Body Fat Distribution in Children and Adolescents With Cerebral Palsy.

To evaluate the body fat distribution in children with cerebral palsy (CP). The present study focusses on a monocentric retrospective analysis of body fat distribution from children diagnosed with CP. The children participated in a rehabilitation program. Reference centiles were calculated based on data from the National Health and Nutrition Examination Survey (NHANES, 1999-2004). Z-scores for trunk-to-leg fat ratio were calculated. Further, fat mass index (FMI) was evaluated based on percentiles that have already been published. 237 males and 194 females with CP were considered (mean age: 11 years and 11 months [SD 3 years]). These were compared to 1059 males and 796 females from the NHANES (mean age: 14 years and 7 months [SD 3 years and 4 months]). The z-scores for trunk-to-leg fat ratio showed the following values: mean -0.47 (SD 1.50) for males, -0.49 (SD 1.11), for females, -0.48 (SD 1.34) for all. The z-scores for FMI showed the following values: mean -0.29 (SD 0.70) for males, -0.88 (SD 2.0) for females, -0.55 (SD 1.46) for all. The results showed rather a gynoid fat distribution and a lower FMI in children with CP than in the reference population (NHANES 1999-2004).

Interaction between fat- and muscle development in children and adolescents.

Background: Prevalence of obesity increased noticeably during the last decades. Little is known so far about the association between fat accumulation due to obesity and skeletal muscle mass. The aim of this study was to describe the association between fat mass and muscle mass after adjusting for relevant confounding factors in the National Health and Nutrition Examination Survey (NHANES) study population of children and adolescents. We postulated a negative correlation between fat mass and lean body mass. Methodology: A total of 849 whole body DXA-scans of the NHANES study population of children and adolescents aged eight to twenty years of the years 1999-2004 were eligible for statistical analysis. Appendicular lean body mass (appLBM) was used to evaluate muscle mass. Bivariate analysis (Pearson's correlation coefficient), multiple linear regression analysis and mediation analysis were performed. The multiple regression analysis and mediation analysis were adjusted for weight, age height, sex ethnicity and physical activity. Results: Fat mass correlates with appendicular lean body mass (Pearons's r 0.336, p < 0.001). In the multiple linear regression analysis the regression coefficient between appLBM and FM was positive (0.204; p < 0.001), when considering appendicular lean body mass, fat mass and body weight, the regression coefficient was negative (-0.517; p < 0.001). Conclusions: Study results indicate a negative association of fat mass and skeletal muscle mass in children and adolescents, when weight, age, height, sex ethnicity and physical activity are considered. Further investigations are needed to evaluate if there is a biochemical interaction between fat cells and muscle cells that could explain this effect.

Artificial intelligence to improve efficiency of administration of gross motor function assessment in children with cerebral palsy.

AIM: To create a reduced version of the 66-item Gross Motor Function Measure (rGMFM-66) using innovative artificial intelligence methods to improve efficiency of administration of the GMFM-66. METHOD: This study was undertaken using information from an existing data set of children with cerebral palsy participating in a rehabilitation programme. Different self-learning approaches (random forest, support vector machine [SVM], and artificial neural network) were evaluated to estimate the GMFM-66 score with the fewest possible test items. Test agreements were evaluated (among other statistics) by intraclass correlation coefficients (ICCs). RESULTS: Overall, 1217 GMFM-66 assessments (509 females, mean age 8y 10mo [SD 3y 9mo]) at a single time and 187 GMFM-66 assessments and reassessments (80 females, mean age 8y 5mo [SD 3y 10mo]) after 1 year were evaluated. The model with SVM predicted the GMFM-66 scores most accurately. The ICCs of the rGMFM-66 and the full GMFM-66 were 0.997 (95% confidence interval [CI] 0.996-0.997) at a single time and 0.993 (95% CI 0.993-0.995) for the evaluation of the change over time. INTERPRETATION: The study shows that the efficiency of the full GMFM-66 assessment can be increased by using machine learning (self-learning algorithms). The presented rGMFM-66 score showed an excellent agreement with the full GMFM-66 score when applied to a single assessment and when evaluating the change over time.

Automated Noninvasive Central Blood Pressure Measurements by Oscillometric Radial Pulse Wave Analysis: Results of the MEASURE-cBP Validation Studies.

BACKGROUND: Central blood pressure becomes increasingly accepted as an important diagnostic and therapeutic parameter. Accuracy of widespread applanation tonometry can be affected by calibration and operator training. To overcome this, we aimed to evaluate novel VascAssist 2 using automated oscillometric radial pulse wave analysis and a refined multi-compartment model of the arterial tree. METHODS: Two hundred and twenty-five patients were prospectively enrolled. Invasive aortic root measurements served as reference in MEASURE-cBP 1 (n = 106) whereas applanation tonometry (SphygmoCor) was used in MEASURE-cBP 2 (n = 119). RESULTS: In MEASURE-cBP 1, we found a mean overestimation for systolic values of 4 ± 12 mmHg (3 ± 10%) and 6 ± 10 mmHg (9 ± 14%) for diastolic values. Diabetes mellitus and low blood pressure were associated with larger variation. In MEASURE-cBP 2, mean overestimation of systolic values was 4 ± 4 mmHg (4 ± 4%) and 1 ± 4 mmHg (1 ± 7%) of diastolic values. Arrhythmia was significantly more frequent in invalid measurements (61 vs. 18%, P < 0.0001) which were most often due to a low quality index of SphygmoCor. CONCLUSIONS: Central blood pressure estimates using VascAssist 2 can be considered at least as accurate as available techniques, even including diabetic patients. In direct comparison, automated measurement considerably facilitates application not requiring operator training and can be reliably applied even in patients with arrhythmias.