JBJI Journal of Bone and Joint Infection JBJIJ. Bone Joint Infect. 2206-3552 Copernicus Publications Göttingen, Germany 10.5194/jbji-10-165-2025Differential synovial fluid white blood cell count for the diagnosis of chronic peri-prosthetic joint infection – a systematic review and meta-analysisDifferential synovial WBC count in chronic PJI Sabater-MartosMarta sabaterms@gmail.com https://orcid.org/0000-0002-9350-3648 ClaussMartin https://orcid.org/0000-0002-1598-2209 RibauAna SousaRicardo on behalf of the Leukocyte Count Synovial Fluid working group for the Unified PJI definition task force Orthopedic and Traumatology Department, Clínic Barcelona. Carrer Villarroel 170, 08036 Barcelona, Spain Center for Musculoskeletal Infections (ZMSI), University Hospital Basel, Basel, Switzerland Department for Orthopaedics and Trauma Surgery, University Hospital Basel, Basel, Switzerland Unidade Local de Sáude do Médio Ave, Famalicao, Portugal Porto 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)14May2025 10 3 165184 10January2025 27February2025 5March2025 Copyright: © 2025 Marta Sabater-Martos et al. 2025 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/jbji-10-165-2025.htmlThe full text article is available as a PDF file from https://jbji.copernicus.org/articles/jbji-10-165-2025.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 > 2600 cells per microlitre and PMN > 70 %. Rule-in thresholds (specificity >95 %) were WBC count ≥ 3000 cells per microlitre and PMN ≥ 75 %, while rule-out thresholds (sensitivity > 95 %) were WBC count ≤ 1500 cells per microlitre 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. Thresholds of WBC count < 1500 and >3000 cells per microlitre and PMN < 65 % and >75 % provide reliable negative and positive predictive values. A standardized diagnostic framework is essential for addressing remaining controversies and ensuring consistent interpretation across clinical settings.

 Introduction

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.

 Material and methods

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

Search 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).

 Inclusion 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).

PRISMA flow diagram.

 Data 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.

 Risk 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.

 Information 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).

 Cutoff 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 and presented them in a scatterplot diagram. The optimal cutoff was determined by using median values. We obtained optimal cutoffs for combined total knee arthroplasty (TKA) and total hip arthroplasty (THA) as well as for TKA and THA individually.

To estimate an ideal rule-in/rule-out threshold, we decided to use only specificity or sensitivity values of >95 %. We used specificity or sensitivity vs. extracted cutoffs and presented them in a scatterplot diagram, and the rule-in/rule-out cutoff was determined using median values. We obtained the rule-in/rule-out value for the combined TKA and THA. There were not enough data to extract a rule-in value for TKA and THA individually.

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.

 Results Search 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).

 Risk 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.

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.

 Characteristics 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.

General characteristics of primary studies.

