Abstract

JBJI

Journal of Bone and Joint Infection

JBJI

J. Bone Joint Infect.

2206-3552

Copernicus Publications

Göttingen, Germany

10.5194/jbji-11-687-2026

Corrigendum to “Differential synovial fluid white blood cell count for the diagnosis of chronic peri-prosthetic joint infection – a systematic review and meta-analysis” published in J. Bone Joint Infect., 10, 165–184, 2025

Corrigendum: Differential synovial WBC count in chronic PJI

Sabater-Martos

Marta

sabaterms@gmail.com

https://orcid.org/0000-0002-9350-3648

Langworthy

Benjamin

Clauss

Martin

https://orcid.org/0000-0002-1598-2209

Ribau

Ana

Sousa

Ricardo

and on behalf of the Leukocyte Count Synovial Fluid working group for the Unified PJI definition task force

1Orthopedic and Traumatology Department, Clínic Barcelona. Carrer Villarroel 170, 08036 Barcelona, Spain 2Division of Biostatistics and Health Data Science, School of Public Health, Minneapolis, MN, USA 3Center for Musculoskeletal Infections (ZMSI), University Hospital Basel, Basel, Switzerland 4Department for Orthopaedics and Trauma Surgery, University Hospital Basel, Basel, Switzerland 5Orthopaedic and traumatology department, Unidade Local de Sáude do Médio Ave, Famalicao, Portugal 6Porto Bone and Joint Infection Group (GRIP), ULS Santo António – Porto and CUF hospitals, Porto and Lisbon, Portugal ➕A full list of authors appears at the end of the paper.

Marta Sabater-Martos (sabaterms@gmail.com)

10November2026

11 6 687704 9October2026 18October2026

2026

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/

This article is available from https://jbji.copernicus.org/articles/11/687/2026/jbji-11-687-2026.html

The full text article is available as a PDF file from https://jbji.copernicus.org/articles/11/687/2026/jbji-11-687-2026.pdf

Abstract

Introduction: Peri-prosthetic joint infection (PJI) is a significant complication of arthroplasty, lacking a single gold standard diagnostic test. Synovial fluid white blood cell (WBC) count and polymorphonuclear neutrophil (PMN) proportion are widely used diagnostic tools, but their optimal cutoffs remain unclear, particularly for chronic PJI. Material and methods: This systematic review and meta-analysis included 74 studies published between 2000 and 2024. Data on diagnostic performance (sensitivity, specificity, and diagnostic odds ratios – DORs) of WBC count and PMN proportions were analysed. Sub-group analyses and heterogeneity assessments were performed, and optimal cutoffs for diagnostic accuracy were identified. Results: The meta-analysis revealed a WBC count summary DOR of 58.38 (95 % CI – confidence interval: 48.48–70.32) with an area under the curve (AUC) of the summarized receiver operating characteristic curve of 0.952. The PMN proportion showed a DOR of 43.17 (95 % CI: 35.31–52.79) and an AUC of 0.941. Optimal diagnostic thresholds for chronic PJI were WBC count ≈ 1792 cells mL⁻¹ (sensitivity 86 %, specificity 87 %) and PMN ≈ 67 % (sensitivity 89 %, specificity 86 %). No single threshold reached ≥ 95 % specificity or ≥ 95 % sensitivity: specificity was ≈ 91 % at WBC count ≥ 3000 cells mL⁻¹ and ≈ 90 % at PMN ≥ 75 %, and sensitivity was ≈ 88 %–90 % at WBC count ≤ 1500 cells mL⁻¹ and PMN ≤ 65 %. Confounding conditions such as fractures, inflammatory arthritis, and metal-related reactions reduced test accuracy. Conclusions: Synovial fluid analysis remains a critical diagnostic tool for chronic PJI. Lower thresholds (WBC count > 1500 cells mL⁻¹; PMN > 65 %) are most useful for raising suspicion of infection and upper thresholds (WBC count >3000 cells mL⁻¹; PMN > 75 %) for supporting it, although as single markers neither reaches the ≥ 95 % specificity. A standardized diagnostic framework is essential for addressing remaining controversies and ensuring consistent interpretation across clinical settings.

1Introduction

Peri-prosthetic joint infection (PJI) is a potentially dismal complication of total joint arthroplasty. As the expected number of arthroplasties is rising worldwide, so, too, is the burden of the small but consistent infection rate (Premkumar et al., 2021; Rupp et al., 2020). In fact, infection is a leading cause of total joint replacement failure, and it is critical to accurately diagnose it, as failure to do so will hamper treatment success (Sousa et al., 2023). Still, there is no available gold standard diagnostic test on its own. The definition of PJI thus relies on the combined interpretation of multiple different tests (Osmon et al., 2013; Parvizi and Gehrke, 2014; Shohat et al., 2019; Sousa et al., 2023; Workgroup Convened by the Musculoskeletal Infection Society, 2011).

Synovial fluid differential white blood cell (WBC) count is a widely available and rather inexpensive test that has long been recognized as a critical part of the preoperative workup before revision arthroplasty. Its accuracy for the diagnosis of PJI in the setting of chronically painful joints has been studied extensively and gives consistently good results.

There are, nevertheless, open questions. First, different proposed cutoffs exist due to variations in PJI definitions and joint-specific studies. Second, conditions such as peri-prosthetic fractures, inflammatory arthritis, crystal arthropathy, or metal-ion-related tissue reactions can elevate WBC counts irrespective of infection, affecting accuracy. Third, the role of differential WBC count in acute postoperative PJI remains less clear due to post-surgical inflammation.

This systematic review aimed to (1) determine the overall diagnostic accuracy of synovial WBC counts and polymorphonuclear neutrophil (PMN) proportions for chronic PJI, (2) establish optimal interpretation cutoffs, and (3) evaluate test performances under confounding conditions.

2Material and methods

This systematic review adheres to PRISMA of Diagnostic Test Accuracy Studies (PRISMA-DTA) (McInnes et al., 2018).

