Background: Adolescent idiopathic scoliosis (AIS) is the most common spine deformity in adolescent patients. Although structural deformity may affect spinal biomechanics of patients with AIS, little is known regarding various period prevalence proportions of back pain and chronic back pain and factors associated with back pain in such patients. Questions/purposes: (1) What are the period prevalence rates of back pain among teenagers with AIS? (2) Is back pain in patients with AIS associated with curve severity? Methods: A total of 987 patients with AIS who were treated without surgery were recruited from a single center’s scoliosis clinic. Between December 2016 and July 2017, this center treated 1116 patients with suspected AIS. During that time, patients were offered surgery when their Cobb angle was at least 50° and had evidence of curve progression between two visits, and most of the patients who were offered surgery underwent it; other patients with AIS were managed nonsurgically with regular observation, brace prescription, posture training, and reassurance. To be included in this prospective, cross-sectional study, a patient needed to be aged between 10 and 18 years with a Cobb angle > 10°. No followup data were required. A total of 1097 patients with AIS were managed nonsurgically (98.3% of the group seen during the period in question). After obtaining parental consent, patients provided data related to their demographics; physical activity levels; lifetime, 12-month, 30-day, 7-day, and current thoracic pain and low back pain (LBP); chronic back pain (thoracic pain/LBP); brace use; and treatments for scoliosis/back pain. Pain was rated on a 10-point numeric rating scale for pain. The Insomnia Severity Index, Epworth Sleepiness Scale, and Depression Anxiety Stress Scales were also assessed. These features and radiologic study parameters between patients with and without back pain were also compared. Factors associated with current and 12-month back pain as well as chronic back pain were analyzed by multivariate analyses. Results: Depending on the types of period prevalence, the prevalence of thoracic pain ranged from 6% (55 of 987) within 12 months to 14% (139 of 987) within 7 days, whereas that of LBP ranged from 6% (54 of 987) to 29% (289 of 987). Specifically, chronic thoracic pain or LBP had the lowest prevalence. Compared with the no pain group, patients with current back pain had more severe insomnia (odds ratio [OR], 1.80; p = 0.02; 95% confidence interval [CI], 1.10-2.93) and daytime sleepiness (OR, 2.41; p < 0.001, 95% CI, 1.43-4.07). Those with chronic back pain had the same problems along with moderate depression (OR, 2.49; p = 0.03; 95% CI, 1.08-5.71). Older age (OR range, 1.17–1.42; all p values ≤ 0.030) and Cobb angle > 40° (OR range, 2.38–3.74; all p values ≤ 0.015), daytime sleepiness (OR range, 2.39-2.41; all p values ≤ 0.011), and insomnia (OR range, 1.76–2.31; all p values ≤ 0.001) were associated with episodic and/or chronic back pain. Females were more likely to experience back pain in the last 12 months than males. Moderate depression (OR, 3.29; 1.45-7.47; p = 0.004) and wearing a brace (OR, 3.00; 1.47-6.15; p = 0.003) were independently associated with chronic back pain. Conclusions: Biopsychosocial factors are associated with the presence and severity of back pain in the AIS population. Our results highlight the importance of considering back pain screening/management for patients with AIS with their psychosocial profile in addition to curve magnitude monitoring. In particular, sleep quality should be routinely assessed. Longitudinal changes and effects of psychotherapy should be determined in future studies. Level of Evidence: Level II, prognostic study.

Scoliosis is a common spinal deformity that occurs often during adolescence. Previous studies suggested that adolescent idiopathic scoliosis (AIS) patients can have various aspects of their lives being affected, due to disease presentation and/or treatment received. It is important to define a reliable instrument based on which the affected patients’ health-related quality of life can be assessed. This study aims to assess the validity, reliability and sensitivity of the EuroQoL 5-dimension 5-level (EQ-5D-5L) in Chinese patients with AIS. Adolescent idiopathic scoliosis patients of Chinese descent were prospectively recruited to complete both the traditional Chinese versions of the EQ-5D-5L and the refined Scoliosis Research Society-22 (SRS-22r) questionnaires. Patients’ demographic profiles and corresponding clinical parameters including treatment modalities, spinal curve pattern and magnitude, and duration of bracing were recorded. Telephone interviews were then conducted at least two weeks later for the assessment of test-retest reliability. Statistical analysis was performed: construct validity of the EQ-5D-5L domains were assessed using Spearman’s correlation test against the SRS-22r; whereas intra-class correlation coefficient (ICC) was used to assess the test-retest reliability, and agreement over the test-retest period was expressed in percentages. Also, the sensitivity of the EQ-5D-5L in differentiating various clinical known groups was determined by effect size, independent t-test and analysis of variance. A total of 227 AIS patients were recruited. Scores of domains of the EQ-5D-5L correlated significantly (r: 0.57-0.74) with the scores of the SRS-22r domains that were intended to measure similar constructs, supporting construct validity. The EQ-5D-5L domain responses and utility scores showed good test-retest reliability (ICC: 0.777; agreement: 76.4 -98.1 %). Internal consistency was good (Cronbach’s α: 0.78) for the EQ-5D-5L utility score. The EQ-5D-5L utility score was sensitive in detecting differences between subjects who had different treatment modalities and bracing duration, but not for curve pattern and its magnitude. The EQ-5D-5L is found to be a valid, reliable and sensitive measure to assess the health-related quality of life in Chinese AIS patients. This potentiates the possibility of utilizing the EQ-5D-5L to estimate AIS patients’ health-related quality of life, based on which the outcome of various treatment options can eventually be evaluated.

