AI Article Synopsis

  • The study aimed to identify different patterns of recovery based on IKDC scores after ACL reconstruction and to find clinical predictors for these patterns.
  • 245 patients who underwent surgeries between 2014 and 2019 were analyzed using growth mixture models, revealing three distinct recovery classes.
  • Class 1 showed consistent improvement, Class 2 had gains only between years 1 and 2, and Class 3 experienced initial improvement followed by a decline; predictors for poorer recovery included revision surgery and a psychiatric history.

Article Abstract

Purpose: To determine whether subgroups of patients exist based on the rate-of-recovery pattern of International Knee Documentation Committee (IKDC) scores after anterior cruciate ligament reconstruction (ACLR) and to determine clinical predictors for these subgroups.

Methods: Patients who underwent primary or revision ACLR at a single institution from January 2014 to January 2019 were identified. Latent class growth analyses and growth mixture models (GMMs) with 1 to 6 classes were used to identify subgroups of patients based on functional rate-of-recovery patterns by use of preoperative, 1-year postoperative, and 2-year postoperative IKDC scores.

Results: A total of 245 patients who underwent ACLR were included in the analysis. A 3-class GMM was chosen as the final model after 6 different models were run. Class 1, showing improvement from preoperatively to 1-year follow-up, with sustained improvement from 1 to 2 years postoperatively, constituted 77.1% of the study population (n = 189), whereas class 2, showing functional improvement between 1- and 2-year follow-up, was the smallest class, constituting 10.2% of the study population (n = 25), and class 3, showing slight improvement at 1-year follow-up, with a subsequent decline in IKDC scores between 1- and 2-year follow-up, constituted 12.7% of the study population (n = 31). Revision surgery (P = .005), a psychiatric history (P = .025), preoperative chronic knee pain (P = .024), and a subsequent knee injury within the follow-up period (P = .011) were the predictors of class 2 and class 3 rate-of-recovery patterns. Patient demographic characteristics, graft type, and concomitant ligament, meniscus, or cartilage injury at the time of surgery were not associated with the different recovery patterns described in this study.

Conclusions: Patients may follow different rate-of-recovery patterns after ACLR. By use of the GMMs, 3 different rate-of-recovery patterns based on IKDC scores were identified. Although most patients follow a more ideal rate-of-recovery pattern, fewer patients may follow less favorable patterns. Revision surgery, a history of psychiatric illness, preoperative chronic knee pain, and a subsequent knee injury within the follow-up period were predictive of less favorable rate-of-recovery patterns.

Level Of Evidence: Level III, retrospective cohort study.

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Source
http://dx.doi.org/10.1016/j.arthro.2022.02.027DOI Listing

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