impaired functional status associated with a higher BMI has been shown to compromise postoperative recovery after certain procedures.¹⁵ Therefore, physicians may believe that patients with a higher BMI need to be monitored more intensely due to their increased risk for postoperative complications and impaired ability to heal. This potential need for extra support may explain the results of our study that obese patients had 1.36 times the risk (P = .040) and morbidly obese patients had 2 times the risk (P = .001) of being discharged to a PAC facility after TAA, when compared with the overweight BMI group. Patients with higher BMIs being more likely to be discharged to a PAC facility may also contribute to the reason why previous studies have found heavier patients to have increased postoperative costs.⁴⁰ It may be important for surgeons to consider that obese and morbidly obese patients are more likely to be discharged to a PAC facility. In increasingly cost-constrained environments where it may be favorable to avoid discharge to a PAC facility due to both cost and complication risks, it may be a consideration for the surgeon to discuss lifestyle changes, such as diet and exercise, to help lower the patient’s BMI before surgery to avoid discharge to a facility other than home. However, being underweight has also been associated with increased mortality and worse outcomes postoperatively in the literature.^{13,14,16,19,23,41} Despite this, the results of our study demonstrated that underweight patients did not have an increased risk of being discharged to a PAC facility (P = .725). Of note, this lack of statistical significance may have been skewed by the small sample size of the underweight BMI group in our study, which therefore may not be representative of the true effect for this cohort.

Our study also evaluated the effect of sex on discharge disposition to PAC. Prior literature found that being male was associated with a decreased likelihood of discharge to an extended care facility after total knee arthroplasty.^42,43 Our study corroborated this finding for patients undergoing ankle arthroplasty and, as noted in our results, men had only 0.48 times the risk of women of being discharged to a PAC facility after TAA (P < .001). Our results also demonstrated that men stayed in the hospital on average 0.29 fewer days than women (P < .001), which is consistent with data from previous studies noting that women undergoing orthopaedic procedures were more likely to have longer hospital stays.^44,45

In addition, we analyzed age as a risk factor for discharge to a PAC. Prior literature has shown that increased age is associated with adverse outcomes after surgical intervention.^39,46
-51 The results of our study found that patients aged ≥65 years had 4.13 times the risk of being discharged to a PAC facility after TAA (P = .012). We also analyzed the effect of age on hospital stay duration and our results demonstrated that age category did not have a statistically significant impact on hospital length of stay. This was in contrast to prior literature that noted an association between patient age and hospital length of stay.^52,53 However, these studies analyzed age as a continuous variable, which may decrease the validity of their findings. The impact that each year older in age makes is likely different among the younger population compared with the older population. Therefore, we feel that the method of analyzing age as a categorical variable that differentiates younger, middle, and older age groups provides more clarity.

The use of PAC has grown substantially in the past several decades.⁵⁴ More than 40% of Medicare beneficiaries receive PAC after hospital discharge.⁵⁵ In 2015, Medicare spent more than $60 billion on PAC, an amount that has been rapidly increasing.^56,57 However, the choice of setting for PAC can impact patient outcomes and cost. A 2016 study found that, after elective total joint arthroplasty, discharge to a skilled nurse facility or an inpatient rehabilitation facility increased the risk of post-discharge adverse events when compared with discharge home.²³ Another 2016 study found that patients who underwent revision total joint arthroplasty and were discharged to a skilled nursing facility or an inpatient rehabilitation facility had significantly increased risk for unplanned readmissions when compared with patients discharged home.²⁴ Of note, the average readmission cost following total joint arthroplasty is approximately $24 000.⁵⁸ These studies showed that discharge destination is an independent risk factor for adverse events requiring readmission, leading to increased patient morbidity and unnecessary health care costs. Discharge destination has also been found to be an independent risk factor for hospital readmissions in older trauma patients despite controlling for injury severity and comorbidities.²⁵ Given this, identifying a modifiable preoperative variable such as larger BMI to carry a greater risk of being discharged to a PAC facility may predict the risk of patient morbidity and substantially higher health care costs, but that is outside the scope of this study.

