Patient-reported impairment following TKA is reduced when a computationally simulated predicted ideal alignment is achieved

Abstract

Purpose Joint dynamics following Total Knee Arthroplasty (TKA) may influence patient-reported outcome. Simulations allow many knee alignment approaches to a single patient to be considered prior to surgery. The simulated kinematics can be matched to patient-reported outcome to predict kinematic patterns most likely to give the best outcome. This study aims to validate one such previously developed algorithm based on a simulated deep knee bend (the Dynamic Knee Score, DKS). Methods 1074 TKA patients with pre- and post-operative Computerised Tomography (CT) scans and 12-month post-operative Knee Injury and Osteoarthritis Outcomes (KOOS) Scores were identified from the 360 Med Care Joint Registry. Landmarking and registration of implant position was performed on all CT scans, and each of the achieved TKAs was computationally simulated and received a predictive outcome score from the DKS. In addition, a set of potential alternative surgical plans which might have been followed were simulated. Comparison of patient-reported issues and DKS score was evaluated in a counter-factual study design. Results Patient-reported impairment with the knee catching and squatting was shown to be 30% lower (p = 0.005) and 22% lower (p = 0.026) in patients where the best possible DKS result was the one surgically achieved. Similar findings were found relating attainment of the best tibial slope and posterior femoral resection DKS plans to patient-reported difficulty straightening the knee (40% less likely, p < 0.001) and descending stairs (35% less likely, p = 0.006). Conclusion The DKS has been shown to correlate with presence of patient-reported impairments post-TKA and the resultant algorithm can be applied in a pre-operative planning setting. Outcome optimization in the future may come from patient-specific selection of an alignment strategy and simulations may be a technological enabler of this trend. Level of evidence. III (Retrospective Cohort Study).

Active Neurodynamics at Home in Patients with Knee Osteoarthritis: A Feasibility Study

“…Computational prediction models have been designed for total knee arthroplasty to evaluate biomechanical effects [56] and patient-reported outcomes, including KOOS [57]. This simulation decreases the likelihood of impairments after total knee arthroplasty and may be useful in planning [58]. These models provide a better understanding of the likely clinical outcomes of an intervention than would exist without their use and have the potential to help in choosing the best treatment [59].…”