Author Year Type of study Infected Joints PJI Special condition WBC Sensitivity PMN Sensitivity Cutoff Reporting patients/ reference count and cutoff and determination funds total standard cutoff specificity specificity Bernd Fink (Fink et al., 2021 Prospective 112/434 Hips and ICM 2018 No 1400 90.2 %/91.9 % NA NA Youden NM 2021) knees 500 98.8 %/91 % Youden 1000 93.8 %/92.9 % Youden 1500 90.2 %/95.7 % Youden 2000 86.6 %/91.2 % Youden 3000 80.6 %/94.9 % Youden Giovanni Balato (Balato et 2018 Retrospective 31/167 Knees MSIS No 3000 80.6 %/91.2 % 80 % 83.9 %/94.9 % Youden NM al., 2018a) Carol Strahm 2018 Retrospective 13/19 Shoulders IDSA No 12 200 92 %/100 % 54 % 100 %/75 % Youden NM (Strahm et al., 2018) Darius M. Thiesen 2019 Retrospective 2/31 Ankles MSIS No NA 100 %/37.5 % NA 100 %/77.8 % Youden No (Thiesen et al., funding 2019) Elena De Vechhi (De 2018 Retrospective 32/66 Hips and MSIS No 3000 93.7 %/91.2 % NA NA Pre-determined NM Vecchi et al., 2018) knees 1600 100 %/82.3 % TWC Casper Dijkman (Dijkman 2020 Retrospective 13/89 Hips and MSIS No 2575 92 %/84 % NA NA Youden NM et al., 2020) knees 1865 100 %/97 % Youden Erik Schiffner 2020 Retrospective 32/149 Knees MSIS No 3000 84 %/96 % 65 % 84 %/94 % Pre-determined NM (Schiffner et al., 2020) Hai Wang (Wang et 2021 Prospective 37/93 Hips and knees MSIS No NA NA 69.96 % 94.6 %/92.8 % Youden NM al., 2021b) Hai Wang (Wang et 2021 Prospective 39/97 Hips and MSIS No NA NA 69.8 % 84.6 %/74.1 % Youden NM al., 2021a) knees Heckman 2020 Prospective 16/78 Hips and MSIS No 3000 100 %/100 % 80 % 81.3 %/96.8 % Pre-determined NM Nathanael Heckmann knees (pre- (pre- (Heckmann et lavage) lavage) al., 2020) 3000 62.5 %/98.4 % 80 % 75 %/95.2 % Pre-determined (post- (post- lavage) lavage) Isaac Majors (Majors 2019 Prospective 32/59 Knees MSIS No 1804 %/81 % 86 % 82.4 %/73.7 % Youden NM and Jagadale, 2019) Ivy (Ivy et al., 2021) 2021 Prospective 18/99 Knees MSIS No 1700 83.3 %/93.8 % 65 % 83.3 %/94.9 % Pre-determined NM Hips 3000 83.3 %/93.8 % 80 % 83.3 %/94.9 % Pre-determined

Continued.

Author Year Type of study Infected Joint PJI Special condition WBC Sensitivity PMN Sensitivity Cutoff Reporting patients/ reference count and cutoff and determination funds total standard cutoff specificity specificity Jana van den Kieboom (van 2021 Retrospective 41/144 Hips and MSIS Fracture 3000 87 %/77.9 % 80 % 79 %/63.2 % Pre-determined NM den Kieboom et al., knees 4552 86.4 %/85.3 % 79.5 % 73.7 %/63.2 % Q point 2021) Alexander Lazarides 2019 Retrospective 16/87 Hips and MSIS Immunosuppression 1293 93.3 %/84.4 % 65 % 81.3 %/87.3 % Youden Yes (Lazarides et al., knees 2019) Michael M. Kheir (Kheir et 2018 Retrospective 549/1202 Hips and ICM 2018 No 2659 85 %/93 % 64.5 % 91 %/86 % Youden No al., 2018) knees 3000 83 %/94 % 80 % 78 %/93 % Pre-determined funding Maxime Huard (Huard et 2020 Prospective 41/138 Hips and MSIS No 3000 87.7 %/95.7 % NA NA Pre-determined NM al., 2020) 50/138 knees IDSA 3000 70 %/94.2 % Pre-determined 68/138 EBJIS 3000 55.8 %/97 % Pre-determined 35/138 Positive 881 80 %/91 % Youden culture Positive 3077 91 %/85 % Youden culture Vikas V. Patel (Patel et al., 2021 Retrospective 20/87 Shoulders ICM 2018 No 3000 30 %/100 % 80 % 20 %/100 % Pre-determined NM 2021) Leilei Qin (Qin et al., 2020 Prospective 25/50 Hips and MSIS No NA NA 69.79 % 92 %/80 % Youden NM 2020) knees Alisina Shahi (Shahi et al., 2017 Retrospective 822/4462 Hips and MSIS No 3000 85.8 %/83 % 80 % 85.8 %/80.8 % Pre-determined NM 2017) knees Katyayini Sharma (Sharma et 2020 Retrospective 50/107 Hips and MSIS No 1100 89 %/98 % 72 % 92 %/91 % Pre-determined NM al., 2020) knees Noam Shohat (Shohat et 2018 Retrospective 567/1220 Hips and MSIS No 3000 81.8 %/95.1 % 80 % 77.7 %/94.3 % Pre-determined NM al., 2018) knees No 2533 83.4 %/93.3 % 73 % 86.8 %/89.9 % Youden Inflammation 3000 88.2 %/78.6 % 80 % 93.7 %/84.6 % Pre-determined Inflammation 2683 91.2 %/78.6 % 72 % 100 %/82.7 % Youden Mesut Tahta (Tahta et al., 2019 Prospective 17/38 Knees MSIS Inflammation 2347 84.6 %/76.4 % 76.7 % 79.2 %/90.3 % Youden NM 2019) Young-Min Kwon (Kwon et 2020 Retrospective 11/89 Hips MSIS ATRLs 2144 93 %/84 % 84 % 84 %/86 % Youden No al., 2020) 13 796 100 %/100 % 82 % 93 %/82 % Youden funding Akos Zahar (Zahar et al., 2018 Retrospective 134/337 Hips and MSIS No 2582 80.6 %/85.2 % 66.1 % 80.6 %/83.3 % Youden NM 2018) knees 1630 83.5 %/82.2 % 60.5 % 80.3 %/77.1 % Youden Hips and 3063 78.1 %/80 % 66.1 % 82.2 %/82.4 % Youden knees

