Skip to main content
Download PDF

Inflammatory blood parameters as prognostic factors for implant-associated infection after primary total hip or knee arthroplasty: a systematic review

BMC Musculoskeletal Disorders volume 24, Article number: 383 (2023) Cite this article

Abstract

Background

Implant-associated infection (IAI) is a potential complication following total hip (THA) or knee arthroplasty (TKA). The initial phase of the inflammatory process can be measured by applying one of the inflammatory blood parameters (IBP). This systematic review aims to assess the response of IBP to trauma caused by orthopedic surgery and evaluate the clinical utility of quantitative measurements of IBP as prognostic factors for infection.

Methods

All studies indexed in Ovid MEDLINE (PubMed), Ovid EMBASE, the Cochrane Library and the ISI Web of Science databases, from inception until January 31, 2020, were analyzed. Studies included were those on adults who underwent THA or TKA with minimum follow up of 30 days after surgery. In addition to minimum follow up, data on the prognostic factors for pre- or post-THA/TKA IAI were mandatory. The Quality Assessment of Diagnostic Accuracy tool (version 2) (QUADAS-2) and Standards for Reporting of Diagnostic Accuracy Studies guideline 2015 (STARD) were used for quality assessment.

Results

Twelve studies fulfilled the inclusion and exclusion criteria. C-reactive protein was analyzed in seven studies, interleukin-6 in two studies and erythrocyte sedimentation rate in eight studies. White blood cell count and procalcitonin were analyzed in the only study. The overall quality of included studies was low. A potential for other cytokines (IL-1ra, IL-8) or MCP-1 was observed.

Conclusions

This is the first systematic review of IBP response to orthopedic surgery which identified some IBP for pre/post-operative screening, despite insufficient data supporting their prognostic potential for patient risk stratification.

Peer Review reports

Introduction

Although joint replacement is a successful treatment for osteoarthritis, there is some risk of potential complications, namely implant-associated infection (IAI) [1,2,3,4].

The IAI occurrence may be effectively reduced by various preoperative and postoperative measures [5, 6]. Therefore, these measures must be individualized for each patient by applying various predictive tools. Although predictive tools, such as the ACS (American College of Surgeons) surgical risk calculator or the NHSN (National Healthcare Safety Network) risk index are available, none of them estimates the risk of an early infection based on inflammatory blood parameters (IBP) [7, 8].

The beginning of the inflammatory process can be measured by applying one of the IBP, including interleukin-6 (IL-6), c-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), white blood cell count (WBC), erythrocyte sedimentation rate (ESR), and procalcitonin (PCT) [9,10,11,12,13].

The present systematic review aimed to identify studies that evaluated the IBP in patients with primary total hip arthroplasty (THA) and total knee arthroplasty (TKA). By analyzing the preoperative and postoperative values of IBP, clinicians would be able to identify patients at a higher risk for IAI and thus tailor their preoperative and postoperative measurement accordingly.

Materials and methods

The systematic review was conducted following the PRISMA guidelines [14]. A protocol of this review was registered in the international prospective register for systematic reviews (PROSPERO, number: CRD42020147925) and published before the completion of this systematic review [15].

Search strategy and study selection

All studies indexed in Ovid MEDLINE (PubMed), Ovid EMBASE, the Cochrane Library and ISI Web of Science databases from 1902 or inception until January 31, 2020, were analyzed. The search terms included both medical subject headings (MeSH) and keywords related to: IBP or risk factors, prosthesis-related infection or surgical site infection, and knee or hip arthroplasty. The complete search strategy and study selection process were published within the systematic review protocol [15].

Data extraction

A standardized form was used to extract data from the studies for the quality assessment study and evidence synthesis. The detailed procedure was conducted as published in Domecky et al. 2021 [15].

Quality assessment

The risk of bias assessment was conducted by two authors in duplicate (PD, ARP), using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) [16]. The Standards for Reporting of Diagnostic Accuracy Studies guideline 2015 (STARD) [17] was used to assess a list of essential items to ensure that the report of diagnostic accuracy study contains all the necessary information. The Cochrane Review Manager (RevMan, version 5.4.1; The Cohrane Collaboration, 2020) was used to extract the risk of bias assessment. Details of quality assessment are available in the published protocol [15].

