Predicting Total Knee Arthroplasty (TKA) Outcomes with Patient-Specific Modeling

Developing a Pre-Surgical Tool

Although TKA is a generally successful procedure, some patients experience post-operative problems, with poorly positioned implants being one of the main causes. Improving our understanding of knee dynamics for specific patients helps, and how they interact with implants is important for creating data to improve pre-surgical planning. Virtual preoperative trials of the resulting kinematics of a given implant selection and component alignment can look at simulation and comparison of different alignment plans, which when linked to patient-reported outcomes (PROs), might allow a predictive algorithm to select plans for future patients.

The full workflow is briefly summarized below:

• Image data on different TKA patients was collected using a computed tomography (CT) protocol, including a cohort with pre-and-post-surgical scans and outcome surveys, and a randomly chosen sample of knees with pre-operative scans but not necessarily post-operative scans
• 3D reconstructed patient femora and tibia were generated from the image data in Simpleware software using a semi-automated process, with anatomical landmarks placed to capture patient-specific soft tissue attachments and bone axes
• For the first cohort, pre-operative bones and 3D implant files were registered to post-operative CT scans segmented using Simpleware software following TKA
• Simulations were run on the models for a multibody simulation of a deep knee bend, and results used in a machine learning algorithm run to predict attainment of a Patient Acceptable Symptom State (PASS) score in captured 12-month post-operative Knee Injury and Osteoarthritis Outcome Scores (KOSS)
• For the second cohort, pre-operative patient data was used with the predictive model to assess its performance for clinical planning
• The generated predictive algorithm (Dynamic Knee Score) contained different features, with the Area Under the Curve (AOC) for predicting attainment of the PASS KOOS score being 0.64

Future Impact

The study found that the method has potential as a powerful additional tool to TKA surgical decision-making processes, but needs to be used in addition to existing techniques and within the context of non-surgical factors not covered by the project. From this approach, applications might include helping to select between otherwise reasonable surgical alignment plans to identify optimized outcomes. Considered alongside the growing use of image-based surgical planning workflows for the hip and other anatomies, as well as the development of Machine Learning-based approaches to speeding up common tasks, 360MedCare's work shows strong promise for future applications and we look forward to seeing their progress as they continue to grow.

Learn More

• Read the full article Can TKA outcomes be predicted with computational simulation? Generation of a patient specific planning tool published in The Knee.
• Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution 4.0 International license
• Read about Simpleware solving segmentation challenges for Personalized Knee Implants