Continued.

Author Year Type of study Infected Joints PJI Special condition WBC Sensitivity PMN Sensitivity Cutoff Reporting patients/ reference count and cutoff and determination funds total standard cutoff specificity specificity Zeyu Zhang (Zhang et al., 2020 Retrospective 21/63 Hips and MSIS No 3000 85.7 %/83.3 % 65 % 90.5 %/85.7 % Youden NM 2020) knees Jiaxing Huang (Huang et 2022 Prospective 43/99 Hips and MSIS No 3000 85.7 %/83.3 % 75 % 95.3 %/78.6 % Youden NM al., 2022) knees Christian Klemt (Klemt et 2023 Retrospective 191/464 Hips ICM 2018 No 3000 86.9 %/79.1 % 80 % 87.9 %/78.7 % Pre-determined NM al., 2023) Julian E. Dilley (Dilley et 2023 Retrospective 321/730 Hips and MSIS No 5600 71.8 %/85.6 % 82 % 81 %/77.5 % Youden NM al., 2023) knees 3000 76 %/80.2 % 80 % 82.2 %/76 % Pre-determined Y. Gramlich 2023 Prospective 111/405 Hips, ICM 2018 No 2478.9 87.7 %/88.1 % 67 % 86 %/88.8 % Youden NM Yves Gramlich knees, 1500 91.5 %/75 % 65 % 86 %/88.8 % (Gramlich et al., and 1750 89.6 %/78.7 % 70 % 82.6 %/90 % 2023) shoulders 2000 89.6 %/83.4 % Knees 2500 86.6 %/88.1 % Hips 3000 82.1 %/91 % 80 % 66.3 %/96.5 % Pre-determined 3085.7 86.7 %/91.8 % 67.01 % 87.5 %/88.2 % Youden 2267.8 87.5 %/83.5 % 67.04 % 81.5 %/94.1 % Youden Feng Chih Kuo (Kuo 2022 Retrospective 42/76 Hips and ICM 2018 No 3000 68 %/94 % 70 % 71 %/84 % Pre-determined No et al., 2022) knees funding Yuanquing Cai (Cai et al., 2023 Retrospective 46/74 Hips and MSIS No 3000 63 %/74 % 80 % 78.2 %/71.4 % Pre-determined NM 2023) knees Igor Lazic (Lazic et al., 2022 Prospective 17/32 Hips and EBJIS Fracture 6130 41 %/100 % 79.5 % 71 %/100 % Youden NM 2022a) knees Taner Karlidag 2024 Retrospective 50/144 Knees ICM 2018 No 2553.5 92 %/91.4 % 81.7 % 88 %/93.6 % Youden No (Karlidag et al., 59/116 Hips 3512.5 83 %/85.9 % 83.6 % 86.4 %/93.6 % Youden funding 2024) Colin M. Baker (Baker et 2022 Retrospective 194/621 Hips and ICM 2018 No 3000 85.7 %/100 % 80 % 92.6 %/96 % Pre-determined NM al., 2022) knees 162/520 Knees 3000 92 %/99 % 80 % 86.4 %/99.2 % Pre-determined 32/68 Hips 3000 92.8 %/98.5 % 80 % 85.3 %/99.4 % Pre-determined Daniel Timothy Burchette 2024 Retrospective 185/362 Hips and ICM 2018 No 2581 74.3 %/88 % 75.4 % 68.8 %/97.6 % Youden NM (Burchette et al., knees 2024) 111/195 Hips 2373 81 %/77.8 % 73.4 % 72.2 %/98.1 % Youden 73/165 Knees 4058 66.7 %/97.2 % 75.4 % 64.8 %/97.2 % Youden Xudong Su (Su et al., 2023 Retrospective 30/60 Hips and MSIS No NA NA 51.1 % 96.7 %/73.3 % Youden NM 2023) knees Hao Li (Li et al., 2023 Retrospective 48/90 Hips and ICM 2018 No 3000 74 %/94.4 % 70 % 79.6 %/79.6 % Pre-determined NM 2023) knees