Data synthesis and analysis

Initially, this systematic review was conducted as a systematic review of diagnostic test accuracy studies. Despite the lack of data to calculate standardized effect sizes and high heterogeneity levels in the included studies, a narrative synthesis of findings was conducted. The detailed data synthesis and the analysis process were described in a published protocol [15].

Statistical analysis

As for the baseline characteristics, there was no substantial clinical homogeneity observed in the studies regarding the participants and assessment methods. Therefore, meta-analysis was not conducted. For data extraction and quality assessment Microsoft Excel (Version 13,801.20864; Microsoft Inc, Redmond, WA, USA) was used. Furthermore, qualitative description was used to summarize the evidence.

Subgroup analysis

The assessment suggested significant heterogeneity, therefore a subgroup analysis based on study-level characteristics was performed. This includes: diagnosis of the knee or hip osteoarthritis, type of surgery, use of antibiotic prophylaxis, average duration of follow-up, exact time of performed blood tests, and laboratory analysis.

Results

Study search and study characteristics

Systematic research identified 4,068 articles. The details of the selection are shown in the PRISMA flow chart (Fig. 1). Nevertheless, only 12 studies met the entry criteria. Baseline characteristics of the included studies are presented in Table 1.

Fig. 1
figure 1

The PRISMA flow chart. This flow diagram demonstrates the search strategy and exclusion or inclusion criteria. Criteria included english language, year: from 1902 to January 31, 2020, primary arthroplasty, inflammatory blood parameters and preoperative and/or postoperative measure of these parameters, and minimum follow-up for of 30 days. All results that not included complications, prognostic factors (inflammatory blood parameters), short follow-up, no preoperative measurement were excluded. For more information see previously published protocol [15]

Full size image
Table 1 Baseline characteristics of the included studies
Full size table

Inflammatory blood parameters

No eligible studies were found for NLR to estimate its prognostic value for IAI after primary THA or TKA. General information about the parameters is available in Table 2.

Table 2 General information about inflammatory blood parameters
Full size table

C-reactive protein [19, 22,23,24,25, 27, 29]

One study found that CRP is more accurate than X-rays in predicting late chronic and early postoperative infections [22]. Although specific cut-off values for CRP have not been determined, other studies suggest that increased preoperative levels of 5 mg/l or higher can be a reliable predictor of implant-associated infection (IAI) and septic revision [23, 27]. In some cases, postoperative CRP levels can also predict the likelihood of complications. For example, one study found that postoperative CRP levels under 6 mg/l on the fifth day after surgery can predict an uncomplicated recovery. However, another study identified the seventh day as an indicator of infection [24]. Interestingly, CRP behaves similarly to interleukin-6 (IL-6) but differently from the erythrocyte sedimentation rate (ESR) [22]. Despite the potential benefits of using CRP to predict infections, some studies suggest that it may not be a reliable diagnostic marker for IAI [19, 29]. In fact, there's a risk of missed infections due to its relatively low sensitivity [25].

Interleukin-6 [22, 26, 29]

Mumingjiang et al. 2014 [22] found that IL-6 had similar kinetics to CRP, with levels almost 7 times higher in patients with late IAI compared to those without infection. However, preoperative levels did not appear to be a reliable predictor of infection risk. In the study by Zarghooni et al. 2019 [29], patients with IAI had significantly higher levels of IL-6, particularly within the first 2 days postoperatively, with a 5.7-fold increase compared to controls. Unfortunately, only IL-1ra and IL-8 were found to be suitable as prognostic cytokines before surgery. Shah et al. 2009 [26] also found that IL-6 was a key marker for infection, with levels 3 times higher in patients with a proven deep infection compared to the control group. In contrast, levels of monocyte chemoattractant protein-1 (MCP-1) were lower in the infected group. The combination of increased IL-6 at 6 h and reduced MCP-1 at 48 h was associated with infection. These studies suggest that monitoring IL-6 levels may be a more effective method for predicting infections after joint replacement surgery compared to CRP. By understanding the relationship between cytokine levels and infection risk, healthcare providers can take appropriate measures to prevent further complications.