Many prior studies have used healthy weight as the reference group to facilitate the evaluation of the effect of BMI on postoperative complications and outcomes. However, given that patients in our data set were more likely to be obese or overweight, we chose to highlight the comparison of other BMI categories with a more represented BMI group (overweight patients). In addition, given that the average BMI in the United States continues to increase, we felt that the frequency of each BMI group in our study reflects the patient population that a surgeon is more likely to encounter.^59
-61 Therefore, using the overweight BMI category as the reference group may be the most useful information for surgeons to consider for this topic.

There were several limitations to our study. We utilized a large database to attempt to analyze these questions and there are well-described potential problems in utilizing this data set, such as coding bias and solely including in-hospital data.^62,63 In addition, this retrospective database had unknown interrupter reliability for coding and did not account for comorbidity, socioeconomic, or psychosocial factors, which may influence discharge destination.

Conclusion

This study found that, compared with the overweight BMI group, obese and morbidly obese patients had a significantly increased hospital length of stay and a greater risk of being discharged to a PAC facility after TAA. Men had a decreased risk of being discharged to a PAC facility and a shorter hospital length of stay when compared with women. Patients aged ≥65 years had an increased risk of being discharged to a PAC facility after TAA. There are both modifiable and non-modifiable risk factors for increased length of stay and the chances of being discharged to PAC facility after TAA that surgeons may want to consider preoperatively and discuss with their patients.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Ethical Approval

Ethical approval for this study was obtained from the Prisma Health Institutional Review Board [1954940].

Informed Consent

Not applicable, because this article does not contain any studies with human or animal subjects.

Trial Registration

Not applicable, because this article does not contain any clinical trials.

ORCID iDs

Yianni Bakaes

J.Benjamin Jackson

References

Morash

Walton

Glazebrook

Ankle arthrodesis versus total ankle arthroplasty. Foot Ankle Clin. 2017;22(2):251-266. doi:10.1016/j.fcl.2017.01.013

Thomas

Daniels

TR.

Ankle arthritis. J Bone Joint Surg Am. 2003;85(5):923-936.

Khlopas

Samuel

, et al Current concepts in osteoarthritis of the ankle: review. Surg Technol Int. 2019;35:280-294.

Coetzee

Raduan

McGaver

RS.

Converting ankle arthrodesis to a total ankle arthroplasty. Orthop Clin North Am. 2021;52(2):181-190. doi:10.1016/j.ocl.2020.12.005

Lawton

Butler

Dekker

2nd Prescott

Kadakia

AR.

Total ankle arthroplasty versus ankle arthrodesis: a comparison of outcomes over the last decade. J Orthop Surg Res. 2017;12(1):76. doi:10.1186/s13018-017-0576-1

McKenna

Cook

, et al Total ankle arthroplasty survivorship: a meta-analysis. J Foot Ankle Surg. 2020;59(5):1040-1048. doi:10.1053/j.jfas.2019.10.011

Park

Mroczek

KJ.

Total ankle arthroplasty. Bull NYU Hosp Jt Dis. 2011;69(1):27-35.

Shah

Umeda

Suriel Peguero

Erwin

Laughlin

Outcome reporting in total ankle arthroplasty: a systematic review. J Foot Ankle Surg. 2021;60(4):770-776. doi:10.1053/j.jfas.2021.02.003

Jeyaseelan

Si-Hyeong Park

Al-Rumaih

, et al Outcomes following total ankle arthroplasty: a review of the registry data and current literature. Orthop Clin North Am. 2019;50(4):539-548. doi:10.1016/j.ocl.2019.06.004

10.

Singh

Ramachandran

Time trends in total ankle arthroplasty in the USA: a study of the National Inpatient Sample. Clin Rheumatol. 2016;35(1):239-245. doi:10.1007/s10067-014-2703-2

11.

Alvi

Mednick

Krishnan

Kwasny

Beal

Manning

DW.

The effect of BMI on 30 day outcomes following total joint arthroplasty. J Arthroplasty. 2015;30(7):1113-1117. doi:10.1016/j.arth.2015.01.049

12.

Chee

Teoh

Sabnis

, et al Total hip replacement in morbidly obese patients with osteoarthritis: results of a prospectively matched study. J Bone Joint Surg Br. 2010;92(8):1066-1071.

