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Abstract：
BACKGROUND: Physiological thoracic kyphosis (TK) allows sagittal balance of human body. Unlike lumbar lordosis (LL), TK has been relatively neglected in the literature. EOS is an imaging technique employing high-sensitivity xenon particles, featured by low-dose exposure combined with high accuracy compared to conventional radiography. The aim of this study was to investigate predictors of TK in patients with phyiological spine morphology using EOS imaging.
METHODS: EOS images of 455 patients without spinal anomalies were retrospectively assessed for TK (T1- T12), upper thoracic kyphosis (UTK, T1-T5), lower thoracic kyphosis (LTK, T5-T12), LL (L1-S1) and pelvic incidence (PI). The latter curves were measured by two researchers separately and the average of the two measurements was used for further analysis. Spearman non-parametric correlation was estimated for age, PI, LL, LTK, UTK and TK. Multiple robust linear regression analysis was employed to estimate TK, controlling for the effect of age, sex, LL and LTK.
RESULTS: The mean age of patients was 28.3 ± 19.2 years and 302 (66.4%) of them were females. The mean TK, UTK and LTK was 45.5° ± 9.3, 16 ± 7.4° and 29.7° ± 8.9, respectively. The mean UTK in people under 40 years of age was 17.0° ± 7.2, whereas for patients 40+ years old it was 13.6° ± 7.4. At univariable analysis TK positively correlated with UTK (p<0.001), LTK (p<0.001) an LL (p<0.001). At multivariable linear regression TK increased with LTK (RC = 0.67; 95%CI: 0.59; 0.75) or LL (RC = 0.12; 95%CI: 0.06; 0.18), whereas it decreased with age (RC = -0.06; 95%CI: -0.09;-0.02).
CONCLUSIONS: If EOS technology is available, the above linear regression model could be used to estimate TK based upon information on age, sex, LL and LTK. Alternatively, TK could be estimated by adding to LTK 17.0° ± 7.4 for patients < 40 years of age, or 13.6° ± 7.4 in patients 40 + years old. The evidence from the present study may be used as reference for research purposes and clinical practice, including spine examination of particular occupational categories or athletes.
METHODS: EOS images of 455 patients without spinal anomalies were retrospectively assessed for TK (T1- T12), upper thoracic kyphosis (UTK, T1-T5), lower thoracic kyphosis (LTK, T5-T12), LL (L1-S1) and pelvic incidence (PI). The latter curves were measured by two researchers separately and the average of the two measurements was used for further analysis. Spearman non-parametric correlation was estimated for age, PI, LL, LTK, UTK and TK. Multiple robust linear regression analysis was employed to estimate TK, controlling for the effect of age, sex, LL and LTK.
RESULTS: The mean age of patients was 28.3 ± 19.2 years and 302 (66.4%) of them were females. The mean TK, UTK and LTK was 45.5° ± 9.3, 16 ± 7.4° and 29.7° ± 8.9, respectively. The mean UTK in people under 40 years of age was 17.0° ± 7.2, whereas for patients 40+ years old it was 13.6° ± 7.4. At univariable analysis TK positively correlated with UTK (p<0.001), LTK (p<0.001) an LL (p<0.001). At multivariable linear regression TK increased with LTK (RC = 0.67; 95%CI: 0.59; 0.75) or LL (RC = 0.12; 95%CI: 0.06; 0.18), whereas it decreased with age (RC = -0.06; 95%CI: -0.09;-0.02).
CONCLUSIONS: If EOS technology is available, the above linear regression model could be used to estimate TK based upon information on age, sex, LL and LTK. Alternatively, TK could be estimated by adding to LTK 17.0° ± 7.4 for patients < 40 years of age, or 13.6° ± 7.4 in patients 40 + years old. The evidence from the present study may be used as reference for research purposes and clinical practice, including spine examination of particular occupational categories or athletes.
摘要：
背景：生理性胸椎后凸（TK）允许人体矢状平衡。与腰椎前凸(LL)不同,传统知识在文献中相对被忽视。EOS是一种采用高灵敏度氙粒子的成像技术，与常规射线照相术相比，具有低剂量曝光和高精度的特点。这项研究的目的是使用EOS成像研究具有生理脊柱形态的患者的TK预测因子。
方法：回顾性评估455例无脊柱异常患者的EOS图像的TK（T1-T12），上胸椎后凸(UTK,T1-T5),下胸椎后凸(LTK,T5-T12),LL（L1-S1）和骨盆发生率（PI）。后面的曲线由两名研究人员分别测量，两次测量的平均值用于进一步分析。Spearman非参数相关性估计为年龄，PI,LL,LTK,UTK和TK。采用多元稳健线性回归分析估计TK，控制年龄的影响，性别,LL和LTK。
结果：患者的平均年龄为28.3±19.2岁，其中302名（66.4%）为女性。平均传统知识，UTK和LTK分别为45.5°±9.3、16±7.4和29.7°±8.9。40岁以下人群的平均UTK为17.0°±7.2，而40岁以上的患者为13.6°±7.4。在单变量分析中，TK与UTK呈正相关(p<0.001)，LTK(p<0.001)和LL(p<0.001)。在多变量线性回归中，TK随LTK（RC=0.67;95CI：0.59;0.75）或LL（RC=0.12;95CI：0.06;0.18）增加，而随着年龄的增长而下降(RC=-0.06;95CI:-0.09;-0.02)。
结论：如果EOS技术可用，上述线性回归模型可用于根据年龄信息估计传统知识，性别,LL和LTK。或者，对于年龄<40岁的患者，可以通过将17.0°±7.4的LTK相加来估计TK，40岁以上患者为13.6°±7.4。本研究的证据可作为研究目的和临床实践的参考。包括特定职业类别或运动员的脊柱检查。
方法：回顾性评估455例无脊柱异常患者的EOS图像的TK（T1-T12），上胸椎后凸(UTK,T1-T5),下胸椎后凸(LTK,T5-T12),LL（L1-S1）和骨盆发生率（PI）。后面的曲线由两名研究人员分别测量，两次测量的平均值用于进一步分析。Spearman非参数相关性估计为年龄，PI,LL,LTK,UTK和TK。采用多元稳健线性回归分析估计TK，控制年龄的影响，性别,LL和LTK。
结果：患者的平均年龄为28.3±19.2岁，其中302名（66.4%）为女性。平均传统知识，UTK和LTK分别为45.5°±9.3、16±7.4和29.7°±8.9。40岁以下人群的平均UTK为17.0°±7.2，而40岁以上的患者为13.6°±7.4。在单变量分析中，TK与UTK呈正相关(p<0.001)，LTK(p<0.001)和LL(p<0.001)。在多变量线性回归中，TK随LTK（RC=0.67;95CI：0.59;0.75）或LL（RC=0.12;95CI：0.06;0.18）增加，而随着年龄的增长而下降(RC=-0.06;95CI:-0.09;-0.02)。
结论：如果EOS技术可用，上述线性回归模型可用于根据年龄信息估计传统知识，性别,LL和LTK。或者，对于年龄<40岁的患者，可以通过将17.0°±7.4的LTK相加来估计TK，40岁以上患者为13.6°±7.4。本研究的证据可作为研究目的和临床实践的参考。包括特定职业类别或运动员的脊柱检查。
