Impaired motor competence in children with transplanted liver.

OBJECTIVES: Although reduced cognitive function has been demonstrated after liver transplantation in children, few data are available concerning motor competence. METHODS: Thirty-five children ages 4 to 12 years were tested using Movement Assessment Battery for Children (M-ABC) test at a median of 5.1 (3.9-6.9) years after liver transplantation and compared with reference material of healthy children. RESULTS: Children with transplantation had worse M-ABC score 8.0 (interquartile range 5.0-11.5), compared with healthy children 3.5 (1.0-6.0) (P < 0.0001). All of the subscores (manual dexterity [P < 0.0001], ball skills [P = 0.0037], and balance [P = 0.0032]) were significantly worse in the children with liver transplantation compared with the healthy reference group. Twenty-nine percent of the children with liver transplantation had impaired motor competence, compared with 9% of a healthy reference group. Seventeen of the patients with transplantation were retested 1 year later, and 11 were tested 4 years later with no changes in total M-ABC score. Ball skill competence was worse 4 years after first assessment (P = 0.013). For children with transplantation and cholestatic liver disease (n =26), renal function was a significant predictor for total M-ABC score (P = 0.018). CONCLUSIONS: Children with liver transplantation had impaired motor competence compared with healthy children. Ball skills developed adversely several years after liver transplantation, and motor competence did not improve with time after transplantation. Renal function was a significant predictor for motor competence in children with liver transplantation and cholestatic liver disease.

Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study.

BACKGROUND: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. METHODS: The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time-frequency decomposition of the movement trajectories of the infant's body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9-15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging. RESULTS: The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). CONCLUSION: The CIMA model may be a clinically feasible alternative to observational GMA.