Erythrocyte sedimentation rate [18,19,20,21,22,23,24, 28]

According to Sanzén et al. 1997 [24], ESR could be a more reliable marker than CRP in cases of chronic low-grade IAI, as some patients showed increased ESR with normal CRP values. Similarly, Carlsson et al. 1978 [18] found that ESR levels of 40 mm/hr or higher indicated deep infection more than three months postoperatively, even in the absence of symptoms or radiographic signs of infection. Mulier et al. 1973 [21] observed that hip replacements were associated with long-term elevated ESRs, which eventually normalized within the first four months after operation. Forster et al. 1982 [20] compared ESR levels in uncomplicated THA with those in IAI and found significantly higher ESR levels in patients with IAI. In case of uncomplicated surgery, ESRs fell to 20 mm/hr within six months and the higher rate after surgery did not imply infection, especially when participants presented a high preoperative level. In the study by Wroblewski et al. 1974 [28], the ESR varied from 30 mm/hr to 140 mm/hr in cases of sepsis after arthroplasty. Preoperative high ESR levels in this study did not predict IAI. Falzarano et al. 2017 [19] concluded that there are two circumstances when the ESR can be a better IBP than CRP or PCT: first, some low-grade bone infections and second, joint infections, both due to the low-level pathogens. In the study by Okafor et al. 1998 [23], there was a significant difference in ESR levels between the normal and the infected group from the second postoperative day (71 vs 91,6 mm/hr) until day 21 (36.5 vs 71.2 mm/hr). The authors thus suggested repeated blood tests between day 7 and 21 after operation to detect early infection. Mumingjiang et al. 2014 [22] observed that ESRs increased and peaked 7 days after surgery and returned to baseline levels within 3 months. There was a significant difference when comparing the uninfected group’s ESR levels to the infected group’s ESR levels at six months following surgery (10.6 vs 42.6 mm/hr).

White blood cell count and procalcitonin [19, 21]

Both WBC and PCT were separately analyzed in only one of the included studies. Therefore, there is no additional benefit in the narrative synthesis for these IBP.

Risk of bias

The risk of bias assessment is shown in Fig. 2. The overall extracted data suggested that not all the included studies were conducted according to the STARD 2015 guidelines [17]. Scores of each included study are shown in Table 3.

Fig. 2
figure 2

Risk of bias and applicability concerns summary (QUADAS-2). This figure demonstrates the risk of bias and applicability concerns summary (QUADAS-2). The overall extracted data suggested that not all the included studies were conducted according to the STARD 2015 guidelines [16, 17]

Full size image
Table 3 Standards for reporting diagnostic accuracy checklist
Full size table

Discussion

To our knowledge, this is the first systematic review focused on IBP response to trauma induced by orthopedic surgery and evaluation of potential clinical usefulness of IBP as predictive factors for implant-associated infections in patients who are at higher risk of developing IAI before undergoing surgery. All the literature eligible according to the inclusion and exclusion criteria was included, thus obtaining 12 studies in total. After data extraction and summarizing all the evidence, no quantitative analysis – including the diagnostic accuracy statistics – could be performed. The included studies were inadequate as they lacked data related to QUADAS-2, or not respecting STARD criteria [16, 17]. Moreover, there were neither enough data nor sufficient non-conflicting evidence to support any prognostic potential of the analyzed IBPs for patient risk stratification and to suggest its routine use to predict IAI in relation to preoperative and postoperative levels of the analyzed IBP. At the same time, no eligible studies were found for NLR after primary THA or TKA.

After a total joint arthroplasty, IL-6 serum levels may increase to 30–430 pg/mL for up to three days before returning to normal levels, if the surgery is uncomplicated [10]. Zarghooni et al. 2019 [29] studied other cytokines and MCPs to determine their potential as prognostic factors in addition to IL-6. They found that IL-1ra and IL-8 showed the most convincing evidence, but IL-6 was confirmed only for diagnostic purposes. Combining IL-6 with MCP-1 may show a specific trend in predicting IAI, but its pharmacokinetics varies depending on tissue damage [10, 22, 26, 29].