2.1Search strategy

A computer-aided search on MEDLINE and EMBASE (1 January 2000–1 February 2024) identified studies evaluating synovial WBC count and PMN proportion for PJI diagnosis. Reference lists of included articles were also reviewed. The search strategy combined terms related to arthroplasty infection, synovial fluid diagnosis, and diagnostic accuracy (see Table S1 in the Supplement).

2.2Inclusion and exclusion criteria

Studies were included if they evaluated synovial fluid WBC count and/or PMN proportion in suspected PJI cases, provided diagnostic performance data, were conducted on humans ≥ 18 years old, and were published in English between 2000 and 2024. Only studies reporting results on chronic PJI were included. We excluded studies on infections unrelated to PJI, case reports, animal studies, and reviews.

Duplicates were removed using Mendeley. Screening was performed with Rayyan software by two independent reviewers per article, first by title or abstract and then by full text. Disagreements were resolved by consensus. The study selection process is shown in Fig. 1 (Page et al., 2021).

Figure 1

PRISMA flow diagram.

2.3Data extraction

Data were extracted in duplicate, resolving discrepancies by consensus. Collected data included study characteristics, diagnostic criteria, infection type, patient characteristics (e.g. inflammatory diseases, fractures, metallosis, or inflammatory arthritis), WBC count and PMN cutoffs, sensitivity, specificity, predictive values, area under the curve (AUC), accuracy, and Youden index. True positive, true negative, false positive, and false negative values were calculated.

2.4Risk of bias and quality assessment

Two independent investigators per article assessed the risk of bias and applicability of each included study by using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) (Whiting et al., 2011) tool, with ratings of “low”, “high”, or “unclear”. Discrepancies were resolved by consensus.

Quality was assessed by two independent investigators per article with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool (Schünemann et al., 2023). This tool is used to evaluate the quality of the evidence and the strength of the recommendations. Studies were classified as “high (A)”, “moderate (B)”, “low (C)”, or “very low (D)” quality.

2.5Information synthesis and statistical analysis

With the information extracted, we calculated the diagnostic odds ratio (DOR) for every study. Although sensitivity and specificity are commonly used in diagnostic test research due to their clinical relevance, these metrics can introduce a threshold effect when multiple studies are compared. Although the DOR has limited direct clinical application, it is particularly useful in systematic reviews of diagnostic accuracy because it reflects the overall effectiveness of a diagnostic test (Arias and Molina, 2015; De Sousa et al., 2009). We used DORs along with 95 % confidence intervals (CIs) and summarized receiver operating characteristic (sROC) curves as part of the meta-analysis (De Sousa et al., 2009). The analyses were conducted by using a random-effects model and the DerSimonian–Laird approach. In addition, a sub-group analysis was performed based on the joint involved (hip or knee).

Heterogeneity in the reported sensitivities and specificities was assessed by using Higgins' I2 statistic. All statistical analyses were performed with Jamovi (version 2.2.5) and Meta-disc (versions 1–4).

2.6Cutoff determination

To determine interpretation cutoff values, we used only those articles that calculated their own ideal cutoff based on their data and excluded all papers that used pre-determined cutoffs. We excluded from this analysis studies that focused on patients with confounding circumstances such as fractures or dislocation, inflammatory arthritis, crystal arthropathy, or adverse local tissue reactions due to metal ions.

We used three different data interpretation strategies: (1) the “optimal” cutoff based on the best combination of specificity and sensitivity, (2) a “rule-in” cutoff that focused on optimizing specificity, and (3) a “rule-out” cutoff that focused on optimizing sensitivity.

We used the Youden index and extracted cutoffs. The optimal cutoff was determined determined as the Youden-optimal point of a multiple-threshold model (diagmeta) fitted across all reported cut-offs. We obtained the operating points for the combined total knee arthroplasty (TKA) and total hip arthroplasty (THA). Per-joint operating points were not separately re-modelled.

To describe rule-in/rule-out behaviour, specificity and sensitivity at higher and lower cut-offs were read from the same model across the full range of cut-offs.

We performed a post hoc sub-analysis by sub-dividing groups between the modern or more sensitive PJI definition criteria (International Consensus Meeting ICM, European Bone and Joint Infection Society EBJIS, and Infectious Disease Society of America IDSA) and the old PJI definition criteria (Musculoskeletal Infection Society MSIS and clinical outcome) to analyse the sensitivity and specificity cutoff values of these two groups. Information concerning other joints (e.g. shoulders, elbows, hemi-hips, uni-knees, or ankles) was too scarce to merit meaningful calculations.

3Results 3.1Search results

The search identified 1685 records. After removing duplicates, 1318 studies underwent title and abstract screening. Of 170 full-text studies assessed, 96 were excluded for reasons such as wrong study type (9), incorrect population (7), unspecified reference standard (9), different index test (56), or lack of chronic PJI separation (18). Five additional studies were identified through bibliography review, yielding 74 included studies (Fig. 1).

3.2Risk of bias and quality assessment

Results from QUADAS-2 (Whiting et al., 2011) are presented in Fig. 2. Most studies had a high risk of bias but good applicability, largely due to retrospective study designs. Patient selection was unclear in more than 10 studies, while index test and reference standard issues were found in more than 20 studies.

Figure 2

QUADAS-2 and GRADE assessment.

GRADE assessment classified 13 studies (17.6 %) as B, 43 (58.1 %) as C, and 18 (24.3 %) as D (Fig. 2). Because of the retrospective nature of the majority of the papers, more than 50 % of the studies included were classified as GRADE C.

3.3Characteristics of the studies

We included 74 studies (Table 1). The publication years ranged from 2003 to 2024, the year with the highest publication rate being 2023 with 13 papers. Twenty-six papers were prospective cohorts (35.1 %) and 48 were retrospective cohorts (64.9 %), covering 18 960 patients (19 to 4462). Papers used different PJI reference standards: 14 used clinical definitions (18.9 %), 3 the IDSA 2013 definition (4.1 %), 35 the MSIS 2013 definition (47.3 %), 17 the ICM 2018 definition (23 %), and 4 the EBJIS 2021 definition (5.4 %). One paper combined MSIS 2013 and ICM 2018.

