SimCP: A Simulation Platform to Predict Gait Performance Following Orthopedic Intervention in Children With Cerebral Palsy

Abstract

Gait deficits in cerebral palsy (CP) are often treated with a single-event multi-level surgery (SEMLS). Selecting the treatment options (combination of bony and soft tissue corrections) for a specific patient is a complex endeavor and very often treatment outcome is not satisfying. A deterioration in 22.8% of the parameters describing gait performance has been reported and there is need for additional surgery in 11% of the patients. Computational simulations based on musculoskeletal models that allow clinicians to test the effects of different treatment options before surgery have the potential to drastically improve treatment outcome. However, to date, no such simulation and modeling method is available. Two important challenges are the development of methods to include patient-specific neuromechanical impairments into the models and to simulate the effect of different surgical procedures on post-operative gait performance. Therefore, we developed the SimCP framework that allows the evaluation of the effect of different simulated surgeries on gait performance of a specific patient and includes a graphical user interface (GUI) that enables performing virtual surgery on the models. We demonstrated the potential of our framework for two case studies. Models reflecting the patient-specific musculoskeletal geometry and muscle properties are generated based solely on data collected before the treatment. The patient's motor control is described based on muscle synergies derived from pre-operative EMG. The GUI is then used to modify the musculoskeletal properties according to the surgical plan. Since SEMLS does not affect motor control, the same motor control model is used to define gait performance pre- and post-operative. We use the capability gap (CG), i.e., the difference between the joint moments needed to perform healthy walking and the joint moments the personalized model can generate, to quantify gait performance. In both cases, the CG was smaller post- then pre-operative and this was in accordance with the measured change in gait kinematics after treatment.

Keywords: capability gap; cerebral palsy; muscle synergies; orthopedic interventions; single event multilevel surgery; subject specific model.

Figure 1

Framework description. Data collected on the patient in the pre-operative phase are used as input to generate a pre-operative model with personalized musculoskeletal geometry and muscle parameters. Next, the motor control of the patient is modeled, using synergy decomposition analysis, based on pre-operative EMG and gait analysis data. Performing virtual surgeries on the pre-operative model generates a post-operative model. This takes into account the changes in the musculoskeletal parameters induced by the performed surgery. The motor control, on the other hand, is not affected by the surgery and the same motor control model computed in the pre-operative condition is used also for the post-operative condition. The gait performance of the patient can be computed in both the pre- and simulated post-operative conditions. In both cases, a synergy constrained optimization tries to match the joint moments relative to a desired motion while taking into account the constraints imposed by the model and by the motor control. The differences between the moments generated by the model and the desired moments are the capability gap, defining the gait performance.

Figure 2

Framework components. (A) Muscle transfer surgery. Used to change the path of a muscle. The lines of action of other muscles are visualized as guide to help during the transfer. (B) Extension and Derotation osteotomy surgery. Two sets of scrollbars define the pose of the two cutting planes (red and blue in the left side figures) that define the wedge of bone to be removed. After wedge removal, the two segments are brought in contact and it is possible to rotate and translate the distal part to correct for abnormal anteversion angles. (C) Patella advancement surgery. It is possible to define the new length of the patella ligament and/or move its insertion on the tibia, during the operation the ligament in the new configuration is shown as a red cylinder.

Figure 3

Capability gap and report on muscle operating lengths. (A) Capability gap computed for the two representative patients, before and after the virtual surgery. The continuous black lines represent the joint moments required for the model to reach the desired kinematics [i.e., typically developed (TD) gait cycle]. Dotted black lines represent the moment exerted by the model. The light gray patches represent the capability gap. (B) Information about the operating conditions of the muscles. Blue dots represent the time instants in which muscles are active at short lengths (activation >0.25 and normalized fiber length smaller than 0.6), red dots the intervals in which muscles are stretched, and exert an excessive passive force (>0.5 times the maximum isometric force).

Figure 4

Effect of treatment options on the capability gap. Data reported are relative to the simulated post-operative condition of the right leg of Patient 2. Capability gap is computed after rectus femoris transfer, femur extension and derotation osteotomy, and patella advancement interventions but before performing any muscle release intervention. The continuous black lines represent the joint moments required for the model to reach the desired kinematics [i.e., typically developed (TD) gait cycle].

Figure 5

Effect of motor control on capability gap. Capability gap computed relative to the pre-operative condition for the right legs of both patients (on the left side is Patient 1, on the right is Patient 2). The continuous black lines represent the joint moments required for the model to reach the desired kinematics [i.e., typically developed (TD) gait cycle]. When synergies are taken into account the muscle activations are computed from the pre-operative weight vectors, which define the muscle coordination specific to the impairment; when synergies are not taken into account muscles are activated selectively, thus simulating an unimpaired motor control.

Figure 6

Predicted and measured changes in patient performance. Data on the left belong to Patient 1, on the right to Patient 2. (A) Kinematics of the two representative cases measured in both pre- and post-operative conditions (mean and standard deviation). Black dotted line is the average data from typically developed (TD) children used as reference for the computation of the capability gap (CG). (B) Changes in the predicted capability gap, and in the root mean square differences (computed with respect to the TD kinematics) between the pre- and post-operative condition.

Figure 7

Effect of tuning of muscle parameters. On the top row are plotted the values of the normalized muscle fiber lengths of all the tuned muscles throughout the gait cycle. The two horizontal dotted lines represent the constraints imposed during the optimization process. On the bottom row are plotted the activations of two representative muscles (semitendinosus and gastrocnemius medialis), before and after the optimization, as well as the experimental EMG signal.