Recovery after anterior cruciate ligament reconstruction is optimal about 85% of the time. Revision surgery, psychiatric history, preoperative chronic knee pain, and subsequent knee injury are associated with suboptimal recovery patterns. Sophisticated growth models can analyze patient recovery trajectories. Growth mixture models (GMM) treat a whole cohort as a single group and characterize that group over time, for example, over the course of knee injury and subsequent recovery after surgical reconstruction. Latent class growth analysis is a subcategory of GMM that sorts the cohort into subgroups and allows analysis regarding groups having, for example, standard, delayed, and suboptimal recoveries. This theoretically allows a physician to anticipate which patients are likely to follow a suboptimal trajectory of recovery, to track that recovery based on the model, and to form a treatment plan accordingly.
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http://dx.doi.org/10.1016/j.arthro.2022.05.001 | DOI Listing |
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