Continued.

Author Year Type of study Infected Joints PJI Special condition WBC Sensitivity PMN Sensitivity Cutoff Reporting patients/ reference count and cutoff and determination funds total standard cutoff specificity specificity Cristoph Theil (Theil et al., 2023 Retrospective 55/108 Knees MSIS ALTRs 1200 94.5 %/75.5 % 63 % 85.5 %/73.6 % Youden NM 2024) 1500 89.1 %/79.2 % 65 % 85.5 %/73.6 % Pre-determined 70 % 76.3 %/77.4 % Pre-determined 3000 70.1 %/94.3 % 80 % 60 %/81.1 % Pre-determined Sara Elisa Diniz 2023 Retrospective 36/102 Hips and EBJIS No 1470 91.7 %/92.4 % 62.5 % 88.9 %/83.9 % Youden NM (Diniz et al., knees 2645 77.8 %/98.5 % 64.4 % 86.1 %/88.7 % Youden 2023) 3280 77.8 %/100 % 79.5 % 75 %/98.4 % Youden Mohammed F. Alkadhem 2024 Retrospective 19/137 Hips and EBJIS No 1500 74 %/71 % 65 % 89 %/78 % Pre-determined NM (Alkadhem et al., knees 3000 75 %/86 % 80 % 89 %/85 % Pre-determined 2024) Knees 3000 100 %/87 % 80 % 100 %/85 % Pre-determined Hips 3000 58 %/84 % 80 % 91 %/84 % Pre-determined Sara Elisa Diniz (Streck 2022 Retrospective 21/35 Shoulders ICM 2018 No 700 85.7 %/100 % NA NA Youden NM et al., 2022b) Igor Lazic (Lazic et al., 2022 Prospective 14/30 Hips and EBJIS Fracture 4550 40 %/100 % 79 % 30 %/100 % Youden NM 2022b) knees Leilei Qin (Qin et al., 2022 Prospective 35/70 Hips and MSIS Inflammation NA NA 69.8 % 88.6 %/80 % Youden NM 2022) knees Yulai Wang (Wang et 2023 Retrospective 164/348 Hips and MSIS No 2673 80.5 %/96.4 % 62 % 92.8 %/92.9 % Youden Yes al., 2023) knees Inflammation 3654 80.5 %/94.4 % 65.9 % 85.4 %/77.8 % Youden Ali Levent (Levent et 2021 Retrospective 109/260 Hips and ICM 2018 No 3000 87.2 %/88.1 % 70 % 89.9 %/84.1 % Pre-determined No al., 2021) knees 2950 88.1 %/88.1 % 77.8 % 89.9 %/91.4 % Youden funding Cristoph Theil (Theil et al., 2022 Prospective 30/92 Hips and ICM 2018 No 3000 90 %/88 % 80 % 73 %/98 % Pre-determined NM 2022) knees Martra Bottagisio 2023 Prospective 12/55 Hips and ICM 2018 No 6740 85.7 %/97.1 % 85 % 85.7 %/97.1 % Youden NM (Bottagisio et al., knees 2023) Yi Ren (Ren et al., 2020 Retrospective 63/NA Hips and Cultures Inflammation 1948 83.3 %/72.7 % 85.3 % 83.3 %/73 % Youden NM 2022) knees Steven Denyer (Denyer 2023 Retrospective 124/283 Knees IDSA No 3000 89.9 %/89.7 % 80 % 89.5 %/100 % Pre-determined NM et al., 2023) 122/289 Hips No 3000 82.5 %/80 % 80 % 82.5 %/62.5 % Pre-determined Laura Elisa Streck (Streck 2021 Retrospective 15/31 Shoulders IDSA No 2800 86.7 %/87.5 % NA NA Youden NM et al., 2022a) Mehdi S. Salymi (Salimy 2023 Retrospective 116/207 Hips and ICM 2018 Antibiotic 2339 95.2 %/93.3 % 83 % 90.5 %/80 % Youden No et al., 2023) knees funding Wayne B. Cohen-Levy 2002 Retrospective 20/109 Knees MSIS + No 2695 94 %/90 % 52.5 % 94 %/93 % Youden No (Cohen-Levy et al., ICM 2018 funding 2022)

