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BMC Surgery

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Albumin and surgical site infection risk in orthopaedics: a meta-analysis

  • Peizhi Yuwen1,
  • Wei Chen1,
  • Hongzhi Lv1,
  • Chen Feng1,
  • Yansen Li1,
  • Tao Zhang1,
  • Pan Hu1,
  • Jialiang Guo1,
  • Ye Tian1,
  • Lei Liu1,
  • Jiayuan Sun1 and
  • Yingze Zhang1Email author
Contributed equally
BMC SurgeryBMC series – open, inclusive and trusted201717:7

https://doi.org/10.1186/s12893-016-0186-6

Received: 21 March 2016

Accepted: 30 September 2016

Published: 16 January 2017

Abstract

Backgroud

Surigical site infection has been a challenge for surgeons for many years, the prevalence of serum albumin <3.5g/dL has been reported to be associated with increased orthopaedic complications. However, the prognostic implications and significance of serum albumin <3.5g/dL after orthopaedic surgeries remain ambiguity. In this study, we performed a meta-analysis to access the predictive value of serum albumin level on SSI.

Methods

A basic data search was performed in PubMed and Web of Science, in addition, references were manually searched. All of the observational studies contained preoperative albumin, outcomes of SSI or valuable data that could be abstracted and analysed for meta-analysis in orthopaedics. All of the studies were assessed using the classic Newcastle Ottawa Scale (NOS). They conformed to critical quality evaluation standards, and the final data analysis was performed with RevMan 5.2 software.

Results

A total of 112,183 patients included in 13 studies were involved. The pooled MD of albumin between the infection group and the non-infection group was MD = −2.28 (95 % CI −3.97–0.58), which was statistically significant (z = 2.63, P = 0.008). The pooled RR of infection when comparing albumin <3.5 with albumin >3.5 was 2.39 (95 % CI 1.57 3.64), which was statistically significant (z = 4.06, P < 0.0001). Heterogeneity were found in the pooled MD of albumin and in the pooled RR for infection (P = 0.05, I2 = 61 % and P = 0.003, I2 = 68 %). No publication bias occurred based on two basically symmetrical funnel plots.

Conclusion

Our meta-analysis demonstrated that an albumin level <3.5 g/dL had an almost 2.5 fold increased risk of SSI in orthopaedics, although this conclusion requires well-designed prospective cohort studies to be confirmed further.

Background

The most common complication an orthopaedics patient can confront is surgical site infection (SSI). SSI has been a challenge for surgeons for many years, and the trends currently prefer the development of post-operation management to decrease the SSI rate. However, can SSI be predicted using less invasive or more tolerable tests? Recently, researchers have shown that malnutrition has links with serious complications in orthopaedics, but as many as 50 % of cases of pre-existing malnutrition are unrecognized in the hospital population [1, 2], with a reasonable explanation being that observable signs of malnutrition appear only in extreme cases. Because serum albumin has high sensitivity, it also can be used to determine and screen for nutritional status [2, 3]. Owoicho Adogwa et al. suggested that preoperative low albumin (serum albumin <3.5 g/dL) was an independent risk factor for postoperative SSI in spine fusion [4], Jason D. Walls et al. identified 49,475 total hip arthroplasty (THA) patients and found that low albumin was a significant risk factor for increased mortality and major morbidity in THA [5]. However, controversy exists as well, with Jiong Jiong Guo finding that ALB had only a weak relationship with delayed wound healing after hip fractures in the elderly [6]. M. Hedström demonstrated that preoperative serum albumin could not be used to predict postoperative deep wound infection [7]. Identical results regarding the relationship between low albumin and SSI from large systematic reviews have not yet been obtained; therefore, we aim to perform a meta-analysis to investigate whether low albumin is effective in predicting SSI and to estimate the relative infection rates in patients with normal albumin and low albumin.

Methods

Literature search

An online search was performed in two databases (Web of Science, PubMed) from 1970 to 2015. Selected references were manually reviewed. The main search terms were “albumin”, “risk” and “infection”. The search details in PubMed were as follows: (Etiology/Broad[filter]) and (“albumin”[tiab]) or (“hypoalbuminemia”[tiab]) and “infection”[tiab] and ((“spine”[tiab] or “hip”[tiab] or “knee”[tiab] or “shoulder” [tiab] or “joint” [tiab] or “fracture” [tiab] or “arthroplasty” [tiab] or “orthopaedics”[tiab]). In Web of Science, the details were as follows: (Ts = albumin or Ts = (hypoalbuminemia)) and Ts = infection and Ts = risk, refined by orthopaedics.

