title: Predicting postoperative gait in cerebral palsy.
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.
authors.affiliation: UNAM, 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.
authors.forename: Omar A
authors.initials: OA
authors.lastname: Galarraga C
authors.affiliation: IBISC-EA 4526, Université d'Evry Val d'Essonne, 40 Rue du Pelvoux, Courcouronnes, France.
authors.forename: Vincent
authors.initials: V
authors.lastname: Vigneron
authors.affiliation: SAMOVAR-UMR 5157, Télécom SudParis, Institut Mines-Télécom, 9 Rue Charles Fourier, Evry, France.
authors.forename: Bernadette
authors.initials: B
authors.lastname: Dorizzi
authors.affiliation: UNAM, Pôle Recherche & Innovation, Fondation Ellen Poidatz, 1 Rue Ellen Poidatz, Saint-Fargeau-Ponthierry, France; Chirurgie Orthopédique Pédiatrique, Hôpital Universitaire Necker-Enfants Malades, 149 Rue de Sèvres, Paris, France.
authors.forename: Néjib
authors.initials: N
authors.lastname: Khouri
authors.affiliation: UNAM, Pôle Recherche & Innovation, Fondation Ellen Poidatz, 1 Rue Ellen Poidatz, Saint-Fargeau-Ponthierry, France. Electronic address: eric.desailly@fondationpoidatz.com.
authors.forename: Eric
authors.initials: E
authors.lastname: Desailly
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001696:Biomechanical Phenomena / Q000502:physiology; D002547:Cerebral Palsy / Q000503:physiopathology* / Q000601:surgery*; D002648:Child; D005260:Female; D020233:Gait Disorders, Neurologic / Q000503:physiopathology*; D006801:Humans; D016014:Linear Models; D035002:Lower Extremity / Q000503:physiopathology / Q000601:surgery*; D000069550:Machine Learning; D008297:Male; D019637:Orthopedic Procedures; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010808:Physical Examination; D011300:Preoperative Care; D025341:Principal Component Analysis; D012189:Retrospective Studies
keywords: Cerebral palsy; Clinical gait analysis; Machine learning; Outcome prediction; Single-event multilevel surgery