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

A retrospective analysis of longitudinally collected athlete monitoring data was conducted to generate a model of neuromuscular recovery after anterior cruciate ligament (ACL) injury and reconstruction (ACLR). Neuromuscular testing data including countermovement jump (CMJ) force‐time asymmetries and knee extensor strength (maximum voluntary contraction(ext)) asymmetries (between‐limb asymmetry index—AI) were obtained from athletes with ACLR using semitendinosus (ST) autograft (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 also calculated. CMJ force‐time asymmetries were measured over discrete movement phases (eccentric deceleration phase, concentric phase). Separate additive mixed effects models (additive mixed effects model [AMM]) were fit for each AI with a main 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 neuromuscular recovery after ACLR. The mean time course was generated from the AMM. Comparative neuromuscular recovery profiles of an athlete with an accelerated progression and an athlete with a delayed progression after a serious multiligament injury were generated. Clinical Significance: This paper provides a new perspective on the utility of longitudinal athlete monitoring including routine testing to develop models of neuromuscular recovery after ACLR that can be used to characterize individual progression throughout rehabilitation.