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A Risk Prediction Model for Redislocation Following First Time Lateral Patellar Dislocation (LPD)
Author(s):
Arendt Elizabeth (United States of America)
,
Arendt Elizabeth (United States of America)
Affiliations:
Askenberger Marie
,
Askenberger Marie
Affiliations:
Agel
,
Agel
Affiliations:
MA Julie
,
MA Julie
Affiliations:
Tompkins M.
Tompkins M.
Affiliations:
ESSKA Academy. Arendt E. 05/09/18; 209683; P12-1791 Topic: Knee
Dr. Elizabeth Arendt
Dr. Elizabeth Arendt
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Abstract
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Objectives: Our objective was to develop a model to help predict the redislocation risk after primary patella dislocations.

Methods: Patients with a history, physical exam, & magnetic resonance imaging (MRI) consistent with LPD without other significant ligamentous injury were identified. Multiple anatomic & injury variables were obtained from the MRI including tibial tubercle - trochlear groove distance, patellar tilt, trochlear depth, trochlear facet asymmetry, trochlear condyle asymmetry, lateral trochlear inclination angle, trochlear sulcus angle (SA), Insall-Salvati ratio (ISR), Caton-Deschamps index, patellotrochlear index, location & severity of chondral & MPFL injury, & open or closed/closing growth plate (GP). Demographic patient information included age & sex. Patients were followed for a minimum of 2 years post injury to determine if they had a redislocation injury. Two ongoing prospective studies with similar data collection methods assessing first time lateral patellar dislocations combined data to form this dataset
Demographic and MRI variables were compared between patients with and without patellar redislocation using group t-tests for continuous variables and Fisher's exact tests for categorical variables; stepwise logistic regression model was used to identify the statistically significant predictors of redislocation. A cutpoint for each continuous MRI variable was determined using an outcome-oriented approach; the cutpoint yielding the most significant association with the redislocation outcome in a simple logistic regression model was chosen. P-values less than 0.05 were considered statistically significant. A prediction model was then created using the statistically significant variables, and a receiver operating characteristic (ROC) curve was applied to the model to evaluate for diagnostic performance.

Results: Inclusion criteria were met by 145 patients with near equal distribution of age (GP closed 76 /open 69) and sex (81 females / 64 Males,). Of all of the variables, the statistically significant variables were GP, SA, and ISR. The identified cutpoints were SA # 154 degrees and ISR # 1.3. The predicted probability of redislocation (percent) was 6% if the patient had closed growth plates and normal SA and ISR, and 78.4% if the patient had open growth plates and abnormal SA and ISR. If two of three variables (GP, SA, ISR) were positive, the risk was between 43 and 55%. The area under the curve for the ROC curve was 0.75 indicating good diagnostic performance.

Conclusions: Our model demonstrates a high risk of redislocation with open growth plates and SA & ISR values above the cutpoints, whereas there are low rates of redislocation with the inverse findings. These variables were additive, increasing risk as more variables were (+).
This model may serve as a simple and clinically applicable means of predicting redislocation following first time LPD.

Keywords:
patella dislocation, reinjury, injury prediction
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