Externally validated treatment algorithm acceptably predicts nonoperative treatment success in patients with anterior cruciate ligament rupture.

Abstract

This study aims to develop and externally validate a treatment algorithm to predict nonoperative treatment success or failure in patients with anterior cruciate ligament (ACL) rupture. Data were used from two completed studies of adult patients with ACL ruptures: the Conservative versus Operative Methods for Patients with ACL Rupture Evaluation study (development cohort) and the KNee osteoArthritis anterior cruciate Ligament Lesion study (validation cohort). The primary outcome variable is nonoperative treatment success or failure. Potential predictor variables were collected, entered into the univariable logistic regression modeland then incorporated into the multivariable logistic regression model for constructing the treatment algorithm. Finally, predictive performance and goodness-of-fit were assessed and externally validated by discrimination and calibration measures. In the univariable logistic regression model, a stable knee measured with the pivot shift test and a posttrauma International Knee Documentation Committee (IKDC) score <50 were predictive of needing an ACL reconstruction. Age >30 years and a body mass index > 30 kg/m2 were predictive for not needing an ACL reconstruction. Age, pretrauma Tegner score, the outcome of the pivot shift testand the posttrauma IKDC score are entered into the treatment algorithm. The predictability of needing an ACL reconstruction after nonoperative treatment (discrimination) is acceptable in both the development and the validation cohort: area under the curve = resp. 0.69 (95% confidence interval [CI]: 0.58-0.81) and 0.68 (95% CI: 0.58-0.78). This study shows that the treatment algorithm can acceptably predict whether an ACL injury patient will have a(n) (un)successful nonoperative treatment (discrimination). Calibration of the treatment algorithm suggests a systematical underestimation of the need for ACL reconstruction. Given the limitations regarding the sample size of this study, larger data sets must be constructed to improve the treatment algorithm further. Level II.