Percutaneous hamstring lengthening in cerebral palsy: Technique and gait outcomes based on GMFCS level

BACKGROUND

Increased knee flexion at initial contact and in stance is a common problem for children with cerebral palsy. Surgical correction with percutaneous hamstring lengthening is an alternative to open hamstring lengthening; however, outcomes are less well documented, and there is concern about increasing anterior pelvic tilt. The purpose of this study was to evaluate the short-term outcomes of percutaneous hamstring lengthenings in children with cerebral palsy using comprehensive gait analysis techniques.

RESEARCH QUESTION

Does percutaneous hamstring lengthening improve knee function, and do knee and anterior pelvic tilt outcomes vary by GMFCS level?

METHODS

A convenience sample of 52 patients with both pre and post-surgical gait analyses was evaluated retrospectively for changes in gait function in terms of temporal-spatial parameters, kinematics and kinetics. Patients were divided into two GMFCS subgroups: GMFCS level I/II and III.

RESULTS

The percutaneous hamstring lengthening results in statistically significant improvements in knee function during gait with increased knee extension at initial contact (mean 32° SD 13° to mean 21° SD 11°, p = 0.000) and improved mean knee flexion in stance (mean 23° SD 12° to mean 16° SD 11°, p = 0.000) for the total study cohort. Beneficial changes were found for knee function in both GMFCS level subgroups; however, there was an increase in pelvic tilt pre to post-surgery in the GMFCS level III subgroup (mean 21° SD 8° to mean 26° SD 6°, p = 0.012) but not the GMFCS level I/II subgroup (mean 18° SD 7° to mean 20° SD 8°, p = 0.427).

SIGNIFICANCE

Percutaneous hamstring lengthening can improve knee function for all patients; however, the impact on anterior pelvic tilt varies by GMFCS level, with increasing anterior tilt for GMFCS level III only. This study highlights the importance of understanding differences in surgical outcomes by GMFCS level to better predict post-surgical function.

Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group, Group-Personalized, and Fully-Personalized Activity Classification Models

Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to impairment and activity limitations; however, studies conducted to date have implemented “one-size fits all” group (G) models. Group-personalized (GP) models specific to the Gross Motor Function Classification (GMFCS) level and fully-personalized (FP) models trained on individual data may provide more accurate assessments of PA; however, these approaches have not been investigated in children with CP. In this study, 38 children classified at GMFCS I to III completed laboratory trials and a simulated free-living protocol while wearing an ActiGraph GT3X+ on the wrist, hip, and ankle. Activities were classified as sedentary, standing utilitarian movements, or walking. In the cross-validation, FP random forest classifiers (99.0–99.3%) exhibited a significantly higher accuracy than G (80.9–94.7%) and GP classifiers (78.7–94.1%), with the largest differential observed in children at GMFCS III. When evaluated under free-living conditions, all model types exhibited significant declines in accuracy, with FP models outperforming G and GP models in GMFCS levels I and II, but not III. Future studies should evaluate the comparative accuracy of personalized models trained on free-living accelerometer data.

Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy

BACKGROUND

Cerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical activity (PA) is a major problem effecting the health and well-being of children with CP. Practical, yet accurate measures of PA are needed to evaluate the effectiveness of surgical and therapy-based interventions to increase PA. Accelerometer-based motion sensors have become the standard for objectively measuring PA in children and adolescents; however, current methods for estimating physical activity intensity in children with CP are associated with significant error and may dramatically underestimate HPA in children with more severe mobility limitations. Machine learning (ML) models that first classify the PA type and then predict PA intensity or energy expenditure using activity specific regression equations may be more accurate than standalone regression models. However, the feasibility and validity of ML methods has not been explored in youth with CP. Therefore, the purpose of this study was to develop and test ML models for the automatic identification of PA type in ambulant children with CP.

METHODS

Twenty two children and adolescents (mean age: 12.8 ± 2.9 y) with CP classified at GMFCS Levels I to III completed 7 activity trials while wearing an ActiGraph GT3X+ accelerometer on the hip and wrist. Trials were categorised as sedentary (SED), standing utilitarian movements (SUM), comfortable walking (CW), and brisk walking (BW). Random forest (RF), support vector machine (SVM), and binary decision tree (BDT) classifiers were trained with features extracted from the vector magnitude (VM) of the raw acceleration signal using 10 s non-overlapping windows. Performance was evaluated using leave-one-subject out cross validation.