Table 1

General characteristics of primary studies.

Author Year Type of study Infected patients/total Joints PJI reference standard Special condition WBC count cutoff Sensitivity and specificity PMN cutoff Sensitivity and specificity Cutoff determination Reporting funds Bernd Fink (Fink et al., 2021) 2021 Prospective 112/434 Hips and knees ICM 2018 No 1400 500 1000 1500 2000 3000 90.2 %/91.9 % 98.8 %/91 % 93.8 %/92.9 % 90.2 %/95.7 % 86.6 %/91.2 % 80.6 %/94.9 % NA NA Youden Youden Youden Youden Youden Youden NM Giovanni Balato (Balato et al., 2018a) 2018 Retrospective 31/167 Knees MSIS No 3000 80.6 %/91.2 % 80 % 83.9 %/94.9 % Youden NM Carol Strahm (Strahm et al., 2018) 2018 Retrospective 13/19 Shoulders IDSA No 12 200 92 %/100 % 54 % 100 %/75 % Youden NM Darius M. Thiesen (Thiesen et al., 2019) 2019 Retrospective 2/31 Ankles MSIS No NA 100 %/37.5 % NA 100 %/77.8 % Youden No funding Elena De Vechhi (De Vecchi et al., 2018) 2018 Retrospective 32/66 Hips and knees MSIS No 3000 1600 93.7 %/91.2 % 100 %/82.3 % NA NA Pre-determined TWC NM Casper Dijkman (Dijkman et al., 2020) 2020 Retrospective 13/89 Hips and knees MSIS No 2575 1865 92 %/84 % 100 %/97 % NA NA Youden Youden NM Erik Schiffner (Schiffner et al., 2020) 2020 Retrospective 32/149 Knees MSIS No 3000 84 %/96 % 65 % 84 %/94 % Pre-determined NM Hai Wang (Wang et al., 2021b) 2021 Prospective 37/93 Hips and knees MSIS No NA NA 69.96 % 94.6 %/92.8 % Youden NM Hai Wang (Wang et al., 2021a) 2021 Prospective 39/97 Hips and knees MSIS No NA NA 69.8 % 84.6 %/74.1 % Youden NM Heckman Nathanael Heckmann (Heckmann et al., 2020) 2020 Prospective 16/78 Hips and knees MSIS No 3000 (pre-lavage) 3000 (post-lavage) 100 %/100 % 62.5 %/98.4 % 80 % (pre-lavage) 80 % (post-lavage) 81.3 %/96.8 % 75 %/95.2 % Pre-determined Pre-determined NM Isaac Majors (Majors and Jagadale, 2019) 2019 Prospective 32/59 Knees MSIS No 1804 %/81 % 86 % 82.4 %/73.7 % Youden NM Ivy(Ivy et al., 2021) 2021 Prospective 18/99 Knees Hips MSIS No 1700 3000 83.3 %/93.8 % 83.3 %/93.8 % 65 % 80 % 83.3 %/94.9 % 83.3 %/94.9 % Pre-determined Pre-determined NM Jana van den Kieboom (van den Kieboom et al., 2021) 2021 Retrospective 41/144 Hips and knees MSIS Fracture 3000 4552 87 %/77.9 % 86.4 %/85.3 % 80 % 79.5 % 79 %/63.2 % 73.7 %/63.2 % Pre-determined Q point NM Alexander Lazarides (Lazarides et al., 2019) 2019 Retrospective 16/87 Hips and knees MSIS Immunosuppression 1293 93.3 %/84.4 % 65 % 81.3 %/87.3 % Youden Yes Michael M. Kheir (Kheir et al., 2018) 2018 Retrospective 549/1202 Hips and knees ICM 2018 No 2659 3000 85 %/93 % 83 %/94 % 64.5 % 80 % 91 %/86 % 78 %/93 % Youden Pre-determined No funding Maxime Huard (Huard et al., 2020) 2020 Prospective 41/138 50/138 68/138 35/138 Hips and knees MSIS IDSA EBJIS Positive culture Positive culture No 3000 3000 3000 881 3077 87.7 %/95.7 % 70 %/94.2 % 55.8 %/97 % 80 %/91 % 91 %/85 % NA NA Pre-determined Pre-determined Pre-determined Youden Youden NM Vikas V. Patel (Patel et al., 2021) 2021 Retrospective 20/87 Shoulders ICM 2018 No 3000 30 %/100 % 80 % 20 %/100 % Pre-determined NM Leilei Qin (Qin et al., 2020) 2020 Prospective 25/50 Hips and knees MSIS No NA NA 69.79 % 92 %/80 % Youden NM Alisina Shahi (Shahi et al., 2017) 2017 Retrospective 822/4462 Hips and knees MSIS No 3000 85.8 %/83 % 80 % 85.8 %/80.8 % Pre-determined NM

Table 1

Continued.

