BACKGROUND: Preoperative identification of patients at risk of failing surgical treatment for periprosthetic joint infection (PJI) is imperative to allow medical optimization and targeted prevention. The purpose of this study was to create a preoperative prognostic calculator for PJI treatment by assessing a patient's individual risk for treatment failure based on many preoperative variables. METHODS: A retrospective review was performed of 1438 PJIs, treated at 2 institutions from 2000 to 2014. Minimum follow-up was 1 year. A total of 63 risk factors, including patient characteristics, microbiology data, and surgical variables were evaluated using logistic regression, in which coefficients were scaled to produce weighted scores. RESULTS: The 10 significant risk factors for PJI treatment failure were in descending order of relative weight: irrigation and debridement (30 points), history of myocardial infarction (15 points), revision surgery (11 points), presence of sinus tract (10 points), resistant organisms (9 points), ever smoker (6 points), prior surgery (2.86 points per prior operation), synovial white blood cell count (8.3 × natural log of cell count), body mass index (0.66 per increment), and erythrocyte sedimentation rate (depends on both smoking and 2 stage, as these are higher order interaction factors). The area under the curve for this risk model was 0.6904 (95% confidence interval: 0.6476-0.7331). CONCLUSION: In this large cohort study, we were able to identify risk factors and their relative weight for predicting PJI treatment failure. Some of the identified factors are indeed modifiable and should be addressed before treating a patient for PJI.
BACKGROUND: Preoperative identification of patients at risk of failing surgical treatment for periprosthetic joint infection (PJI) is imperative to allow medical optimization and targeted prevention. The purpose of this study was to create a preoperative prognostic calculator for PJI treatment by assessing a patient's individual risk for treatment failure based on many preoperative variables. METHODS: A retrospective review was performed of 1438 PJIs, treated at 2 institutions from 2000 to 2014. Minimum follow-up was 1 year. A total of 63 risk factors, including patient characteristics, microbiology data, and surgical variables were evaluated using logistic regression, in which coefficients were scaled to produce weighted scores. RESULTS: The 10 significant risk factors for PJI treatment failure were in descending order of relative weight: irrigation and debridement (30 points), history of myocardial infarction (15 points), revision surgery (11 points), presence of sinus tract (10 points), resistant organisms (9 points), ever smoker (6 points), prior surgery (2.86 points per prior operation), synovial white blood cell count (8.3 × natural log of cell count), body mass index (0.66 per increment), and erythrocyte sedimentation rate (depends on both smoking and 2 stage, as these are higher order interaction factors). The area under the curve for this risk model was 0.6904 (95% confidence interval: 0.6476-0.7331). CONCLUSION: In this large cohort study, we were able to identify risk factors and their relative weight for predicting PJI treatment failure. Some of the identified factors are indeed modifiable and should be addressed before treating a patient for PJI.
Authors: Linsen T Samuel; Assem A Sultan; Matthew Kheir; Jesus Villa; Preetesh Patel; Javad Parvizi; Carlos A Higuera Journal: Clin Orthop Relat Res Date: 2019-07 Impact factor: 4.176
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