Exclusion and eligibility criteria

Studies were required to meet the following eligibility criteria: 1) studies regarding surgical site infections (SSI, superficial SSI, deep SSI, organ space SSI) in orthopaedics; 2) studies including infection and non-infection groups subdivided by serum albumin <3.5 g/dL and serum albumin >3.5 g/dL; 3) studies with sample sizes with a mean ± standard deviation of albumin between an infection group and a non-infection group; 4) cross-sectional studies, cohort studies, and cross-sectional, cohort studies containing assessable data or reported risk ration (RR) with 95 % confidence interval (CI) of infection risk between two groups or other cross-sectional studies, cohort studies, and cross-sectional, cohort studies containing assessable data; and 5) studies providing sufficient data to fulfil the contingency tables.

Non-English-language articles, case reports, reviews, duplicate papers with same results, and conference reports were excluded. Original articles without control groups were excluded, and articles with incomplete or unacceptable information were excluded.

Data collection

Standardized two-by-two contingency tables were used to record the following abstracted data: title, first author, country, publication year, research year, study type, average age of subjects, surgery type, patient number and the mean ± standard deviation of albumin in infection and non-infection groups; Relevant variables were carefully read and extracted from each study. Missing data were supplemented by contacting the corresponding authors.

All of the studies were assessed for quality evaluation standards using the classic Newcastle Ottawa Scale (NOS) [8].

Statistical analysis

The Cochrane Collaboration’s RevMan 5.2 software was used for the data analysis. Pooled mean difference (MD) with 95 % CIs for continuous variables and enumeration data for Pooled RR with 95 % CIs were calculated, and the Z test was performed to determine overall effects. If the heterogeneity between studies was statistically significant (I2 > 50 %), a random effects model was used for further sensitivity analysis; otherwise, a fixed effects model was used (I2 < 50 %).

Results

Results of literature search and evaluation of methodological quality

Two authors (Yuwen Peizhi and Chenwei) sorted and reviewed all of the titles and abstracts of the retrieved articles; 25 studies finally met the eligibility criteria. Each full text article was read by two reviewers, and 12 studies involving 112,183 patients were ultimately placed on the short list to complete the form. The quality of the articles was assessed according to the literature quality evaluation criteria (Fig. 1, Tables 1 and 2).
Fig. 1

Flow diagram showing selection of studies

Table 1

Characteristics of selected studies for dichotomous variable meta-analysis

First author, Year

Country

Study design

Age (year)

Year of operation

Time of Infection

Type of surgery

Type of infection

Infection group

No infection group

NOS

albumin <3.5

albumin >3.5

albumin <3.5

albumin >3.5

DM Masatu, 2010 [16]

Tanzania

Cohort

36.11 ± 14.38

3–12,2009

30 days

femoral fractures

SSI

7

2

18

73

7

Lan B.MC Phee, 1998 [9]

Australia

Cohort

infection 54 ± 13, primary 53 ± 17

11/1984–5/1995

not mentioned

spine metastases

SSI

9

5

47

9

6

Owoicho Adogwa, 2014 [4]

US

Cohort

53.8 ± 17.0

2011–2013

12 months

spine fusion surgery

deep

2

2

55

77

 

superficial

1

0

56

79

7

Charles L Nelson, 2015 [12]

US

Cohort

not mentioned

2006–2013

during hospitalization

TKA

superficial

20

228

1546

35,298

8

deep

6

42

1560

35,484

8

Organ space

7

54

1559

35,472

8

Carlos J. Lavernia, 1999 [11]

US

Cohort

64.6 ± 15.62

1/-1-31/12, 1997

during hospitalization

THA and TKA

deep

1

2

21

95

7

Klein, Jeffrey D, 1996 [15]

US

Cohort

45

1990–1992

3 Year

lumber decompression and fusion

SSI

9

2

20

83

7

Jason D. Walls, 2015 [5]

US

Cohort

not mentioned

2006–2013

30 days

THA

superficial

24

164

1098

22,952

7

deep

8

62

1114

23,054

7

Organ space

3

46

1119

23,070

7

Hiroyuki Hayashi, 2015 [19]