Author Year Type of study Infected patients/total Joints PJI reference standard Special condition WBC count cutoff Sensitivity and specificity PMN cutoff Sensitivity and specificity Cutoff determination Reporting funds Katyayini Sharma (Sharma et al., 2020) 2020 Retrospective 50/107 Hips and knees MSIS No 1100 89 %/98 % 72 % 92 %/91 % Pre-determined NM Noam Shohat (Shohat et al., 2018) 2018 Retrospective 567/1220 Hips and knees MSIS No No Inflammation Inflammation 3000 2533 3000 2683 81.8 %/95.1 % 83.4 %/93.3 % 88.2 %/78.6 % 91.2 %/78.6 % 80 % 73 % 80 % 72 % 77.7 %/94.3 % 86.8 %/89.9 % 93.7 %/84.6 % 100 %/82.7 % Pre-determined Youden Pre-determined Youden NM Mesut Tahta (Tahta et al., 2019) 2019 Prospective 17/38 Knees MSIS Inflammation 2347 84.6 %/76.4 % 76.7 % 79.2 %/90.3 % Youden NM Young-Min Kwon (Kwon et al., 2020) 2020 Retrospective 11/89 Hips MSIS ATRLs 2144 13 796 93 %/84 % 100 %/100 % 84 % 82 % 84 %/86 % 93 %/82 % Youden Youden No funding Akos Zahar (Zahar et al., 2018) 2018 Retrospective 134/337 Hips and knees Hips Knees MSIS No 2582 1630 3063 80.6 %/85.2 % 83.5 %/82.2 % 78.1 %/80 % 66.1 % 60.5 % 66.1 % 80.6 %/83.3 % 80.3 %/77.1 % 82.2 %/82.4 % Youden Youden Youden NM Zeyu Zhang (Zhang et al., 2020) 2020 Retrospective 21/63 Hips and knees MSIS No 3000 85.7 %/83.3 % 65 % 90.5 %/85.7 % Youden NM Jiaxing Huang (Huang et al., 2022) 2022 Prospective 43/99 Hips and knees MSIS No 3000 85.7 %/83.3 % 75 % 95.3 %/78.6 % Youden NM Christian Klemt (Klemt et al., 2023) 2023 Retrospective 191/464 Hips ICM 2018 No 3000 86.9 %/79.1 % 80 % 87.9 %/78.7 % Pre-determined NM Julian E. Dilley (Dilley et al., 2023) 2023 Retrospective 321/730 Hips and knees MSIS No 5600 3000 71.8 %/85.6 % 76 %/80.2 % 82 % 80 % 81 %/77.5 % 82.2 %/76 % Youden Pre-determined NM Y. Gramlich Yves Gramlich (Gramlich et al., 2023) 2023 Prospective 111/405 Hips, knees, and shoulders Knees Hips ICM 2018 No 2478.9 1500 1750 2000 2500 3000 3085.7 2267.8 87.7 %/88.1 % 91.5 %/75 % 89.6 %/78.7 % 89.6 %/83.4 % 86.6 %/88.1 % 82.1 %/91 % 86.7 %/91.8 % 87.5 %/83.5 % 67 % 65 % 70 % 80 % 67.01 % 67.04 % 86 %/88.8 % 86 %/88.8 % 82.6 %/90 % 66.3 %/96.5 % 87.5 %/88.2 % 81.5 %/94.1 % Youden Pre-determined Youden Youden NM Feng Chih Kuo (Kuo et al., 2022) 2022 Retrospective 42/76 Hips and knees ICM 2018 No 3000 68 %/94 % 70 % 71 %/84 % Pre-determined No funding Yuanquing Cai (Cai et al., 2023) 2023 Retrospective 46/74 Hips and knees MSIS No 3000 63 %/74 % 80 % 78.2 %/71.4 % Pre-determined NM Igor Lazic (Lazic et al., 2022a) 2022 Prospective 17/32 Hips and knees EBJIS Fracture 6130 41 %/100 % 79.5 % 71 %/100 % Youden NM Taner Karlidag (Karlidag et al., 2024) 2024 Retrospective 50/144 59/116 Knees Hips ICM 2018 No 2553.5 3512.5 92 %/91.4 % 83 %/85.9 % 81.7 % 83.6 % 88 %/93.6 % 86.4 %/93.6 % Youden Youden No funding Colin M. Baker (Baker et al., 2022) 2022 Retrospective 194/621 162/520 32/68 Hips and knees Knees Hips ICM 2018 No 3000 3000 3000 85.7 %/100 % 92 %/99 % 92.8 %/98.5 % 80 % 80 % 80 % 92.6 %/96 % 86.4 %/99.2 % 85.3 %/99.4 % Pre-determined Pre-determined Pre-determined NM Daniel Timothy Burchette (Burchette et al., 2024) 2024 Retrospective 185/362 111/195 73/165 Hips and knees Hips Knees ICM 2018 No 2581 2373 4058 74.3 %/88 % 81 %/77.8 % 66.7 %/97.2 % 75.4 % 73.4 % 75.4 % 68.8 %/97.6 % 72.2 %/98.1 % 64.8 %/97.2 % Youden Youden Youden NM Xudong Su (Su et al., 2023) 2023 Retrospective 30/60 Hips and knees MSIS No NA NA 51.1 % 96.7 %/73.3 % Youden NM Hao Li (Li et al., 2023) 2023 Retrospective 48/90 Hips and knees ICM 2018 No 3000 74 %/94.4 % 70 % 79.6 %/79.6 % Pre-determined NM Cristoph Theil (Theil et al., 2024) 2023 Retrospective 55/108 Knees MSIS ALTRs 1200 1500 3000 94.5 %/75.5 % 89.1 %/79.2 % 70.1 %/94.3 % 63 % 65 % 70 % 80 % 85.5 %/73.6 % 85.5 %/73.6 % 76.3 %/77.4 % 60 %/81.1 % Youden Pre-determined Pre-determined Pre-determined NM Sara Elisa Diniz (Diniz et al., 2023) 2023 Retrospective 36/102 Hips and knees EBJIS No 1470 2645 3280 91.7 %/92.4 % 77.8 %/98.5 % 77.8 %/100 % 62.5 % 64.4 % 79.5 % 88.9 %/83.9 % 86.1 %/88.7 % 75 %/98.4 % Youden Youden Youden NM

Table 1

Continued.

