ABSOLUTE NEUTROPHIL COUNT: A NOVEL MARKER FOR DIAGNOSING CHRONIC PERIPROSTHETIC JOINT INFECTION FOLLOWING TOTAL HIP AND KNEE ARTHROPLASTY
Bibliographic record
Abstract
Establishing the diagnosis of periprosthetic joint infection (PJI) is challenging. To date, no gold standard test exists. Criteria used to diagnose PJI include synovial white blood cell (WBC) count and polymorphonuclear neutrophil (PMN) percentage. Elevations in these markers suggest the presence of PJI. However, it is currently unclear whether PJI is present if levels are discordant with one marker elevated and the other within normal limits. Our objectives were to determine the performance of synovial absolute neutrophil count (ANC) in the diagnosis of chronic PJI and evaluate its accuracy in predicting PJI when discordance exists between WBC and PMN. This retrospective study included 472 patients from a revision arthroplasty registry treated with revision total hip arthroplasty (THA) or revision total knee arthroplasty (TKA). All patients were evaluated with preoperative aspiration and 3 intraoperative cultures. Synovial fluid markers (WBC and PMN) and serum markers (CRP and ESR) were collected. ANC was calculated as the product of WBC count and PMN percentage. Chronic infection was defined using the 2013 version of the Musculoskeletal Infection Society (MSIS) criteria. ANC thresholds were generated using data-driven methods (upper quartile) and the literature-derived estimates. Patients with discordance were identified within the sample population. ANC was compared to other markers in the diagnosis of infection using area under the curve (AUC) analysis on receiver operating characteristic (ROC) curves, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). There were 193 patients with chronic PJI and 279 patients with no infection. Levels of all infection markers including ANC were significantly higher in chronically infected patients compared with noninfected patients. ANC performed well in predicting chronic PJI in patients treated with revision THA and TKA. Discordance between WBC count and PMN percentage occurred in approximately 12% of patients. Based on the results, we propose a diagnostic algorithm for use in patients with discordance (Figure 1B). An ANC threshold of 2983 cells/µL was shown to effectively diagnose chronic PJI in the study population of 472 patients. Further investigation is required to validate the use of ANC in diagnostic algorithms. For any figures or tables, please contact the authors directly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".