13.

Finkielman

Gajic

Afessa

Underweight is independently associated with mortality in post-operative and non-operative patients admitted to the intensive care unit: a retrospective study. BMC Emerg Med. 2004;4(1):3. doi:10.1186/1471-227X-4-3

14.

Gebauer

Meyer

Ptok

, et al Impact of body mass index on early postoperative and long-term outcome after rectal cancer surgery. Visc Med. 2017;33(5):373-382. doi:10.1159/000479852

15.

Hussan

Gray

2nd Hinton

Krishna

Conwell

Stanich

PP.

Morbid obesity is associated with increased mortality, surgical complications, and incremental health care utilization in the peri-operative period of colorectal cancer surgery. World J Surg. 2016;40(4):987-994.

16.

Shen

Impact of being underweight on peri-operative and post-operative outcomes of total knee or hip arthroplasty: a meta-analysis. World J Clin Cases. 2022;10(30):10967-10983. doi:10.12998/wjcc.v10.i30.10967

17.

McCalden

Charron

MacDonald

, et al Does morbid obesity affect the outcome of total hip replacement? an analysis of 3290 THRs. J Bone Joint Surg Br. 2011;93(3):321-325.

18.

Memtsoudis

Besculides

Gaber

Liu

González Della Valle

Int Orthop Risk factors for pulmonary embolism after hip and knee arthroplasty: a population-based study. 2009;33(6):1739-1745.

19.

Migita

Takayama

Matsumoto

, et al Impact of being underweight on the long-term outcomes of patients with gastric cancer. Gastric Cancer. 2016;19(3):735-743. doi:10.1007/s10120-015-0531-y

20.

Wasserstein

Farlinger

Brull

, et al Advanced age, obesity and continuous femoral nerve blockade are independent risk factors for inpatient falls after primary total knee arthroplasty. J Arthroplasty. 2013;28(7):1121-1124.

21.

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

22.

Zalikha

Crespi

Tuluca

Zakaria

Hajj Hussein

El-Othmani

MM.

Underweight body mass index is associated with increased in-hospital complications and length of stay after revision total joint arthroplasty. J Am Acad Orthop Surg. 2022;30(20):984-991. doi:10.5435/JAAOS-D-22-00214

23.

Keswani

Tasi

Fields

Lovy

Moucha

Bozic

KJ.

Discharge destination after total joint arthroplasty: an analysis of Postdischarge outcomes, placement risk factors, and recent trends. J Arthroplasty. 2016;31(6):1155-1162.

24.

Keswani

Weiser

Shin

Lovy

Moucha

CS.

Discharge destination after revision total joint arthroplasty: an analysis of postdischarge outcomes and placement risk factors. J Arthroplasty. 2016;31(9):1866-1872.

25.

Strosberg

Housley

Vazquez

Rushing

Steinberg

Jones

Discharge destination and readmission rates in older trauma patients. J Surg Res. 2017;207:27-32. doi:10.1016/j.jss.2016.07.015

26.

Bovonratwet

Malpani

Ottesen

, et al Aseptic revision total hip arthroplasty in the elderly: quantifying the risks for patients over 80 years old. Bone Joint J. 2018;100-B(2):143-151. doi:10.1302/0301-620X.100B2.BJJ-2017-0895.R1

27.

Tornero

García-Oltra

García-Ramiro

, et al Prosthetic joint infections due to Staphylococcus aureus and coagulase-negative staphylococci. Int J Artif Organs. 2012;35(10):884-892. doi:10.5301/ ijao.5000148

28.

Trickey

Wright

Donovan

, et al Interrater reliability of hospital readmission evaluations for surgical patients. Am J Med Qual. 2017;32(2):201-207. doi:10.1177/1062860615623854

29.

Bohl

Ondeck

Darrith

Hannon

Fillingham

Della Valle

CJ.

Impact of operative time on adverse events following primary total joint arthroplasty. J Arthroplasty. 2018;33:2256-2262e4. doi:10.1016/j.arth.2018.02.037

30.