Author Year Type of study Infected patients/total Joints PJI reference standard Special condition WBC count cutoff Sensitivity and specificity PMN cutoff Sensitivity and specificity Cutoff determination Reporting funds Mohammed F. Alkadhem (Alkadhem et al., 2024) 2024 Retrospective 19/137 Hips and knees Knees Hips EBJIS No 1500 3000 3000 3000 74 %/71 % 75 %/86 % 100 %/87 % 58 %/84 % 65 % 80 % 80 % 80 % 89 %/78 % 89 %/85 % 100 %/85 % 91 %/84 % Pre-determined Pre-determined Pre-determined Pre-determined NM Sara Elisa Diniz (Streck et al., 2022b) 2022 Retrospective 21/35 Shoulders ICM 2018 No 700 85.7 %/100 % NA NA Youden NM Igor Lazic (Lazic et al., 2022b) 2022 Prospective 14/30 Hips and knees EBJIS Fracture 4550 40 %/100 % 79 % 30 %/100 % Youden NM Leilei Qin (Qin et al., 2022) 2022 Prospective 35/70 Hips and knees MSIS Inflammation NA NA 69.8 % 88.6 %/80 % Youden NM Yulai Wang (Wang et al., 2023) 2023 Retrospective 164/348 Hips and knees MSIS No Inflammation 2673 3654 80.5 %/96.4 % 80.5 %/94.4 % 62 % 65.9 % 92.8 %/92.9 % 85.4 %/77.8 % Youden Youden Yes Ali Levent (Levent et al., 2021) 2021 Retrospective 109/260 Hips and knees ICM 2018 No 3000 2950 87.2 %/88.1 % 88.1 %/88.1 % 70 % 77.8 % 89.9 %/84.1 % 89.9 %/91.4 % Pre-determined Youden No funding Cristoph Theil (Theil et al., 2022) 2022 Prospective 30/92 Hips and knees ICM 2018 No 3000 90 %/88 % 80 % 73 %/98 % Pre-determined NM Martra Bottagisio (Bottagisio et al., 2023) 2023 Prospective 12/55 Hips and knees ICM 2018 No 6740 85.7 %/97.1 % 85 % 85.7 %/97.1 % Youden NM Yi Ren (Ren et al., 2022) 2020 Retrospective 63/NA Hips and knees Cultures Inflammation 1948 83.3 %/72.7 % 85.3 % 83.3 %/73 % Youden NM Steven Denyer (Denyer et al., 2023) 2023 Retrospective 124/283 122/289 Knees Hips IDSA No No 3000 3000 89.9 %/89.7 % 82.5 %/80 % 80 % 80 % 89.5 %/100 % 82.5 %/62.5 % Pre-determined Pre-determined NM Laura Elisa Streck (Streck et al., 2022a) 2021 Retrospective 15/31 Shoulders IDSA No 2800 86.7 %/87.5 % NA NA Youden NM Mehdi S. Salymi (Salimy et al., 2023) 2023 Retrospective 116/207 Hips and knees ICM 2018 Antibiotic 2339 95.2 %/93.3 % 83 % 90.5 %/80 % Youden No funding Wayne B. Cohen-Levy (Cohen-Levy et al., 2022) 2002 Retrospective 20/109 Knees MSIS + ICM 2018 No 2695 94 %/90 % 52.5 % 94 %/93 % Youden No funding Asa Nilsdotter (Nilsdotter-Augustinsson et al., 2007) 2007 Prospective 25/85 Hips Symptoms Cultures Sinus No 1700 86 %/92 % NA NA Youden NM Giovanni Balato (Balato et al., 2018b) 2017 Prospective 16/51 Knees MSIS No 3000 75 %/91.4 % 80 % 75 %/97 % Pre-determined NM Benjamin Zmistowski (Zmistowski et al., 2012) 2013 Retrospective 73/150 Knees Cultures Synovial fluid No 3000 93 %/94 % 75 % 93 %/83 % Youden NM Pieter Berger (Berger et al., 2017) 2017 Prospective 34/121 Hips and knees MSIS No 3000 89.3 %/96.2 % 80 % 89.3 %/92.2 % Pre-determined NM Peter N. Chalmers (Chalmers et al., 2014) 2015 Prospective 91/433 Knees Cultures Sinus Synovial fluid Fracture 4450 90 %/98.5 % 73 % 89.4 %/93.6 % Youden NM Cara A. Cipriano (Cipriano et al., 2012) 2012 Prospective 165/871 Hips and knees Cultures Synovial fluid No Inflammation 3450 3444 91 %/93 % 88.2 %/80 % 78 % 75 % 95.5 %/87.3 % 100 %/93 % Youden Youden No funding Alex Dinnen (Dinneen et al., 2013) 2013 Prospective 34/75 Hips and knees Hips Knees Cultures Histology No 1590 1425 1715 89.5 %/91.3 % NA NA 65 % 65 % 54 % 89.7 %/86.6 % NA NA Youden Youden Youden NM Eli Ghanem (Ghanem et al., 2008b) 2008 Retrospective 91/128 Knees Cultures X-rays Purulence CRP No 1760 95.6 %/68.6 % 73 % 95.6 %/73 % Youden Yes

Table 1

Continued.