The hepatocytes produce CRP, an acute-phase protein, in response to inflammation, infection, and neoplasm. While CRP is not 100% sensitive and low-grade or encapsulated infections may result in less intensive systemic reactions, healthy patients without inflammation, infection, or neoplasm typically exhibit low serum concentrations of CRP. Following surgery, CRP levels peak within two to three days and return to normal approximately three to eight weeks after surgery, making it a recommended marker for monitoring the postoperative course. Unfortunately, there is significant inter-individual variation in CRP levels during the first week after surgery across studies, although subsequent decreases follow a similar pattern [30,31,32,33]. According to the included studies, CRP levels can predict an uncomplicated postoperative course. The potential prediction for early or later infection remains unknown. Therefore, it is not appropriate to predict postoperative complications based on this parameter [19, 23, 24, 27].

The relative lack of specificity limits the ESR usage especially in patients suffering from undergoing inflammatory joint disease such as rheumatoid arthritis [22]. However, the ESR may potentially be used in cases where the normal assay of CRP was found, particularly in some chronic low-grade IAI [24]. Also, in cases of delayed infection since the increased levels due to its kinetics remain high for approximately three to twelve months [18]. An exciting feature of the ESR is that even if it remains elevated for a long time after surgery because of infection, the ESR quickly drops to preoperative levels after the infection is healed [18,19,20,21,22,23,24, 28].

PCT detection is useful for patients with sepsis due to its properties. However, it can also increase in other inflammatory conditions such as major surgery, including primary THA and TKA. After surgery, PCT levels peak on the first day and quickly return to pre-intervention levels within 6 to 14 days. Unfortunately, there is insufficient precise information about PCT induction in relation to postoperative conditions. Typically, the increase in PCT concentration within the first day after surgery does not surpass levels associated with bacterial infections [30, 34,35,36].

A sensitivity of 45% limits the usage of WBC. Although the higher specificity of 87% may be helpful in some specific situations, peak levels are usually observed within 2–3 days after surgery [37].

The diagnostic criteria of IAI have been changing over time as knowledge and evidence have progressed. In 2013, the Infection Diseases Society of America (IDSA) modified the definition [38]. In addition to MSIS and IDSA criteria, the International Consensus Meeting (ICM) in 2013 adapted a definition from Parvizi et al. 2013 [39, 40]. The MSIS, IDSA and ICM criteria are widely used for IAI diagnosis [38, 40]. Unfortunately, an accurate determination can still not be made using the current diagnostic criteria (especially the ICM and MSIS criteria) [41]. Exactly for that reason a new definition has been proposed establishing an evidence-based and weight-adjusted scoring system in 2018 [42].

The evidence-based stepwise algorithm for an IAI diagnosis is established according to major criteria (i.e., the microbiological confirmation of positive cultures, a sinus tract with evidence of communication to the joint, or visualization of the prosthesis), as well as to minor criteria (synovial inflammatory parameters or IBP), also based on intraoperative diagnoses. Each of the criteria is individually scored to reach the final decision [42]. Since the studies included in our study were conducted in different periods of time, all the facts discussed above resulted in discrepancies among the reference standards. Therefore, various criteria for reference standards as’IAI diagnoses’ were used in the included studies.

The strength of the present study stems from a thorough search in all of the essential databases and the review of the relevant literature; at least two independent reviewers collecting data, including a third reviewer to judge disagreements and assess the risk of bias; the quality of the included studies according to the QUADAS-2, the STARD Guideline 2015; and finally, the appropriate set of eligibility criteria [16, 17].

Nevertheless, the present study has several limitations. Except for the study done by Windisch et al. 2016 [27], data were limited because the included studies were found lacking in diagnostic accuracy statistics, including specificity, sensitivity, and area under the receiver operating characteristics curve. Therefore, no quantitative synthesis, or its associated components, were performed. On the other hand, due to the prospective nature of most of these studies, there was a fair amount of withdrawal. Preoperative and postoperative measurements were differently applied across the included studies, resulting in a potential selection bias and a possible exaggeration of the outcome. Notably, no strict case definition for IAI was used through the studies as the reference standard as’IAI diagnoses’ evolved over the time. This could have led to a classification bias between the infected and non-infected patients regarding each study reference standards. Finally, variation was observed among cut-off values of the index tests used in the included studies.

Conclusion

This systematic review showed that there are insufficient data available to support any prognostic potential of the analyzed IBP for patient risk stratification based on these IBP only. CRP had better diagnostic accuracy than X-rays in predicting late chronic and early postoperative infections. However, no specific cut-off values were detected. IL-6 was found to be a suitable marker for predicting IAI, while its kinetics were found to be the same as CRP kinetics in an uncomplicated postoperative course. ESR was found to be a better indicator for some low-grade bone infections and joint infections. Nevertheless, there is also a potential for other cytokines (IL-1ra, IL-8) or MCP-1 for preoperative and postoperative routine screening. Still, the heterogeneity of the included studies suggested a need for further research to establish reliable cutoff values for the IBPs.