RESULTS

SVM (82.0-89.0%) and RF (82.6-88.8%) provided significantly better classification accuracy than BDT (76.1-86.2%). Hip (82.7-85.5%) and wrist (76.1-82.6%) classifiers exhibited comparable prediction accuracy, while the combined hip and wrist (86.2-89.0%) classifiers achieved the best overall performance. For all classifiers, recognition accuracy was excellent for SED (94.1-97.9%), good to excellent for SUM (74.0-96.6%) and brisk walking (71.5-86.0%), and modest for comfortable walking (47.6-70.4%). When comfortable and brisk walking were combined into a single walking class, recognition accuracy ranged from 90.3 to 96.5%.

CONCLUSIONS

ML methods provided acceptable classification accuracy for detection of a range of activities commonly performed by ambulatory children with CP. The resultant models can help clinicians more effectively monitor bouts of brisk walking in the community. The results indicate that 2-step models that first classify PA type and then predict energy expenditure using activity specific regression equations are worthy of exploration in this patient group.

Treatment with Botulinum toxin A in a total population of children with cerebral palsy - a retrospective cohort registry study

BACKGROUND

Botulinum toxin A (BTX-A) has been used to reduce spasticity in children with cerebral palsy (CP) for decades. The purpose of this study was to analyze to what extent BTX-A treatment was used to treat spasticity in a total population of children with CP. We investigated 1) the use of BTX-A in relation to age, sex, and Gross Motor Function Classification System (GMFCS) level, 2) the most common muscle groups treated with BTX-A in relation to the same variables, and 3) changes in the proportions of children treated with BTX-A between two time points (2010 and 2015).

METHODS

The study was based on data from CPUP, a combined Swedish follow-up program and national healthcare registry, comprising >95% of all children with CP in Sweden. The participants (N = 3028) were born in 2000 or later. Potential BTX-A treatment and treated muscle groups were included from all CPUP assessments recorded in the registry in 2014-2015. In Aim 3, BTX-A administration in 3-5 year-olds at two time points was assessed. Crosstabs and 95% confidence intervals (CIs) for binominal proportions were calculated and logistic regression was used to regress age, sex, and GMFCS level on BTX-A treatment. Muscle groups treated with BTX-A were assessed using crosstabs and 95% CIs. Proportional change in BTX-A treatment over a 5-year period was analyzed using chi-square.

RESULTS

We included 3028 children (57% boys; median age 7 years) of whom 26% received BTX-A. Significantly more boys (28%) than girls (23%) received BTX-A (OR = 1.25, [95% CI 1.05-1.48]). Significant differences were found for age and GMFCS levels; 4-6 year-olds and those at GMFCS III-IV were more likely to receive BTX-A. BTX-A treatment in the gastrocnemius muscle was most common in the 4-6 year-olds and at GMFCS I-III, whereas treatment of the hamstring and adductor muscles was more common in older children and at GMFCS IV-V. No significant change in the proportion of BTX-A administered in 2010 and 2015 was demonstrated.

CONCLUSIONS

BTX-A treatment differed based on age, sex, and GMFCS level. Proportion of BTX-A treatment in Sweden has remained stable during the past five years.

Differences in Body Composition According to Gross Motor Function in Children With Cerebral Palsy

OBJECTIVE

To assess differences in body composition according to gross motor function in children with cerebral palsy (CP) compared with healthy controls.

DESIGN

Retrospective case-control study.

SETTING

Tertiary referral center for CP.

PARTICIPANTS

Participants (N=146) comprised consecutive patients with CP (n=100; mean age, 11.5±4.2y) who were admitted for orthopedic surgery between May 2014 and March 2016 and typically developing children (TDC, n=46; control group).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Bioelectrical impedance analysis (BIA) was used to assess body composition, including body fat, soft lean mass (SLM), fat-free mass (FFM), skeletal muscle mass (SMM), body cell mass (BCM), bone mineral content (BMC), and basal metabolic rate. Body composition measures were compared according to Gross Motor Function Classification System (GMFCS) level, as well as between children with CP and TDC.

RESULTS

Children with CP with GMFCS levels IV and V had a lower height, weight, and body mass index than those with GMFCS levels I to III. Children with CP with GMFCS levels IV and V had a significantly lower SLM, SLM index, FFM, FFM index, SMM, SMM index, BCM, BCM index, BMC, and BMC index than those with GMFCS levels I to III and TDC. GMFCS level significantly affected SLM and BMC.

CONCLUSIONS

Body composition analysis using BIA showed that nonambulatory children with CP had significantly lower FFM, SLM, SMM, BCM, and BMC than ambulatory children with CP and TDC. However, further study is required to allow the use of BIA as a valid nutritional assessment tool in patients with CP.