Continued.

Author Year Type of study Infected Joints PJI Special condition WBC Sensitivity PMN Sensitivity Cutoff Reporting patients/ reference count and cutoff and determination funds total standard cutoff specificity specificity Asa Nilsdotter 2007 Prospective 25/85 Hips Symptoms No 1700 86 %/92 % NA NA Youden NM (Nilsdotter- Cultures Augustinsson et al., Sinus 2007) Giovanni Balato (Balato et 2017 Prospective 16/51 Knees MSIS No 3000 75 %/91.4 % 80 % 75 %/97 % Pre-determined NM al., 2018b) Benjamin Zmistowski 2013 Retrospective 73/150 Knees Cultures No 3000 93 %/94 % 75 % 93 %/83 % Youden NM (Zmistowski et al., Synovial 2012) fluid Pieter Berger (Berger et 2017 Prospective 34/121 Hips and MSIS No 3000 89.3 %/96.2 % 80 % 89.3 %/92.2 % Pre-determined NM al., 2017) knees Peter N. Chalmers (Chalmers 2015 Prospective 91/433 Knees Cultures Fracture 4450 90 %/98.5 % 73 % 89.4 %/93.6 % Youden NM et al., 2014) Sinus Synovial fluid Cara A. Cipriano (Cipriano 2012 Prospective 165/871 Hips and Cultures No 3450 91 %/93 % 78 % 95.5 %/87.3 % Youden No et al., 2012) knees Synovial Inflammation 3444 88.2 %/80 % 75 % 100 %/93 % Youden funding fluid Alex Dinnen (Dinneen et 2013 Prospective 34/75 Hips and Cultures No 1590 89.5 %/91.3 % 65 % 89.7 %/86.6 % Youden NM al., 2013) knees Histology Hips 1425 NA 65 % NA Youden Knees 1715 NA 54 % NA Youden Eli Ghanem 2008 Retrospective 91/128 Knees Cultures No 1760 95.6 %/68.6 % 73 % 95.6 %/73 % Youden Yes (Ghanem et al., X-rays 2008b) Purulence CRP Eli Ghanem 2008 Retrospective 161/429 Knees Abscess No 1100 90.7 %/88.1 % 64 % 95 %/94.7 % Youden Yes (Ghanem et al., Sinus 2008a) Cultures Purulence Nathanel Heckmann 2023 Retrospective 21/172 Knees ICM 2018 No 3000 85.7 %/100 % 80 % 79.2 %/99.1 % Pre-determined NM (Heckmann et al., 2023) Carlos A. Higuera (Higuera et 2017 Retrospective 79/453 Hips MSIS No 3966 89.5 %/91.2 % 80 % 92.1 %/85.8 % NA No al., 2017) funding Joshua Bingham 2014 Retrospective 19/61 Hips and MSIS No 1700 95 %/85 % NA NA Youden Yes (Bingham et al., knees 2014)

Continued.

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

NA means “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).

Proposed interpretation cutoffs for joints other than TKA and THA.

WBC count (cells per microlitre) PMN (%) 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.

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

 Meta-analysis

WBC count showed a summary DOR of 58.38 (95 % CI: 48.48–70.32), an AUC of the sROC of 0.952 (SE: 0.0042), and a Q index of 0.893. PMN proportion showed a summary DOR of 43.17 (95 % CI: 35.31–52.79), an AUC of the sROC of 0.941 (SE: 0.057), and a Q index of 0.879 (Fig. 3).

sROC for WBC count and PMN % in all joints.

