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Predicting postoperative gait in cerebral palsy
Omar A Galarraga C1, Vincent Vigneron2, Bernadette Dorizzi3
1UNAM, Pôle Recherche & Innovation, Fondation Ellen Poidatz, 1 Rue Ellen Poidatz, Saint-Fargeau-Ponthierry, France; IBISC-EA 4526, Université d'Evry Val d'Essonne, 40 Rue du Pelvoux, Courcouronnes, France.
Insights
Predicting postoperative lower limb kinematics in children with cerebral palsy (CP) using preoperative data is possible. While not yet precise for surgical planning, the model offers unbiased outcome predictions, aiding clinicians and patients.
Area of Science:
- Biomechanical Engineering
- Pediatric Orthopedics
- Rehabilitation Science
Background:
- Cerebral palsy (CP) significantly impacts lower limb kinematics, affecting gait and mobility.
- Predicting surgical outcomes in CP is crucial for effective treatment planning.
- Current methods for predicting postoperative kinematics have limitations.
Purpose of the Study:
- To develop and evaluate a model for predicting postoperative lower limb kinematics in children with CP.
- To assess the relationship between preoperative kinematics, physical examination, and surgical data with postoperative outcomes.
- To determine the accuracy and reliability of kinematic predictions.
Main Methods:
- Utilized data from 115 children with CP undergoing single-event multilevel surgery.
- Applied principal component analysis for preoperative data dimension reduction.
- Performed multiple linear regressions to predict postoperative kinematics using preoperative data, physical examination variables, and surgical procedures.
Main Results:
- Mean prediction errors ranged from 4° to 10° depending on the kinematic angle.
- Frontal plane angles showed the lowest prediction errors.
- Sagittal plane angles were predicted more accurately than transverse plane angles.
- Prediction errors were smaller than the inherent variability of gait parameters in CP.
- Model performance was independent of preoperative CP severity.
Conclusions:
- The developed model provides an unbiased estimation of likely postoperative lower limb kinematics in children with CP.
- While not yet sufficient for definitive surgical planning, it serves as a valuable tool for clinicians and patients.
- Further data collection is needed to enhance model precision for broader surgical and gait pattern predictions.
Abstract:
In this work, postoperative lower limb kinematics are predicted with respect to preoperative kinematics, physical examination and surgery data. Data of 115 children with cerebral palsy that have undergone single-event multilevel surgery were considered. Preoperative data dimension was reduced utilizing principal component analysis. Then, multiple linear regressions with 80% confidence intervals were performed between postoperative kinematics and bilateral preoperative kinematics, 36 physical examination variables and combinations of 9 different surgical procedures. The mean prediction errors on test vary from 4° (pelvic obliquity and hip adduction) to 10° (hip rotation and foot progression), depending on the kinematic angle. The unilateral mean sizes of the confidence intervals vary from 5° to 15°. Frontal plane angles are predicted with the lowest errors, however the same performance is achieved when considering the postoperative average signals. Sagittal plane angles are better predicted than transverse plane angles, with statistical differences with respect to the average postoperative kinematics for both plane's angles except for ankle dorsiflexion. The mean prediction errors are smaller than the variability of gait parameters in cerebral palsy. The performance of the system is independent of the preoperative state severity of the patient. Even if the system is not yet accurate enough to define a surgery plan, it shows an unbiased estimation of the most likely outcome, which can be useful for both the clinician and the patient. More patients' data are necessary for improving the precision of the model in order to predict the kinematic outcome of a large number of possible surgeries and gait patterns.