Availability of data and materials

All data generated or analysed during this study are included in this published article.

References

  1. Fu K, Robbins SR, McDougall JJ. Osteoarthritis: the genesis of pain. Rheumatology (Oxford). 2018;57(suppl_4):iv43–50.

    Article  CAS  PubMed  Google Scholar 

  2. Grayson CW, Decker RC. Total joint arthroplasty for persons with osteoarthritis. PM R. 2012;4(5 Suppl):S97-103.

    Article  PubMed  Google Scholar 

  3. Hunter D, Pietro-Alhambra D, Arden N. Osteoarthritis: the facts. 2nd ed. Oxford: Oxford University Press; 2014.

    Google Scholar 

  4. Loeser RF, Collins JA, Diekman BO. Ageing and the pathogenesis of osteoarthritis. Nat Rev Rheumatol. 2016;12(7):412–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bratzler DW, Dellinger EP, Olsen KM, Perl TM, Auwaerter PG, Bolon MK, Fish ND, Napolitano LM, Sawyer RG, Slain D, Steinberg JP, Weinstein RA. Clinical practice guidelines for antimicrobial prophylaxis in surgery. Am J Health-Syst Pharm. 2013;70:195–283.

    Article  CAS  PubMed  Google Scholar 

  6. Korol E, Johnston K, Waser N, Sifakis F, Jafri HS, Lo M, Kyaw MH. A systematic review of risk factors associated with surgical site infections among surgical patients. PLoS One. 2013;8(12):e83743. https://doi.org/10.1371/journal.pone.0083743.

  7. ACS NSQIP Surgical Risk Calculator. In: ACS. Accessed 02 23 2023. Available from: https://riskcalculator.facs.org/RiskCalculator/index.jsp.

  8. Culver DH, Horan TC, Gaynes RP, Martone WJ, Jarvis WR, Emori TG, Banerjee SN, Edwards JR, Tolson JS, Henderson TS, et al. Surgical wound infection rates by wound class, operative procedure, and patient risk index. National Nosocomial Infections Surveillance System. Am J Med. 1991;91(3B):152S-157S.

    Article  CAS  PubMed  Google Scholar 

  9. Bhat T, Teli S, Rijal J, Bhat H, Raza M, Khoueiry G, Meghani M, Akhtar M, Costantino T. Neutrophil to lymphocyte ratio and cardiovascular diseases: a review. Expert Rev Cardiovasc Ther. 2013;11(1):55–9.

    Article  CAS  PubMed  Google Scholar 

  10. Majors I, Jagadale VS. Serum interleukin 6 could be a valuable initial diagnostic tool in prosthetic knee joint infections. Eur J Orthop Surg Traumatol. 2019;29(8):1781–8.

    Article  PubMed  Google Scholar 

  11. Daryapeyma A, Aarstad HJ, Wahlgren CM, Jonung T. Perioperative cytokine response to infection associated with elective arterial surgery. Vasc Endovascular Surg. 2014;48(2):116–22.

    Article  PubMed  Google Scholar 

  12. Germolec DR, Shipkowski KA, Frawley RP, Evans E. Markers of Inflammation. Methods Mol Biol. 1803;2018:57–79.

    Google Scholar 

  13. Lowsby R, Gomes C, Jarman I, Lisboa P, Nee PA, Vardhan M, Eckersley T, Saleh R, Mills H. Neutrophil to lymphocyte count ratio as an early indicator of blood stream infection in the emergency department. Emerg Med J. 2015;32(7):531–4. https://doi.org/10.1136/emermed-2014-204071. (Epub 2014 Sep 2 PubMed PMID: 25183249).

    Article  PubMed  Google Scholar 

  14. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA Statement. BMJ. 2009;2009(339): b2535.

    Article  Google Scholar 

  15. Domecky P, RejmanPatkova A, Mala-Ladova K, Maly J. Inflammatory blood parameters as prognostic factors for surgical site infection after primary hip or knee arthroplasty: a systematic review protocol. BMJ Open. 2021;11(9): e046027.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM, QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36.