Surace

Sultan

George

, et al The association between operative time and short-term complications in total hip arthroplasty: an analysis of 89,802 surgeries. J Arthroplasty. 2019;34(3):426-432. doi:10.1016/j.arth.2018.11.015

31.

A healthy lifestyle: WHO recommendations. World Health Organization. Accessed 21 October, 2023. https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle—who-recommendations.

32.

Hardin

Hilbe

JM.

Generalized Linear Models and Extensions. 4th ed. College Station, TX: Stata Press; 2018.

33.

Singh

Cleveland

JD.

Age, race, comorbidity, and insurance payer type are associated with outcomes after total ankle arthroplasty. Clin Rheumatol. 2020;39(3):881-890. doi:10.1007/s10067-019-04826-5

34.

Singh

Ramachandran

Racial disparities in total ankle arthroplasty utilization and outcomes. Arthritis Res Ther. 2015;17(1):70. doi:10.1186/s13075-015-0589-2

35.

Palsis

Simpson

Matthews

Traven

Eichinger

Friedman

RJ.

Current trends in the use of shoulder arthroplasty in the United States. Orthopedics. 2018;41:e416-e423.

36.

Husted

Jørgensen

Gromov

Kehlet

; Lundbeck Foundation Center for Fast-track Hip and Knee Replacement Collaborative Group. Does BMI influence hospital stay and morbidity after fast-track hip and knee arthroplasty. Acta Orthop. 2016;87(5):466-472. doi:10.1080/17453674.2016.1203477

37.

Tornese

Robustelli

Ricci

Rancoita

PMV

Maffulli

Peretti

GM.

Predictors of postoperative hospital length of stay after total knee arthroplasty. Singapore Med J. 2021;65(2):68-73. doi:10.11622/smedj.2021142

38.

Zeidan

Goz

Lakomkin

Spina

Brodke

Spiker

WR.

Predictors of readmission and prolonged length of stay after cervical disc arthroplasty. Spine (Phila Pa. 1976). 2021;46(8):487-491. doi:10.1097/BRS.0000000000003839

39.

Zizza

Herring

Stevens

Popkin

BM.

Length of hospital stays among obese individuals. Am J Public Health. 2004;94(9):1587-1591. doi:10.2105/ajph.94.9.1587

40.

Kim

SH.

Morbid obesity and excessive hospital resource consumption for unilateral primary hip and knee arthroplasty. J Arthroplasty. 2010;25(8):1258-1266.

41.

Smith

Shaffrey

Glassman

, et al Risk-benefit assessment of surgery for adult scoliosis: an analysis based on patient age. Spine. 2011;36:817-824.

42.

Bozic

Wagie

Naessens

, et al Predictors of discharge to an inpatient extended care facility after total hip or knee arthroplasty. J Arthroplasty. 2006;21(6):151-156.

43.

Schwarzkopf

Quinn

Snir

Mukamel

Factors influencing discharge destination after total knee arthroplasty: a database analysis. Geriatr Orthop Surg Rehabil. 2016;7(2):95-99. doi:10.1177/2151458516645635

44.

Lao

Lees

Patel

White

Lawrenson

Length of hospital stay for osteoarthritic primary hip and knee replacement surgeries in New Zealand. Int J Environ Res Public Health. 2019;16(23):4789. doi:10.3390/ijerph16234789

45.

Saltzman

Basques

Leroux

, et al The influence of gender on early adverse events, hospital charges and length of stay after shoulder arthroplasty. Int Orthop. 2018;42(1):149-155. doi:10.1007/s00264-017-3547-6

46.

Acosta

Jr McClendon

Jr O’Shaughnessy

, et al Morbidity and mortality after spinal deformity surgery in patients 75 years and older: complications and predictive factors. J Neurosurg Spine. 2011;15(6):667-674.

47.

Daubs

Lenke

Cheh

Stobbs

Bridwell

KH.

Adult spinal deformity surgery: complications and outcomes in patients over age 60. Spine. 2007;32(20):2238-2244. Accessed 28 October, 2023. https://journals.lww.com/spinejournal/Fulltext/2007/09150/Adult_Spinal_Deformity_Surgery__Complications_and.15.aspx.

