Motor and functional outcome of selective dorsal rhizotomy in children with spastic diplegia at 12 and 24 months of follow-up

Background: Selective dorsal rhizotomy (SDR) in ambulatory children affected by cerebral palsy (CP) is a surgical treatment option to lower spasticity and thereby improve gait and ambulation. The aim of the current study is to investigate the outcome of children with respect to spasticity, muscle strength, and overall function after SDR. Methods: All children who underwent SDR via a single-level laminotomy in the time period from January 2007 to April 2015 at our center were enrolled in this study. Within a standardized evaluation process, the following was assessed routinely pre-operatively and 12 and 24 months following surgery: extent of spasticity at hip adductors and hamstrings as characterized by the Modified Ashworth Scale (MAS), maximal muscle strength as characterized by the Medical Council Research Scale (MRC), overall function regarding ambulation as characterized by the Gross Motors Function Classification System (GFMCS), and overall function as characterized by the Gross Motor Function Measure (GMFM-88). Results: Matching sets of pre- and post-operative assessments of the chosen outcome parameters were available for 109 of the 150 children who underwent SDR within the observation period. After 24 months, the MAS scores of hip adductors (n = 59) improved in 71% and 76% of children on the right and left side, respectively. In 20% and 19%, it remained unchanged and worsened in 9% and 5% of children on the right and left side, respectively (p < 0.00625). For hamstrings, the rates for the right and left sides were 81% and 79% improvement, 16% and 16% unchanged, and 4% and 5% worsened, respectively (p < 0.00625). Muscle strength of ankle dorsiflexion and knee extension significantly improved after 24 months. Overall function assessed by GMFM-88 improved significantly by 4% after 12 months (n = 77) and by 7% after 24 months (n = 56, p < 0.0001). Conclusions: The presented data underlines the benefit of SDR in a pediatric patient collective with bilateral spastic CP. The procedure resulted in an effective and permanent reduction of spasticity and improved overall function without causing relevant weakness of the lower extremities

Unexpected Course of Nonlinear Cardiac Interbeat Interval Dynamics during Childhood and Adolescence

The fluctuations of the cardiac interbeat series contain rich information because they reflect variations of other functions on different time scales (e.g., respiration or blood pressure control). Nonlinear measures such as complexity and fractal scaling properties derived from 24 h heart rate dynamics of healthy subjects vary from childhood to old age. In this study, the age-related variations during childhood and adolescence were addressed. In particular, the cardiac interbeat interval series was quantified with respect to complexity and fractal scaling properties. The R-R interval series of 409 healthy children and adolescents (age range: 1 to 22 years, 220 females) was analyzed with respect to complexity (Approximate Entropy, ApEn) and fractal scaling properties on three time scales: long-term (slope β of the power spectrum, log power vs. log frequency, in the frequency range 10−4 to 10−2 Hz) intermediate-term (DFA, detrended fluctuation analysis, α2) and short-term (DFA α1). Unexpectedly, during age 7 to 13 years β and ApEn were higher compared to the age <7 years and age >13 years (β: −1.06 vs. −1.21; ApEn: 0.88 vs. 0.74). Hence, the heart rate dynamics were closer to a 1/f power law and most complex between 7 and 13 years. However, DFA α1 and α2 increased with progressing age similar to measures reflecting linear properties. In conclusion, the course of long-term fractal scaling properties and complexity of heart rate dynamics during childhood and adolescence indicates that these measures reflect complex changes possibly linked to hormonal changes during pre-puberty and puberty

Sleep Instabilities Assessed by Cardiopulmonary Coupling Analysis Increase During Childhood and Adolescence