Japan

Cohort

53.8

4/2006–6/2013

not mentioned

spondylectomy

SSI

2

6

16

101

8

Daniel D. Bohl, 2015 [18]

US

Cohort

not mentioned

2011–2013

30 days

THA and TKA

SSI

45

457

1919

47,182

8

Table 2

Characteristics of selected studies for continuous variable meta-analysis

First author, Year

Country

Study design

Age (year)

Year of operation

Time of infection

Type of surgery

Infection group

No infection group

NOS

N

albumin

N

higher albumin

Lena Gunningberg, 2008 [20]

Sweden

Cohort

66.6 ± 10.1

9/2004–4/2005

30 days

Orthopaedic surgery Thoracic surgery

6

39.5 ± 1.0

88

42.2 ± 4.0

8

M. Hedstrom1, 1998 [7]

Sweden

Cohort

infection:82 ± 11; non-infection 81 ± 9

1993–1994

30 days

femoral neck fractures with two Olmed cancellous bone screws

13

38 ± 5

415

37 ± 4

7

George N. Guild MD, 2012 [17]

US

Cohort

not mentioned

1/2001–5/2007

30 days

orthopaedic trauma surgery

15

31.6 ± 3.6

49

35.2 ± 3.8

6

Lan B.MC Phee, 1998 [9]

Australia

Cohort

infection 54 ± 13, primary 53 ± 17

1/1984–5/1995

not mentioned

spine metastases

14

36.5 ± 5.2

56

39.7 ± 4.8

6

Main meta-analysis

Albumin difference between the infection and non-infection groups

Four studies reported available albumin data with relative higher heterogeneity (P = 0.05, I2 = 61 %). A random effects model was applied for meta-analysis, and the results showed that preoperative albumin was significantly lower in the infection group than in the non-infection group (OR = −2.28, 95 % CI [−3.97, −0.58], P =0.008) (Fig. 2).
Fig. 2

Forest plot of pooled albumin MD between albumin <3.5 mg/dL group and albumin ≥3.5 mg/dL group

SSI rate between the infection and non-infection groups

Nine studies (Lan B. MC Phee contain both Albumin difference and SSI rate) reported the incidence of SSI in both groups. In SSI group, the infection rate was 2.96 % (143/4837) in the albumin <3.5 g/dL group and 1.00 % (1070/106,641) in the albumin >3.5 g/dL group, (RR = 2.39, 95 % CI [1.57 3.64], which was statistically significant (Z = 4.06, p < 0.0001) in a random model (I2 = 68 %). In superficial SSI subgroup, the infection rate was 1.64 % (45/2745) in the albumin <3.5 g/dL group and 0.67 % (392/58,721) in the albumin >3.5 g/dL group, (RR = 2.46, 95 % CI [1.81 3.35], Z = 5.73, p < 0.00001 in a fixed model (I2 = 0 %). In deep SSI subgroup, the infection rate was 0.61 % (17/2767) in the albumin <3.5 g/dL group and 0.18 % (108/58,818) in the albumin >3.5 g/dL group, (RR = 2.62, 95 % CI [1.56 4.42], Z = 3.62, p = 0.0003) in a fixed model (I2 = 0 %). In organ space SSI subgroup, the infection rate was 0.37 % (10/2688) in the albumin <3.5 g/dL group and 0.17 % (100/58,642) in the albumin >3.5 g/dL group, (RR = 2.17, 95 % CI [1.13 4.15], Z = 2.34, p = 0.02 in a fixed model (I2 = 18 %) (Fig. 3).
Fig. 3

Forest plot of pooled OR of infection rate in albumin <3.5 mg/dL and albumin ≥3.5 mg/dL

Sensitivity analysis

Regarding the pooled MD of albumin between the infection group and the non-infection group was MD = −2.28 (95 % CI −3.97–0.58), which was statistically significant (z = 2.63, P = 0.008). Regarding the overall effect RR (95 % CI) of the difference in albumin, the SSI rates between the compared groups in a random model were 2.39 (95 % CI 1.57, 3.64) (z = 4.06, P < 0.001), superficial SSI, deep SSI and organ space SSI between the compared groups in the fixed model were 2.46 (95 % CI 1.81, 3.35), 2.62 (95 % CI 1.56, 4.42) and 2.17 (95 % CI 1.13, 4.15), respectively. All showed statistically significant (z = 5.73, P < 0.00001; z =3.62, P = 0.0003 and z =2.34, P = 0.02, respectively), the results were consistent between the random and fixed effects models, suggesting that all of the findings in our study were fundamentally reliable (Figs. 2 and 3).

