NEUTROPHIL TO LYMPHOCYTE RATIO AS AN INDICATOR OF PERIPROSTHETIC JOINT INFECTION: A RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Periprosthetic joint infection (PJI) following total joint arthroplasty (TJA) is a serious complication posing significant clinical implications for patients, as well as substantial economic burdens for the entire health care system. Given the substantial economic and health-related burdens, accurate diagnosis of PJI is essential. Currently, the most accepted reference standard for PJI diagnosis is the Musculoskeletal Infection Society (MSIS) 2018 criteria. However, components of the MSIS diagnostic criteria rely upon specimens obtained intraoperatively, and thus the diagnosis of PJI may not be made until cultures have been resulted, which often occurs late in the post-operative period. The difficulty in diagnosing PJI has led to the use of screening markers such as CRP, despite its low sensitivity for detecting PJI. 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 cut-off for predicting infection. A retrospective review of patients who underwent revision TJA for PJI at a single centre between January 1, 2005 and December 31, 2018 was performed and compared with a matched cohort who underwent aseptic revision TJA. NLR was calculated from CBC performed at index surgery and time of revision surgery. Receiver operating characteristic curves were analyzed, along with sensitivity, specificity, positive and negative likelihood ratios. There were 89 patients included in each cohort with no significant demographic differences between groups. Mean NLR in patients who underwent revision for PJI was 2.85 ± 1.27 at time of index surgery and 6.89 ± 6.64 at time of revision surgery (p=0.017). NLR in patients undergoing revision for PJI (6.89) was significantly higher than aseptic revisions (3.17) (p<0.001). NLR of >2.45 at time of revision had a sensitivity of 78.65% (68.69% to 86.63%) and specificity of 38.20% (28.10% to 49.11%) for diagnosing PJI. NLR of >1.94 at time of revision had a sensitivity of 91.01% (83.05% to 96.04%) and specificity of 21.35% (13.37% to 31.31%). In patients who underwent revision surgery for PJI, NLR was significantly elevated at time of revision compared to at time of index surgery. Furthermore, septic revisions had a significantly higher NLR than aseptic revisions. NLR >2.45 had a moderate sensitivity and poor specificity, whereas NLR >1.94 had a high sensitivity for PJI. As 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 and NLR >1.94 may be a more appropriate cut-off than 2.45.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".