Identification of preoperative predictive markers of periprosthetic joint infection in presumed aseptic revisions of total hip arthroplasty: a retrospective observational study.

INTRODUCTION

Periprosthetic joint infections (PJIs) occur at a rate of 1-2% in primary arthroplasties and 4% in revisions, posing a significant healthcare challenge. PJI diagnosis is hindered by absence of a universally accepted diagnostic standard. The primary outcome was to develop a predictive model to identify patients at risk of developing unforeseen PJI subsequent to presumed aseptic revision of total hip arthroplasty (THA). The secondary aim was to determine the reintervention-free survival rate among aseptic patients and those presenting unexpected positive cultures.

METHODS

This study included all ascertained septic and presumed aseptic patients without mechanical causes of THA failure and with sufficient clinical and laboratory data. Patients with incomplete anamnestic data, mechanical loosening related causes, rheumatologic conditions, or immunosuppression were excluded. Univariable and multivariable logistic regression analyses identified preoperative predictive markers for unexpected hip PJI.

RESULTS

283 inpatients from a high volume referral center were recruited from 2016 to 2019. The predictive preoperative parameters related with hip PJI were: presence of comorbidities (OR: 0.28; 95% CI 0.10-0.78), erythrocyte sedimentation rate (OR: 1.02; 95% CI 1.00-1.03), leukocyte count (OR: 1.18; 95% CI 1.02-1.36) and prothrombin time (OR: 1.42; 95% CI 1.02-1.98). Gender, age, body mass index (BMI) and previous surgery were not significantly related with unexpected PJI. Overall, the Kaplan-Meier analysis estimated a reoperation-free survival rate at 36 months of 96% (CI 95%) for the aseptic and 98% (CI 95%) for the unexpected group; however, the relation between the two survival curves was not significant (p = 0.086).

CONCLUSIONS

Erythrocyte sedimentation rate, leukocyte count, and increased prothrombin time were risk factors for PJI, whereas comorbidities were associated with a lower risk. Further research is needed to understand this association fully. Larger datasets are recommended to explore the complex interplay between risk factors and PJI development.