Publication bias

The funnel plots of pooled MD in albumin levels between the infection and non-infection groups and in the incidence of SSI in the two groups were both basically symmetrical, demonstrating no significant publication bias (Figs. 4 and 5).
Fig. 4

Funnel plot for publication bias. The symmetrical panel suggested no publication bias for albumin MD meta-analysis

Fig. 5

Funnel plot for publication bias. The symmetrical panel suggested no publication bias for infection rate meta-analysis

Discussion

The meta-analysis indicated that an albumin <3.5 mg/dL had an almost 2.5fold increased risk of SSI in orthopaedics, and these outcomes were statistically significant (p < 0.05) and robust. Many factors have been indicated and proved to have effects on SSI; among these factors, malnutrition has stood out, and a broad array of serological laboratory values, such as a serum albumin <3.5 mg/dL, have presented a significantly increased risk of infection in spine metastases [9], spine fusion [4], joint arthroplasty [10] and hip fracture [5, 11]. Theoretically, our wound healing progress was fundamentally based on our own knowledge of the potential relationship between nutrition and SSI, which could help us forecast SSI or even through some potent treatment, maintain the patient’s nutritional status, which in turn could promote the body’s resistance to pathogenic bacteria, obtaining satisfactory clinical results.

Charles LN et al. reported that low serum albumin had a more dominant association with complications after TKA than obesity [12]. Carlos J. L et al. evaluated the standard preoperative laboratory tests of 119 patients and demonstrated that preoperative nutritional status was an excellent predictor as SSI, as well as controllable factors for postoperative complications in patients undergoing joint replacement surgery [11]. Dickhaut et al. showed that low serum albumin and a low lymphocyte count increased the risk of wound complications in ankle amputations [13]. A shoulder arthroplasty study referred to a general prevalence of malnutrition of 7.6 %, and TSA patients with a preoperative albumin <3.5 g/dL tended to experience greater morbidity after surgery than patients with albumin in the normal reference ranges [10].

We perceived some heterogeneity between the included studies, especially in the infection rate comparison. The most dominant manuscript contributing to the heterogeneity of SSI incidence was Lan B. MC Phee et al. (1998) [9], after removing this study from consideration, the heterogeneity became relatively lower (P = 0.18, I2 = 33 %), as determined by a fixed effects model. Other possible reasons for heterogeneity were that low albumin was not the only susceptibility factor for SSI, obesity, age, low total lymphocyte counts, transferrin and combinations of these factors could all exerted an impact on SSI [14], and there were inconsistent factors among these studies. The sources of heterogeneity of pooled MD with regard albumin consisted mainly of the study by M. Hedström1 (1998), which provided only medians and interquartile ranges of albumin. After removing this study, the adjusted heterogeneity was P = 0.75 (I2 = 0 %), as determined by a fixed referenced model, indicating very acceptable, low heterogeneity.

Despite the existing heterogeneity, we still found positive findings that low albumin was related to SSI and that albumin <3.5 g/dL could be seen as a risk factor for SSI in orthopaedics.

In our study, the incidence of SSI in orthopaedics of SSI comparison was 1.09 % (1213/111,478), the rate of superficial SSI was 6.8 % (4371/64,466), the rate of deep SSI was 0.20 % (125/61,585) and the rate of organ space SSI was 0.18 % (110/61,330). Incidence of SSI in low albumin group in each comparison were higher than the normal albumin group [4, 5, 7, 9, 11, 12, 1520]. Approximately 40 % of admitted adult patients were undernourished, in particular, 4.3 % of community-dwelling adults were in the same situation [2123]. Ozkalkanli MY also referred to similar rates of malnutrition and morbidity in orthopaedic surgery of 3.5 and 4.1 %, respectively [23]. Normally, SSI rates are generally higher in orthopaedics patients than in other types of surgeries due to various and serious types of trauma [16, 24]. A rate of SSI of 7.1 % in spine surgery was reported by Satoru Demura [25]. In spinal metastases, the rate of SSI has been reported to range from 6.8 to 20 % [18, 19, 26, 27]. Huang demonstrated higher acute infection rate in THA patients with low albumin [28]. A systematic review indicated the incidence of SSI after total hip arthroplasty ranged from 0.2 % before discharge to 1.1 % for the period up to and including 5 years postoperation [29]. While in TKA, the rates of superficial and prosthetic joint infections were 2.9 and 0.80 %, respectively [30]. These findings identified our consequences, also indicating the important and severe current situation in orthopaedics. Given that malnutrition contributes to inadequate and incomplete wound healing, it could also lead to more devastating outcomes; parameters such as serum albumin and TLC are easily obtained, stable, inexpensive and established biochemical markers of nutritional status [3133]. Therefore, we recommend thorough nutritional consultation for each hospitalized patient in orthopaedics.

