Neutrophil to Lymphocyte Ratio as an Indicator of Periprosthetic Joint Infection: A Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Periprosthetic joint infection (PJI) after total joint arthroplasty (TJA) is a serious complication posing notable clinical implications for patients and substantial economic burdens. Neutrophil to lymphocyte ratio (NLR) is an emerging biomarker of inflammation, which may better predict PJI. The objective of this review was to evaluate NLR changes in patients with confirmed PJI, to compare NLR between an aseptic revision and a revision for PJI, and to establish whether an NLR of 2.45 is an appropriate cutoff for predicting infection. METHODS: A retrospective review of patients who underwent revision TJA for PJI at a single center between January 1, 2005, and December 31, 2018, was performed and compared with an aseptic cohort who underwent aseptic revision TJA. NLR was calculated from complete blood counts performed at index surgery and at the time of revision surgery. Receiver operating characteristic curves were analyzed, along with sensitivity, specificity, and positive and negative likelihood ratios. RESULTS: There were 89 patients included in each cohort. Mean NLR in patients who underwent revision for PJI was 2.85 (± 1.27) at the time of index surgery and 6.89 (± 6.64) at the time of revision surgery ( P = 0.017). Mean NLR in patients undergoing revision for PJI (6.89) was significantly higher than aseptic revisions (3.17; P < 0.001). DISCUSSION: In patients who underwent revision surgery for PJI, NLR was markedly elevated at time of revision compared with the time of index surgery. Because it is a cost-effective and readily available test, these findings suggest that NLR may be a useful triage test in the diagnosis of PJI. LEVEL OF EVIDENCE: Level III Diagnostic Study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".