    Article  PubMed  Google Scholar 

  17. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, de Vet HC, Kressel HY, Rifai N, Golub RM, Altman DG, Hooft L, Korevaar DA, Cohen JF, STARD Group. Stard 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ. 2015;351:h5527.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Carlsson AS. Erythrocyte sedimentation rate in infected and non-infected total-hip arthroplasties. Acta Orthop Scand. 1978;49(3):287–90.

    Article  CAS  PubMed  Google Scholar 

  19. Falzarano G, Piscopo A, Grubor P, Rollo G, Medici A, Pipola V, Bisaccia M, Caraffa A, Barron EM, Nobile F, Cioffi R, Meccariello L. Use of common inflammatory markers in the long-term screening of total hip arthroprosthesis infections: our experience. Adv Orthop. 2017;2017:9679470.

    PubMed  PubMed Central  Google Scholar 

  20. Forster IW, Crawford R. Sedimentation rate in infected and uninfected total hip arthroplasty. Clin Orthop Relat Res. 1982;168:48–52.

    Article  Google Scholar 

  21. Mulier JC, Desmet L, Martens M, Hoogmartens M. Postoperative infection in total hip replacement. Acta Orthop Belg. 1973;39(3):630–43.

    CAS  PubMed  Google Scholar 

  22. Mumingjiang Y, Zhou X, He R. Value of knee skin temperature measured by infrared thermography and soluble intercellular adhesion molecule-1 in the diagnosis of peri-prosthetic knee infection in Chinese individuals following total knee arthroplasty. Chin Med J (Engl). 2014;127(17):3105–9 (PMID: 25189954).

    PubMed  Google Scholar 

  23. Okafor B, MacLellan G. Postoperative changes of erythrocyte sedimentation rate, plasma viscosity and C-reactive protein levels after hip surgery. Acta Orthop Belg. 1998;64(1):52–6.

    CAS  PubMed  Google Scholar 

  24. Sanzén L, Sundberg M. Periprosthetic low-grade hip infections. Erythrocyte sedimentation rate and C-reactive protein in 23 cases. Acta Orthop Scand. 1997;68(5):461–5.

    Article  PubMed  Google Scholar 

  25. Sastre S, Soriano À, Garcia S, et al. Serum C-reactive protein as predictor of infected arthroplasty. Eur J Orthop Surg Traumatol. 2006;16:17–9.

    Article  Google Scholar 

  26. Shah K, Mohammed A, Patil S, McFadyen A, Meek RM. Circulating cytokines after hip and knee arthroplasty: a preliminary study. Clin Orthop Relat Res. 2009;467(4):946–51.

    Article  PubMed  Google Scholar 

  27. Windisch C, Brodt S, Roehner E, Matziolis G. The C-reactive protein level after total knee arthroplasty is gender specific. Knee Surg Sports Traumatol Arthrosc. 2016;24(10):3163–7.

    Article  PubMed  Google Scholar 

  28. Wroblewski BM. Erythrocyte sedimentation rate and polymethylmethacrylate. J R Coll Surg Edinb. 1974;19(3):182.

    CAS  PubMed  Google Scholar 

  29. Zarghooni K, Hackenberg RK, Sander G, Mahabir E. Suitability of serum cytokine profiling for early diagnosis of implant-associated infections after orthopaedic surgery: a preliminary prospective study. Cytokine. 2019;116:88–96.

    Article  CAS  PubMed  Google Scholar 

  30. Battistelli S, Fortina M, Carta S, Guerranti R, Nobile F, Ferrata P. Serum C-reactive protein and procalcitonin kinetics in patients undergoing elective total hip arthroplasty. Biomed Res Int. 2014;2014: 565080.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Pérez-Prieto D, Portillo ME, Puig-Verdié L, Alier A, Martínez S, Sorlí L, Horcajada JP, Monllau JC. C-reactive protein may misdiagnose prosthetic joint infections, particularly chronic and low-grade infections. Int Orthop. 2017;41(7):1315–9. https://doi.org/10.1007/s00264-017-3430-5. (Epub 2017 Mar 21 PMID: 28321490).