There were several limitations to our meta-analysis. Firstly, heterogeneity existed due to small search range, relatively low-quality and fixed literature types. Secondly, information on potential confounding factors such as age, general health and co-morbidities were lacking for different aspects of analysis in included articles. Thirdly, SSI got many combined influencing factors even though low albumin can dramatically affects it [1, 3, 10, 13, 20, 21], interference between those factors was not identified.

Conclusion

Our meta-analysis found that albumin <3.5 g/dL had an almost 2.5 fold increased risk of SSI in orthopaedics. Prospective, multicentre studies should be performed to verify this conclusion.

Abbreviations

CI: 

Confidence interval

MD: 

Mean difference

NOS: 

Newcastle Ottawa Scale

RR: 

Relative risk

SD: 

Standard deviation

SSI: 

Surgical site infection

THA: 

Total hip arthroplasty

TKA: 

Total knee arthroplasty

TSA: 

Total shoulder arthroplasty

Declarations

Acknowledgements

The authors wish to thank Dr. Yong Shen, orthopedics, Yi Cui, head nurse, for their assistance and cooperation in this study.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 81401789).

Availability of data and materials

The dataset supporting the conclusions of this article is included within the article.

Authors’ contributions

YZ and PZ designed the study; PZ, WC did the most search work, and PZ, WC, HZL analyzed data and performed statistical analysis; PZ and WC drafted the manuscript; CF, YSL, TZ, PH gave specific suggestions about the writing, JLG, TY, JYS, LL made substantial assisted contributions to this study; YZ had primary responsibility for final content. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Orthopedic Surgery, the Third Hospital of Hebei Medical University