Author Year Type of study Infected patients/total Joints PJI reference standard Special condition WBC count cutoff Sensitivity and specificity PMN cutoff Sensitivity and specificity Cutoff determination Reporting funds Eli Ghanem (Ghanem et al., 2008a) 2008 Retrospective 161/429 Knees Abscess Sinus Cultures Purulence No 1100 90.7 %/88.1 % 64 % 95 %/94.7 % Youden Yes Nathanel Heckmann (Heckmann et al., 2023) 2023 Retrospective 21/172 Knees ICM 2018 No 3000 85.7 %/100 % 80 % 79.2 %/99.1 % Pre-determined NM Carlos A. Higuera (Higuera et al., 2017) 2017 Retrospective 79/453 Hips MSIS No 3966 89.5 %/91.2 % 80 % 92.1 %/85.8 % NA No funding Joshua Bingham (Bingham et al., 2014) 2014 Retrospective 19/61 Hips and knees MSIS No 1700 95 %/85 % NA NA Youden Yes Markus Lenski (Lenski and Scherer, 2014) 2014 Retrospective 31/69 Hips, knees, and shoulders MSIS No 2300 60 %/94.3 % NA NA Youden NM Young-Min Kwon (Kwon et al., 2016) 2016 Retrospective 7/62 Hips MSIS ALTRs 730 12 600 6800 4400 3835 2930 2700 892 86 %/80 % 14 %/96 % 43 %/96 % 43 %/95 % 57 %/95 % 57 %/93 % 71 %/93 % 71 %/80 % 65 % 94 % 89 % 87 % 84 % 80 % 75 % 74 % 100 %/70 % 17 %/94 % 33 %/89 % 33 %/85 % 50 %/85 % 50 %/79 % 83 %/79 % 83 %/70 % Youden Youden Youden Youden Youden Youden Youden Youden No funding J. Bohannon Mason (Mason et al., 2003) 2003 Retrospective 31/86 Knees Cultures Histology No 5000 2500 19 %/100 % 69 %/98 % 80 % 60 % 57 %/100 % 76 %/89 % Paired Paired No funding Mohamed Omar (Omar et al., 2015) 2015 Prospective 21/80 Hips Sinus Purulence Cultures No 3089 85 %/86.3 % 72.1 % 90 %/99 % Youden NM Javad Parvizi (Parvizi et al., 2006) 2006 Prospective 94/145 Hips and knees Cultures Purulence CRP/ESR No 1760 90 %/99 % 73 % 93 %/95 % Youden NM Paul H. Yi (Yi et al., 2015) 2015 Retrospective 19/150 Hips MSIS ATLRs 4350 100 %/95 % 85 % 82 %/87 % Youden Yes Unicondylar Knee Society (Schwartz et al., 2012) 2012 Retrospective 28/259 Unicondylar knees Cultures Sinus Purulence Histology No 6200 90 %/96.5 % 60 % 90.8 %/93.8 % Youden NM Ricardo Sousa (Sousa et al., 2017) 2017 Prospective 23/55 Hips and knees ICM 2018 No 1463 2064 100 %/71.9 % 91.3 %/75 % 78 % 81 % 87 %/71.9 % 78.3 %/75 % Youden Youden No funding Andrej Trampuz (Trampuz et al., 2004) 2004 Prospective 34/133 Knees Cultures Purulence Histology No 1700 94 %/88 % 65 % 97 %/98 % NM Yes Roshan P. Shah (Shah et al., 2016) 2016 Retrospective 14/121 Hips and knees MSIS Fracture 2707 100 %/65 % 77 % 100 %/63 % Youden Yes

NA: not available

We extracted 135 different cutoffs for synovial WBC count and 114 different cutoffs for synovial PMN. The distribution by joint for synovial WBC count was 63 for hips and knees together, 28 for knees only, 31 for hips only, 4 for shoulders only, and 7 for all four joints together. The distributions by joint for synovial polymorphonuclear proportion (PMN %) were 52, 29, 29, 2, and 4, respectively. There was also one study that focused on unicompartmental knee replacements (Schwartz et al., 2012), one on hip hemiarthroplasties (Salimy et al., 2024), and one on total ankle replacement (Thiesen et al., 2019), which provided information on synovial WBC count and PMN % (Table 2).

Table 2

Proposed interpretation cutoffs for joints other than TKA and THA.

WBC count PMN (cells mL⁻¹) (%) Range Median Range Median Total shoulder 700–12 200 2900 54 %–80 % 67 % Unicompartmental knee 6200^* 60 %^* Hip hemiarthroplasty 2700^* 81 %^* Total ankle NA^* NA^*

^* Only one paper for this condition. TKA: total knee arthroplasty. THA: total hip arthroplasty. WBC: white blood cell. PMN %: polymorphonuclear proportion. NA: not available.

Figure 3

sROC for WBC count and PMN % in all joints.

Considering all papers with no sub-groupings at all, the proposed optimal cutoffs varied widely between 700 and 19 240 cells mL⁻¹ and between 53 % and 99 % PMN. Median values were 3000 cells mL⁻¹ and 75.4 % PMN for all joints and all conditions. Median values for knees, hips, and shoulders separately were 3000 cells mL⁻¹ and 71.5 % PMN, 3000 cells mL⁻¹ and 80 % PMN, and 2900 cells mL⁻¹ and 67 % PMN, respectively.

3.4Meta-analysis

WBC count showed a summary DOR of 58.38 (95 % CI: 48.48–70.32), an AUC of the sROC of 0.941 (SE: 0.0045), and a Q index of 0.879. PMN proportion showed a summary DOR of 43.17 (95 % CI: 35.31–52.79), an AUC of the sROC 0.932 (SE: 0.0057), and a Q index of 0.867 (Fig. 3).

The heterogeneities (I2) of sensitivity and specificity for WBC count were 79.5 % and 92.4 %, respectively, and for PMN % they were 85 % and 91.4 %, respectively. DOR heterogeneity was 77.4 % for WBC count and 77.9 % for PMN %.

In the sub-group analysis by joint, knees presented a WBC count summary DOR of 83.1 (95 % CI: 54.72–126.21), an AUC of the sROC of 0.954 (SE: 0.0077), a Q index of 0.896, a PMN proportion summary DOR of 63.29 (95 % CI: 38.62–103.74), an AUC of the sROC of 0.942 (SE: 0.011), and a Q index of 0.88. In hips, results showed a WBC count summary DOR of 33.58 (95 % CI: 21.44–52.63), an AUC of the sROC of 0.908 (SE: 0.015), a Q index of 0.84, a PMN proportion summary DOR of 27.12 (95 % CI: 16.22–45.35), an AUC of the sROC of 0.91 (SE: 0.019), and a Q index of 0.842 (Figs. 4 and 5).

Figure 4

sROC for WBC count and PMN % in knees.

Figure 5

sROC for WBC count and PMN % in hips.

3.5Cutoff determination

In the chronic stage, when no confounding circumstances were present, and excluding papers that used pre-determined cutoffs, the suggested cutoffs varied between 1100 and 5000 cells mL⁻¹ and 53 % and 86 % PMN. The optimal cutoff was determined as the Youden-optimal point at 1792 cells mL⁻¹ and 67 %.