Background: Determining the peak growth velocity of a patient with adolescent idiopathic scoliosis (AIS) is important for timely treatment to prevent curve progression. It is important to be able to predict when the curve-progression risk is greatest to maximize the benefits of any intervention for AIS. The distal radius and ulna (DRU) classification has been shown to accurately predict skeletal growth. However, its utility in predicting curve progression and the rate of progression in AIS is unknown. Questions/purposes: (1) What is the relationship between radius and ulna grades to growth rate (body height and arm span) and curve progression rate? (2) When does peak curve progression occur in relation to peak growth rate as measured by months and by DRU grades? (3) How many months and how many DRU grades elapse between peak curve progression and plateau? Methods: This was a retrospective analysis of a longitudinally maintained dataset of growth and Cobb angle data of patients with AIS who presented with Risser Stages 0 to 3 and were followed to maturity at Risser Stage 5 at a single institute with territory-wide school screening service. From June 2014 to March 2016, a total of 513 patients with AIS fulfilled study inclusion criteria. Of these, 195 were treated with bracing at the initial presentation and were excluded. A total of 318 patients with AIS (74% girls) with a mean age of 12 ± 1.5 years were studied. For analysis, only data from initial presentation to commencement of intervention were recorded. Data for patients during the period of bracing or after surgery were not used for analysis to eliminate potential interventional confounders. Of these 318 patients, 192 were observed, 119 were braced, and seven underwent surgery. Therefore 192 patients (60.4%) who were observed were followed up until skeletal maturity at Risser Stage 5; no patients were lost to followup. The mean curve magnitude at baseline was 21.6^o ± 4.8^o. Mean followup before commencing intervention or skeletal maturity was 4.3 ± 2.3 years. Standing body height, arm span, curve magnitude, Risser stage, and DRU classification were studied. A subgroup analysis of 83 patients inclusive of acceleration, peak, and deceleration progression phases for growth and curve progression was studied to determine any time lag between growth and curve progression. Results were described in mean ± SD. Results: There was positive correlation between growth rate and curve progression rate for body height (r = 0.26; p < 0.001) and arm span (r = 0.26; p < 0.001). Peak growth for body height occurred at radius grade (R) 6 (0.56 ± 0.29 cm/month) and ulna grade (U) 4 (0.65 ± 0.31 cm/month); peak change in arm span occurred at R5 (0.67 ± 0.33 cm/month) and U3 (0.67 ± 0.22 cm/month); and peak curve progression matched with R7 (0.80 ± 0.89 cm/month) and U5 (0.84 ± 0.78 cm/month). Subgroup analysis confirmed that peak curve progression lagged behind peak growth rate by approximately 7 months or one DRU grade. The mean time elapsed between the peak curve progression rate and the plateau phase at R9 U7 was approximately 16 months, corresponding to two DRU grades. Conclusions: By using a standard skeletal maturity parameter in the DRU classification, this study showed that the maximal curve progression occurs after the peak growth spurt, suggesting that the curve should be monitored closely even after peak growth. In addition, the period of potential curve continuing progression extends nearly 1.5 years beyond the peak growth phase until skeletal maturity. Future studies may evaluate whether by observing the trend of growth and curve progression rates, we can improve the outcomes of interventions like bracing for AIS. Level of Evidence: Level II, prognostic study.

Assessment of skeletal maturity is important for a clinician to make a decision of the most appropriate treatment on various skeletal disorders. This task is very challenging when using machine learning method due to the limited data and large anatomical variations among different subjects. In this article, we propose an ensemble-based deep learning pipeline to automatically assess the distal radius and ulna (DRU) maturity from left-hand radiographs. At the same time, we adapted the concept of densely connected mechanism in the proposed network architecture to reuse features and prevent gradient disappearance. Therefore, the model acquires two convincing advantages: first, our model preserves the maximum information flow and has a much faster convergence rate. Second, our model avoids overfitting even if training with limited data. The experimental dataset contains 1189 left-hand X-ray scans of children and teenagers. The proposed method achieves 85.27% and 91.68% for radius and ulna classification, respectively. Extensive experiments prove that our model performs better than using other network structures.

Background: Existing studies on health-related quality of life (HRQoL) mainly covered single growth stages of childhood or adolescence and did not report on the trends in the relationships of HRQoL with sleep duration, physical activity, and screen time. This study aimed to establish the population norm of HRQoL in children and adolescents aged 6–17 years and examine the associations of screen time, sleep duration, and physical activity with HRQoL in this population. Methods: We conducted a large-scale cross-sectional population-based survey study of Hong Kong children and adolescents aged 6 to 17 years. A representative sample of students were interviewed to assess their HRQoL using PedsQL and EQ-5D-Y-5L. Multivariable homoscedastic Tobit regression with linear form or restricted cubic spline of predictors was used to analyze the associations between screen time, sleep duration, and HRQoL. Multiple imputation by chained equations was performed to deal with missing data. Results: A total of 7555 respondents (mean age 11.5, SD 3.2; 55.1% female) were sampled. Their EQ VAS scores, PedsQL physical summary scores, and psychosocial summary scores were positively correlated with sleep duration and moderate/vigorous activity but was negatively correlated with screen time. Conclusions: Children and adolescents who had longer exposure to screen, shorter sleep duration, and lower physical activity levels appeared to have poorer HRQoL as assessed by PedsQL and EQ-5D-Y-5L. Advice and guidance on screen time allocation for children and adolescents should be provided at the levels of school, community, and family.