References

  1. Hill GL, Blackett RL, Pickford I, Burkinshaw L, Young GA, Warren JV, Schorah CJ, Morgan DB. Malnutrition in surgical patients. An unrecognised problem. Lancet (London, England). 1977;1(8013):689–92.View ArticleGoogle Scholar
  2. Jensen JE, Jensen TG, Smith TK, Johnston DA, Dudrick SJ. Nutrition in orthopaedic surgery. J Bone Joint Surg Am. 1982;64(9):1263–72 (Jensen JE Nutrition in orthopaedic surgery).View ArticlePubMedGoogle Scholar
  3. Del Savio GC, Zelicof SB, Wexler LM, Byrne DW, Reddy PD, Fish D, Ende KA. Preoperative nutritional status and outcome of elective total hip replacement. Clin Orthop Relat Res. 1996;326:153–61.View ArticleGoogle Scholar
  4. Adogwa O, Martin JR, Huang K, Verla T, Fatemi P, Thompson P, Cheng J, Kuchibhatla M, Lad SP, Bagley CA, et al. Preoperative serum albumin level as a predictor of postoperative complication after spine fusion. Spine. 2014;39(18):1513–9 (http://www.ncbi.nlm.nih.gov/pubmed/?term=Preoperative+serum+albumin+level+as+a+predictor+of+postoperative+complication+after+spine+fusion).View ArticlePubMedGoogle Scholar
  5. Walls JD, Abraham D, Nelson CL, Kamath AF, Elkassabany NM, Liu J. Hypoalbuminemia more than morbid obesity is an independent predictor of complications after total hip arthroplasty. J Arthroplast 2015. (http://www.ncbi.nlm.nih.gov/pubmed/?term=Hypoalbuminemia+More+Than+Morbid+Obesity+is+an+Independent+Predictor+of+Complications+After+Total+Hip+Arthroplasty)
  6. Guo JJ, Yang H, Qian H, Huang L, Guo Z, Tang T. The effects of different nutritional measurements on delayed wound healing after hip fracture in the elderly. J Surg Res. 2010;159(1):503–8.View ArticlePubMedGoogle Scholar
  7. Hedstrom M, Grondal L, Ortquist A, Dalen N, Ahl T. Serum albumin and deep infection in femoral neck fractures. A study of 437 cases followed for one year. Int Orthop. 1998;22(3):182–4 (http://www.ncbi.nlm.nih.gov/pubmed/?term=Serum+albumin+and+deep+infection+in+femoral+neck+fractures.+A+study+of+437+cases+followed+for+one+year.).View ArticlePubMedPubMed CentralGoogle Scholar
  8. Wells GA, D O’Connell BS, Peterson J, Welch V, Losos M, et al. The Newcastlee Ottawa Scale (NOS) for assessing the quality of non-randomized studies in meta-analysis. Ottawa Health Research Institute. Available from: URL:http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp [Accessed 07 May 2012].
  9. McPhee IB, Williams RP, Swanson CE. Factors influencing wound healing after surgery for metastatic disease of the spine. Spine. 1998;23(6):726–32. discussion 732–723(http://www.ncbi.nlm.nih.gov/pubmed/?term=Factors+influencing+wound+healing+after+surgery+for+metastatic+disease+of+the+spine).View ArticlePubMedGoogle Scholar
  10. Garcia GH, Fu MC, Dines DM, Craig EV, Gulotta LV. Malnutrition: a marker for increased complications, mortality, and length of stay after total shoulder arthroplasty. J Shoulder Elbow Surg. 2015. (http://www.ncbi.nlm.nih.gov/pubmed/?term=Malnutrition%3A+a+marker+for+increased+complications%2C+mortality%2C+and+length+of+stay+after+total+shoulder+arthroplasty)
  11. Lavernia CJ, Sierra RJ, Baerga L. Nutritional parameters and short term outcome in arthroplasty. J Am Coll Nutr. 1999;18(3):274–8 (http://www.tandfonline.com/doi/abs/10.1080/07315724.1999.10718863).
  12. Nelson CL, Elkassabany NM, Kamath AF, Liu J. Low albumin levels, more than morbid obesity, are associated with complications after TKA. Clin Orthop Relat Res. 2015;473(10):3163–72 (http://dx.doi.org/10.1007/s11999-015-4333-7).View ArticlePubMedPubMed CentralGoogle Scholar
  13. Dickhaut SC, DeLee JC, Page CP. Nutritional status: importance in predicting wound-healing after amputation. J Bone Joint Surg Am. 1984;66(1):71–5 (http://jbjs.org/content/66/1/71.abstract).
  14. Jones RE, Russell RD, Huo MH. Wound healing in total joint replacement. The Bone & Joint Journal. 2013;95-b(11 Suppl A):144–7 (http://www.bjj.boneandjoint.org.uk/content/95-B/11_Supple_A/144).View ArticleGoogle Scholar
  15. Klein JD, Hey LA, Yu CS, Klein BB, Coufal FJ, Young EP, Marshall LF, Garfin SR. Perioperative nutrition and postoperative complications in patients undergoing spinal surgery. Spine. 1996;21(22):2676–82 (http://journals.lww.com/spinejournal/Abstract/1996/11150/Perioperative_Nutrition_and_Postoperative.18.aspx).
  16. DM Masatu CM. Predictive values of serum nutritional indices for early postoperative wound infections in surgically treated closed femoral fractures. SA Orthopaedic Journal 2010:63–67. (http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S1681-150X2010000400012)