Figure 6

Forest plot of specificity of the WBC (A) and proportion of PMN (B) based on a linear mixed model with a random intercept for study. Based on this model the specificity at a cutoff of 3000 cells mL⁻¹ is 90.6 % and 88.3 % for 75 % PMN proportion.

Analysed by cut-off value with the multiple-threshold model, specificity rose with increasing cut-off but did not exceed approximately 91 % (WBC count, at ≥ 3000 cells mL⁻¹) or 90 % (PMN %, at ≥ 75 %) at clinically usable thresholds, and sensitivity at the lower cut-offs (≤ 1500 cells mL⁻¹; ≤ 65 %) did not exceed approximately 90 %; no threshold reached 95 % specificity or sensitivity (Fig. 6 and Table 3).

Table 3

Summary of the proposed cutoffs.

WBC count (cells mL⁻¹) PMN (%) Cutoff Sensitivity (95 % CI) Specificity (95 % CI) Cutoff Sensitivity (95 % CI) Specificity (95 % CI) Optimal (Youden) 1.792 0.86 (0.80–0.91) 0.87 (0.82–0.91) 70 % 0.89 (0.84-0.93) 0.86 (0.8-0.91) 1500 0.88 (0.81–0.92) 0.86 (0.80–0.90) 65 % 0.90 (0.85–0.94) 0.85 (0.79–0.90) 3000 0.82 (0.74–0.87) 0.91 (0.87–0.94) 75 % 0.84 (0.77–0.89) 0.90 (0.86–0.93)

When sub-dividing groups between more sensitive PJI definition criteria (ICM, EBJIS, and IDSA) and old PJI definition criteria (MSIS and clinical outcome), we could see that median reported cutoffs were very similar between them, 2530 and 2413 cells mL⁻¹ for leucocyte count, and 73.3 % and 68.5 % for PMN % respectively.

3.6Confounding conditions

Some papers were focused on specific conditions that can modify synovial cellularity and PMN %, the so-called confounding conditions. We found six different conditions: five papers were specific to peri-prosthetic fracture associated with PJI (Chalmers et al., 2014; van den Kieboom et al., 2021; Lazic et al., 2022a, b; Shah et al., 2016), six papers to inflammatory conditions (Cipriano et al., 2012; Qin et al., 2022; Ren et al., 2022; Shohat et al., 2018; Tahta et al., 2019; Wang et al., 2023), four papers to adverse local tissue reactions due to head–neck taper corrosion or metal-on-metal bearings (Kwon et al., 2016, 2020; Theil et al., 2024; Yi et al., 2015), one paper to immunosuppressed patients (Lazarides et al., 2019), and one paper to antibiotic use (Salimy et al., 2023).

Table 4

Summary findings of confounding conditions.

WBC count PMN (cells mL⁻¹) (%) Range Median Range Median Fracture 2707–6130 4500 73 %–80 % 79.3 % Inflammation 1948–5208 3222 65.9 %–85.3 % 75 % ALTRs and metal-on-metal 730–13 796 2815 63 %–94 % 82 % Immunosuppression 1293^* Antibiotic 2339^* 83 %^*

^* Only one paper for this condition. WBC: white blood cell. PMN %: polymorphonuclear proportion. ALTRs: adverse local tissue reactions.

The cutoff variability and medians of confounding conditions are presented in Table 4. We were not able to replicate our cutoff interpretation strategy due to the small sample size, but Fig. 7 shows a rough estimate of the diagnostic accuracy in each scenario.

Figure 7

Sensitivity and specificity for special conditions.

4Discussion

Accurate PJI diagnosis is challenging, as there is no perfect gold standard test. Definitive diagnosis must therefore rely on a set of pre-determined criteria that include both preoperative and intraoperative findings such as microbiological and histological results. In the past decade, several different PJI definitions have been proposed, which have all included synovial fluid examination (Mcnally et al., 2021; Osmon et al., 2013; Shohat et al., 2019; Workgroup Convened by the Musculoskeletal Infection Society, 2011). Although there is no universally accepted algorithm for diagnosis, it is indisputable that joint aspiration with a differential synovial fluid WBC count is a critical part of current diagnosis.

Our systematic review confirms its value. The DOR and sROC results obtained in our meta-analysis demonstrated that both tests have a high diagnostic accuracy and are powerful tools for reaching PJI diagnosis. On the basis of our results, WBC count analysis demonstrated the highest DOR and sROC values compared with those for PMN proportion. In addition to the high diagnostic accuracy of synovial fluid analysis, it is both inexpensive and widely available. We believe it should therefore continue to be a critical part of any diagnostic flowchart and definition criteria.

Still, there are a number of open questions that we were not able to address entirely in this paper. First, the role of synovial WBC and PMN proportion analysis in the immediate postoperative period is not as well studied. After joint replacement surgery, there is a rise in both WBC count and PMN proportion that takes several weeks to normalize despite an uneventful course (Christensen et al., 2013). We found a number of studies that focused on acute postoperative PJI, but they lacked consistency in how to define infection or even in what timing constitutes the acute period, and therefore we chose to analyse them separately in a different paper.

A different controversy that we were not able to clarify completely is whether the affected joint has an effect on the optimal threshold of the WBC count and/or PMN proportion. The vast majority of papers we found focused on TKA and/or THA separately or combined. We did not find significant differences to recommend adopting different thresholds for hips and knees. In addition, there is not enough available evidence to recommend alternative cutoffs for joints other than TKA and THA.

Though it is not controversial that total WBC count and PMN proportion are highly valuable tests in the diagnostic workup for chronic PJI, still lacking is a universal consensus on what the ideal interpretation thresholds are. Many of the papers that examine the performance of these tests simply adopt previously proposed cutoffs and report on their accuracy in a specific cohort. Many others analyse their own population, adopt certain diagnostic criteria to serve as the PJI gold standard, and offer optimal cutoffs for interpretation. These results are influenced by variables such as population characteristics (e.g. PJI prevalence), a PJI definition standard, or even different laboratory (e.g. microbiology) standards that greatly influence final results. That being said, despite the wide range of proposed cutoffs, we were able to find some consistent results.

