Forecasting neuromuscular recovery after anterior cruciate ligament injury: Athlete recovery profiles with generalized additive modeling

A retrospective analysis of longitudinally collected athlete monitoring data wasconducted to generate a model of neuromuscular recovery after anterior cruciateligament (ACL) injury and reconstruction (ACLR). Neuromuscular testing data in-cluding countermovement jump (CMJ) force‐time asymmetries and knee extensorstrength (maximum voluntary contractionext) asymmetries (between‐limb asymmetryindex—AI) were obtained from athletes with ACLR using semitendinosus (ST) au-tograft (n= 29; AI measurements: n= 494), bone patellar tendon bone autograft(n= 5; AI measurements:n= 88) and noninjured controls (n= 178; AI measurements:n= 3188). Explosive strength measured as the rate of torque development was alsocalculated. CMJ force‐time asymmetries were measured over discrete movementphases (eccentric deceleration phase, concentric phase). Separate additive mixedeffects models (additive mixed effects model [AMM]) were fit for each AI with amain effect for the surgical technique and a smooth term for the time since surgery(days). The models explained between 43% and 91% of the deviance in neuro-muscular recovery after ACLR. The mean time course was generated from the AMM.Comparative neuromuscular recovery profiles of an athlete with an acceleratedprogression and an athlete with a delayed progression after a serious multiligamentinjury were generated. Clinical Significance: This paper provides a new perspectiveon the utility of longitudinal athlete monitoring including routine testing to developmodels of neuromuscular recovery after ACLR that can be used to characterizeindividual progression throughout rehabilitation