The electrocardiogram-based cardiopulmonary coupling (CPC) technique may be used to track sleep instabilities. With progressing age, maturational changes during childhood and adolescence affect sleep. The objective was to assess developmental changes in sleep instabilities in a natural setting. ECGs during nighttime sleep on regular school days were recorded from 363 subjects aged 4 to 22 years (204 females). The estimated total sleep time (ETST) decreased from 598 to 445 min during childhood and adolescence. Stable sleep linearly decreased with progressing age (high frequency coupling (HFC): 70–48% ETST). Unstable sleep [low frequency coupling (LFC): 9–19% ETST], sleep fragmentation or disordered breathing (elevated LFC: 4–12% ETST), and wake/REM states [very low frequency coupling (VLFC): 20–32% ETST] linearly increased with age. Hence, with progressing age the sleep of children and adolescents shortens, becomes more unstable and is more often affected by fragmentation or sleep disordered breathing, especially in the age group &gt;13 years. It remains to be clarified whether some of the changes are caused by a social jetlag, i.e., the misalignment of body clock and social time especially in adolescents

Course of the frequency domain measures during childhood and adolescence.

<p>HF – high frequency component, LF – low frequency component, VLF – very low frequency component, ULF – ultra low frequency component, ln ms² – natural logarithm of the absolute values in ms², ln LF/HF - natural logarithm of the ratio LF/HF. The circles represent 24 h averages. Solid and dashed lines show moving average and the fitted polynomial model of order 3, respectively.</p

Examples of the power spectral density (PSD) of a 24 h R-R interval series in a double logarithmic plot (decadic logarithm as indicated by ‘log₁₀’).

<p>The slope β denotes the slope of the linear regression calculated using the averaged PSD (grey circles; frequency range: 10⁻⁴ to 10⁻² Hz). Top: male subject, age 7.9 years; bottom: male subject, age 18.1 years.</p

Course of the mean R-R interval and its accompanying standard deviation (SDNN) as basic time domain measures during childhood and adolescence.

<p>The circles represent 24 h averages. Solid and dashed lines show moving average and the fitted polynomial model of order 3, respectively.</p

Course of the fractal and complexity measures during childhood and adolescence.

<p>Slope β – long-term fractal scaling properties, DFA α₂ – intermediate-term fractal scaling properties, DFA α₁ – short-term fractal scaling properties, ApEn - complexity. The dots represent 24 hour averages. Solid and dashed lines show moving average and the fitted polynomial model of order 3, respectively.</p

Comparison of different age groups with respect to scaling and complexity measures.

<p>Slope β – long-term fractal scaling properties, DFA α₂ – intermediate-term fractal scaling properties, DFA α₁ – short-term fractal scaling properties, ApEn - complexity. Each group shows three values: 24 h averages are plotted in black, night-time (left of black dot) and wake time averages (right of black dot) are plotted in grey. Note that the slope β can only be calculated for the entire recording. The symbol above a value/dot refers to comparisons within the same time period (24 h, night-time or wake time). * Group differed from 3 other groups. + Group differed from group <7 years and group 7 to 9 years. □ Group differed from group 7 to 9 years. ▵ Group differed from group 7 to 9 years and group 10 to 13 years.</p

Comparison of different age groups with respect to time and frequency domain measures.

<p>R-R – mean R-R interval, SDNN – standard deviation of R-R interval series, HF – high frequency component, LF – low frequency component, VLF – very low frequency component, ULF – ultra low frequency component, ln ms² – natural logarithm of the absolute values in ms², ln LF/HF - natural logarithm of the ratio LF/HF. Each group shows three values: 24 h averages are plotted in black, night-time (left of black dot) and wake time averages (right of black dot) are plotted in grey. Note that ULF can only be calculated for the entire recording. The frequency domain measures were transformed by taking the natural logarithm to yield normal distributions (indicated by ‘ln ms²’). The symbol above a value/dot refers to comparisons within the same time period (24 h, night-time or wake time).* Group differed from 3 other groups. + Group differed from group <7 years and group 7 to 9 years. □ Group differed from group 7 to 9 years. ▵ Group differed from group 7 to 9 years and group 10 to 13 years.</p