    Article  PubMed  Google Scholar 

  32. Sproston NR, Ashworth JJ. Role of C-Reactive Protein at Sites of Inflammation and Infection. Front Immunol. 2018;9:754.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Qin L, Li F, Gong X, Wang J, Huang W, Hu N. Combined Measurement of D-Dimer and C-Reactive protein levels: highly accurate for diagnosing chronic Periprosthetic joint infection. J Arthroplasty. 2020;35(1):229–34.

    Article  PubMed  Google Scholar 

  34. Yuan K, Li WD, Qiang Y, Cui ZM. Comparison of procalcitonin and C-reactive protein for the diagnosis of periprosthetic joint infection before revision total hip arthroplasty. Surg Infect (Larchmt). 2015;16(2):146–50.

    Article  PubMed  Google Scholar 

  35. Liu D, Su L, Han G, Yan P, Xie L. Prognostic value of Procalcitonin in adult patients with sepsis: a systematic review and meta-analysis. PLoS One. 2015;10(6): e0129450.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Meisner M. Update on procalcitonin measurements. Ann Lab Med. 2014;34(4):263–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kang KT, Son DW, Lee SH, Song GS, Sung SK, Lee SW. Variation of C-Reactive protein and white blood cell counts in spinal operation: primary fusion surgery versus revision fusion surgery. Korean J Spine. 2017;14(3):66–70.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Osmon DR, Berbari EF, Berendt AR, Lew D, Zimmerli W, Steckelberg JM, Rao N, Hanssen A, Wilson WR, Infectious Diseases Society of America. Diagnosis and management of prosthetic joint infection: clinical practice guidelines by the Infectious Diseases Society of America. Clin Infect Dis. 2013;56(1):e1–25.

    Article  PubMed  Google Scholar 

  39. Parvizi J, Gehrke T, International Consensus Group on Periprosthetic Joint Infection. Definition of periprosthetic joint infection. J Arthroplasty. 2014;29(7):1331.

    Article  PubMed  Google Scholar 

  40. Parvizi J, Gehrke T, Chen AF. Proceedings of the International Consensus on Periprosthetic Joint Infection. Bone Joint J. 2013;95-B(11):1450–2.

    Article  CAS  PubMed  Google Scholar 

  41. Koh IJ, Cho WS, Choi NY, Parvizi J, Kim TK, Korea Knee Research Group. How accurate are orthopedic surgeons in diagnosing periprosthetic joint infection after total knee arthroplasty?: a multicenter study. Knee. 2015;22(3):180–5.

    Article  PubMed  Google Scholar 

  42. Parvizi J, Tan TL, Goswami K, Higuera C, Della Valle C, Chen AF, Shohat N. The 2018 definition of periprosthetic hip and knee infection: an evidence-based and validated criteria. J Arthroplasty. 2018;33(5):1309-1314.e2.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

PD as a PhD student is supported by Charles University (Project SVV 260 551).

Author information

Authors and Affiliations

  1. Department of Social and Clinical Pharmacy, Faculty of Pharmacy in Hradec Kralove, Charles University, Ak. Heyrovskeho 1203/8, 500 05, Hradec Kralove, Czech Republic

    Petr Domecky, Anna Rejman Patkova, Katerina Mala-Ladova & Josef Maly

Authors
  1. Petr Domecky
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anna Rejman Patkova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katerina Mala-Ladova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Josef Maly
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Review conceptualization was designed by PD and ARP. Methodology of study design and literature search strategies were developed and done by PD, ARP and KML. Evaluation of study quality risk assessment tools, data extraction, data synthesis and statistical tests were assessed by PD, ARP and KML. Project supervision was carried out by JM. PD wrote the manuscript draft according to the PRISMA guidelines. All authors contributed to the writing and revising of the final text.

Corresponding author

Correspondence to Josef Maly.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

Each author certifies that there are no funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article related to the author or any immediate family members.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Domecky, P., Rejman Patkova, A., Mala-Ladova, K. et al. Inflammatory blood parameters as prognostic factors for implant-associated infection after primary total hip or knee arthroplasty: a systematic review. BMC Musculoskelet Disord 24, 383 (2023). https://doi.org/10.1186/s12891-023-06500-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12891-023-06500-z

Keywords

BMC Musculoskeletal Disorders

ISSN: 1471-2474

Contact us