48.

Fehlings

Nakashima

Tetreault

, et al Does age affect surgical outcomes in patients with degenerative cervical myelopathy? Results from the prospective, multicenter AOSpine International Study on 479 patients. Proc 30Th Annu Meet North Am Spine Soc. 2015;15(10 suppl):S105-S106. Accessed 29 October, 2023 http://www.sciencedirect.com/science/article/pii/S152994301500741X.

49.

Smith

Sansur

Shaffrey

CI.

Standardized measures of health status and disability and the decision to pursue operative treatment in elderly patients with degenerative scoliosis. Neurosurgery. 2010;66(1):42-47.

50.

Hamel

Henderson

Khuri

Daley

Surgical outcomes for patients aged 80 and older: morbidity and mortality from major noncardiac surgery. J Am Geriatr Soc. 2005;53(3):424-429. doi:10.1111/j.1532-5415.2005.53159.x

51.

Passias

Kozanek

, et al Adult scoliosis in patients over sixty-five years of age: outcomes of operative versus nonoperative treatment at a minimum two-year follow-up. Spine. 2009;34:2165-2170.

52.

Gholson

Noiseux

Otero

Gao

Shah

AS.

Patient factors systematically influence hospital length of stay in common orthopaedic procedures. Iowa Orthop J. 2017;37:233-237.

53.

Makino

Takenaka

Sakai

Yoshikawa

Kaito

Factors related to length of hospital stay after two-stage corrective surgery for adult spinal deformity in elderly Japanese. J Orthop Sci. 2021;26(1):123-127. doi:10.1016/j.jos.2020.02.016

54.

Werner

Konetzka

RT.

Trends in post–acute care use among Medicare beneficiaries. JAMA. 2018;319(15):1616-1617. doi:10.1001/jama.2018.2408

55.

Healthcare Cost Utilization Project Tian W. An all-payer view of hospital discharge to post-acute care. 2013. Accessed 20 October, 2023. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.

56.

Chandra

Dalton

Holmes

Large increases in spending on postacute care in Medicare point to the potential for cost savings in these settings. Health Aff (Millwood). 2013;32(5):864-872. doi:10.1377/hlthaff.2012.1262

57.

Medicare Payment Advisory Commission. A data book: health care spending and the Medicare program. Accessed 24 October, 2023. http://www.medpac.gov/docs/default-source/data-book/jun17_databookentirereport_sec.pdf?sfvrsn=0.

58.

Clair

Evangelista

Lajam

Slover

Bosco

Iorio

Cost analysis of total joint arthroplasty readmissions in a bundled payment care improvement Initiative. J Arthroplasty. 2016;31:1862-1865.

59.

James

WP.

WHO recognition of the global obesity epidemic. Int J Obes (Lond). 2008;32(suppl 7):S120-S126.

60.

Butterworth

Landorf

Smith

Menz

HB.

The association between body mass index and musculoskeletal foot disorders: a systematic review. Obes Rev. 2012;13(7):630-642. doi:10.1111/j.1467-789X.2012.00996.x

61.

Felcher

Mularski

Mosen

Kimes

DeLoughery

Laxson

SE.

Incidence and risk factors for venous thromboembolic disease in podiatric surgery [Erratum in: Chest. 2009;136(2):653]. Chest. 2009;135(4):917-922. doi:10.1378/chest.08-1631

62.

Abdel-Kader

Eisenkraft

Katz

DJ.

Overview and limitations of database research in anesthesiology: a narrative review. Anesth Analg. 2021;132(4):1012-1022. doi:10.1213/ANE.0000000000005346

63.

Yoshihara

Yoneoka

Understanding the statistics and limitations of large database analyses. Spine (Phila Pa. 1976). 2014;39(16):1311-1312. doi:10.1097/BRS.0000000000000352

Body Mass Index,Sex,and Age Are Predictors of Discharge to a Post-acute Care Facility Following Total Ankle Arthroplasty

Abstract

Background

Methods

Results

Conclusion

Levels of Evidence:

Keywords

Introduction

Methods

Results

Discussion

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

Ethical Approval

Informed Consent

Trial Registration

ORCID iDs

References