The heterogeneities (I2) of sensitivity and specificity for WBC count were 99.3 % and 88.3 %, respectively, and for PMN % they were 93.9 % and 88.5 %, 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: 0.0082), 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 0.936 (SE: 0.011), and a Q index of 0.882. In hips, results showed a WBC count summary DOR of 33.58 (95 % CI: 21.44–52.63), an AUC of the sROC of 0.952 (SE: 0.019), a Q index of 0.893, a PMN proportion summary DOR of 27.12 (95 % CI: 16.22–45.35), an AUC of the sROC of 0.954 (SE: 0.012), and a Q index of 0.897 (Figs. 4 and 5).

sROC for WBC count and PMN % in knees.

sROC for WBC count and PMN % in hips.

Summary of the proposed cutoffs.

WBC count (cells per microlitre) PMN (%) Optimal1 Rule-out2 Rule-in3 Optimal1 Rule-out2 Rule-in3 Chronic PJI* 2600 1640 2700 70 % 66.5 % 74.5 % Hip 2696 – – 70.5 % – – Knee 2537 – – 67 % – –

* Not enough information available to recommend cutoffs for joints other than TKA and THA. 1 Defined by the best combination of sensitivity and specificity. 2 Defined by a sensitivity of >95 %. 3 Defined by a specificity of >95 %.

 Cutoff 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 per microlitre and 53 % and 86 % PMN. The optimal cutoff values were 2537 cells per microlitre for knees, 2696 cells per microlitre for hips, and 70 % PMN for both (Fig. 6, Table 3).

Scatterplot Youden cutoff WBC count and PMN.

Rule-in cutoffs of ≥2700 cells per microlitre and ≥75 % consistently demonstrated over 95 % specificity, and rule-out cutoffs of ≤1640 cells per microlitre and ≤66 % exceeded 95 % sensitivity for both hips and knees. There was not enough information to recommend a different cutoff for joints other than TKA and THA (Table 3).

When sub-dividing groups between more sensitive PJI definition criteria (ICM, EBJIS, and IDSA; Group 1) and other PJI definition criteria (MSIS and clinical outcome; Group 2), we could see that optimal cutoffs were very similar between them. However, when we applied a rule-in/rule-out calculation, Group 2 presented insufficient data in some cases due to the small number of papers with a specificity or sensitivity of >95 % (Table 4).

Cutoffs in the modern or more sensitive PJI definition (Group 1) and in the older PJI definition (Group 2).

WBC count (cells per microlitre) PMN (%) Optimal1 Rule-out2 Rule-in3 Optimal1 Rule-out2 Rule-in3 Group 1 (ICM, EBJIS, and IDSA) 2530 982 3140 73.7 % 54 % 75.4 % Group 2 (MSIS and clinical outcome) 2413 1813 2193* 68.5 % 66.4 % 69.7 %

1 Defined by the best combination of sensitivity and specificity. 2 Defined by a sensitivity of >95 %. 3 Defined by a specificity of >95 %. * No papers with >95 % specificity in THA, leaving a reduced sample to compare with other calculations.

 Confounding 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).

The cutoff variability and medians of confounding conditions are presented in Table 5. 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.

Sensitivity and specificity for special conditions.

Summary findings of confounding conditions.

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

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

 Discussion

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 2600 cells per microlitre and 70 % 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 per microlitre (sensitivity 88.7 %, specificity 98.5 %) and 75 % PMN (sensitivity 77 %, specificity 97.8 %) 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 at >95 %. 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 per microlitre (sensitivity 100 %, specificity 88 %) and 65 % PMN (sensitivity 95.8 %, specificity 78.6 %).

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.

 Conclusion

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 per microlitre and proportions of PMN < 65 % and >75 % seem to have very good negative and positive predictive values, respectively. 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-10-165-2025-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 performed the statistical analysis and 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. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

Acknowledgements

The authors would like to express their gratitude to Benjamin Langworthy at the Division of Biostatistics & Health Data Science at the University of North Carolina, Chapel Hill, for his advice on how to proceed with data interpretation.

Review statement

This paper was edited by Anna Stefánsdóttir and reviewed by two anonymous referees.

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