  17. Guild GN, Moore TJ, Barnes W, Hermann C. CD4 count is associated with postoperative infection in patients with orthopaedic trauma who are HIV positive. Clin Orthop Relat Res. 2012;470(5):1507–12 (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3314762/).View ArticlePubMedGoogle Scholar
  18. Bohl DD, Shen MR, Kayupov E, Della Valle CJ. Hypoalbuminemia independently predicts surgical site infection, pneumonia, length of stay, and readmission after total joint arthroplasty. J Arthroplasty. 2016;31(1):15–21 (https://www.ncbi.nlm.nih.gov/pubmed/26427941).View ArticlePubMedGoogle Scholar
  19. Hayashi H, Murakami H, Demura S, et al. Surgical site infection after total en bloc spondylectomy: risk factors and the preventive new technology. Spine. 2015;15(1):132–7 (http://www.thespinejournalonline.com/article/S1529-9430(14)00783-9/abstract).View ArticleGoogle Scholar
  20. Gunningberg L, Persson C, Akerfeldt T, et al. Pre- and postoperative nutritional status and predictors for surgical-wound infections in elective orthopaedic and thoracic patients. e-SPEN, the European e-Journal of Clinical Nutrition and Metabolism. 2008;3:e93ee101. http://e-spenjournal.org.marlin-prod.literatumonline.com/article/S1751-4991(08)00016-4/abstract.View ArticleGoogle Scholar
  21. Cross MB, Yi PH, Thomas CF, Garcia J, Della Valle CJ. Evaluation of malnutrition in orthopaedic surgery. J Am Acad Orthop Surg. 2014;22(3):193–9.View ArticlePubMedGoogle Scholar
  22. World Health Organization: WHO,Nutrition Experts Take Action on Malnutrition. http://www.who.int/nutrition/pressnote_action_on_malnutrition/en/.
  23. Ozkalkanli MY, Ozkalkanli DT, Katircioglu K, Savaci S. Comparison of tools for nutrition assessment and screening for predicting the development of complications in orthopedic surgery. Nutr Clin Pract. 2009;24(2):274–80. https://www.ncbi.nlm.nih.gov/pubmed/19321901.View ArticlePubMedGoogle Scholar
  24. Steinberg EL, Amar E, Sagy Y, Rath E, Kadar A, Sternheim A. The impact of serum albumin and serum protein levels on POSSUM score of patients with proximal femur fractures. Injury. 2014;45(12):1928–31.View ArticlePubMedGoogle Scholar
  25. Demura S, Kawahara N, Murakami H, Nambu K, Kato S, Yoshioka K, Okayama T, Tomita K. Surgical site infection in spinal metastasis: risk factors and countermeasures. Spine. 2009;34(6):635–9.View ArticlePubMedGoogle Scholar
  26. Xu R, Garc_es-Ambrossi GL, McGirt MJ, Witham TF, Wolinsky JP, Bydon A, Gokaslan ZL, Sciubba DM. Thoracic vertebrectomy and spinal reconstruction via anterior, posterior, or combined approaches: clinical outcomes in 91 consecutive patients with metastatic spinal tumors. J Neurosurg Spine. 2009;11:272–84.View ArticlePubMedGoogle Scholar
  27. Finkelstein JA, Zaveri G, Wai E, Vidmar M, Kreder H, Chow E. A population-based study of surgery for spinal metastases. Survival rates and complications. J Bone Joint Surg (Br). 2003;85:1045–50.View ArticleGoogle Scholar
  28. Huang R, Greenky M, Kerr GJ, Austin MS, Parvizi J. The effect of malnutrition on patients undergoing elective joint arthroplasty. J Arthroplast. 2013;28(8 Suppl):21–4 (http://www.arthroplastyjournal.org/article/S0883-5403(13)00497-X/abstract).View ArticleGoogle Scholar
  29. Urquhart DM, Hanna FS, Brennan SL, Wluka AE, Leder K, Cameron PA, et al. Incidence and risk factors for deep surgical site infection after primary total hip arthroplasty: a systematic review. J Arthroplasty. 2010;25(8):1216e22.View ArticleGoogle Scholar
  30. Jämsen E, Varonen M, Huhtala H, Lehto MU, Lumio J, Konttinen YT, et al. Incidence of prosthetic joint infections after primary knee arthroplasty. J Arthroplasty. 2010;25(1):87e92.View ArticleGoogle Scholar
  31. Di Monaco M, Di Monaco R, Manca M, Cavanna A. Positive association between total lymphocyte count and femur bone mineral density in hip-fractured women. Gerontology. 2002;48(3):157–61.View ArticlePubMedGoogle Scholar
  32. Formiga F, Chivite D, Mascaro J, Ramon JM, Pujol R. No correlation between mini-nutritional assessment (short form) scale and clinical outcomes in 73 elderly patients admitted for hip fracture. Aging Clin Exp Res. 2005;17(4):343–6.View ArticlePubMedGoogle Scholar
  33. Reuben DB, Ix JH, Greendale GA, Seeman TE. The predictive value of combined hypoalbuminemia and hypocholesterolemia in high functioning community-dwelling older persons: MacArthur studies of successful aging. J Am Geriatr Soc. 1999;47(4):402–6.View ArticlePubMedGoogle Scholar

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