The optimal threshold, defined as the median of proposed cutoffs based on the best combination of specificity and sensitivity, was found to be around 1792 cells mL⁻¹ and 67 % PMN overall. Nevertheless, we aimed to find a rule-in threshold by focusing on optimizing specificity at > 95 % and found that it was slightly higher (both for WBC count and PMN %). We recommend the 3000 cells mL⁻¹ (sensitivity 82 %, specificity 91 %) and 75 % PMN (sensitivity 84 %, specificity 90 %) thresholds to support the infection diagnosis and definition, as these figures are easy to recall and are compatible with both previously proposed cutoffs and results specifically in papers that use more sensitive or modern definitions (i.e. EBJIS 2021, ICM 2018, and IDSA). On the other hand, when one is looking to rule out PJI during the diagnostic workup, we recommend adopting a lower threshold that optimizes sensitivity. Following similar principles enunciated previously, we recommend that infection be suspected and other diagnostic test(s) performed with a WBC count of over 1500 cells mL⁻¹ (sensitivity 88 %, specificity 86 %) and 65 % PMN (sensitivity 90 %, specificity 85 %).

Lastly, it is crucial to acknowledge that there are special confounding circumstances that hamper the diagnostic accuracy of these tests. Previous antibiotic therapy is associated with a high number of false negative results (Salimy et al., 2023; Shahi et al., 2015; Trampuz et al., 2007). In the chronic setting, this limitation is simple to overcome by withholding antibiotics and repeating the joint tap at a later stage (at least 2 weeks after antibiotic discontinuation). Other pro-inflammatory conditions are perennial and may cause elevated WBC counts regardless of the presence of infection. These circumstances may be joint- or implant-related, e.g. associated peri-prosthetic fractures, dislocation, or metallosis, or may include systemic diseases such as inflammatory arthritis or crystal arthropathy. We found no evidence to justify adopting different condition-specific cutoffs, but information given by the WBC count and/or PMN proportion should be interpreted with caution, as diagnostic accuracy is inferior in these settings.

5Conclusion

Synovial fluid WBC count is a crucial part of any PJI diagnostic workup and definition. In the absence of confounding circumstances, WBC counts of < 1500 and > 3000 cells mL⁻¹ and proportions of PMN < 65 % and > 75 % have a good negative predictive value at the lower thresholds and, at the upper thresholds, support the diagnosis. A uniform PJI diagnostic standard is required to facilitate studies able to address persistently open questions, such as the true optimal interpretation thresholds or the need for joint-specific or even condition-specific cutoffs.

Code and data availability

Software code and data supporting the conclusions of this article will be made available by the authors on request.

The supplement related to this article is available online at https://doi.org/10.5194/jbji-11-687-2026-supplement.

Team list

Marjan Wouthuyzen-Bakker (Department of Medical Microbiology and Infection Prevention, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands), Thomas Bauer (Department of Pathology, The Cleveland Clinic Foundation, Cleveland, OH, USA), Elie Berbari (Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA), Martin Clauss (Department of Orthopaedics and Trauma Surgery, University Hospital Basel, Basel, Switzerland), Nicolas Cortes-Penfield (Division of Infectious Diseases, University of Nebraska Medical Center, Omaha, NE, USA), Matthew Dietz (Department of Orthopaedics, School of Medicine, West Virginia University, Morgantown, WV, USA), Jaime Esteban (Department of Medical Microbiology, IIS-Fundacion Jimenez Diaz, Madrid, Spain), Tristan Ferry (Infectious Diseases Department, Croix-Rousse Hospital, Hospices Civils de Lyon, Lyon, France), Thorsten Gehrke (Department of Orthopaedic Surgery, Endo-Klinik Hamburg, Specialist Clinic for Bone and Joint Surgery, Hamburg, Germany), Andor Glaudemans (Medical Imaging Center, Department of Nuclear Medicine & Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands), Benjamin Langworth (Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Rochester, MN, USA), Martin McNally (The Bone Infection Unit, Nuffield Orthopaedic Centre, Oxford University Hospitals, Oxford, UK), Andy Miller (Department of Infectious Disease, Hospital for Special Surgery, New York, NY, USA), Sandra Nelson (Division of Infectious Diseases, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA), Javad Parvizi (Department of Orthopaedic Surgery, International Joint Center, Acibadem University Hospital, Istanbul, Türkiye), Robin Patel (Division of Infectious Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA), Holger Rohde (Institute of Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg-Eppendorf, Hamburg, Germany), Thorsten Seyler (Department of Orthopaedics, Duke University Hospital, Durham, NC, USA), Irene Sigmund (Department of Orthopaedics and Trauma Surgery, Medical University of Vienna, Vienna, Austria), Alex Soriano (Department of Infectious Diseases, University of Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Hospital Clinic of Barcelona, CIBERINF Ciber in Infectious Diseases, Barcelona, Spain), and Ricardo Sousa (Department of Orthopedics, Centro Hospitalar Universitário de Santo António, Porto, Portugal) on behalf of the Leukocyte Count Synovial Fluid working group for the Unified PJI definition task force.

Author contributions

RS and MC designed the research. MSM, MC, AR, and RS conducted the screening and selection of the articles and the collection of the data. MSM and BL performed the statistical analysis. MSM wrote the first draft of the paper. MSM, MC, AS, and RS contributed to the writing of the paper. All the authors contributed to the data interpretation, revised each draft for important intellectual content, and read and approved the final manuscript.

Competing interests

Two of the (co-)authors are members of the editorial board of Journal of Bone and Joint Infection and are ExCom members (president Ricardo Sousa and vice-president Martin Clauss) of EBJIS. The other authors have no other competing interests to declare.

Ethical statement

No ethical approval was needed for systematic review.

Disclaimer

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. The authors bear the ultimate responsibility for providing appropriate place names. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Review statement

This paper was edited by Fintan Moriarty.

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