Abstract：
BACKGROUND: A subgroup of patients with pelvic anteversion can present with an unusually large degree of lumbar lordosis (LL), a highly sloped sacrum, and a relatively small pelvic incidence (PI). Prior to lumbar surgery, it can be important to consider such unique sagittal alignment. However, until now, there has been a lack of a predictive model considering different pelvic alignments. Furthermore, the dynamic characteristics of an anteverted pelvis (AP) subgroup have also been unclear.
OBJECTIVE: To build linear predictive formulas for LL that take pelvic anteversion into consideration and to explore the dynamic characteristics of an AP subgroup.
METHODS: Monocentric, cross-sectional study.
METHODS: Five hundred and sixty-five asymptomatic Chinese men and women between the ages of 18 and 80 years.
METHODS: Sagittal parameters including LL, lumbar lordosis minus thoracic kyphosis (LL-TK), PI, pelvic tilt (PT), pelvic incidence minus lumbar lordosis (PI-LL), sacral slope (SS), sacral slope divided by pelvic incidence (SS/PI), sagittal vertical axis (SVA), thoracic kyphosis (TK), and T1 (first thoracic vertebra) pelvic angle (TPA) were measured on whole spine radiographs obtained with participants in standing and sitting positions.
METHODS: All participants underwent radiography in the standing position; 235 of them underwent additional radiography in the sitting position to allow measurement of sagittal parameters. The participants with pelvic anteversion were placed in an AP (anteverted pelvis) group. Sagittal parameters were compared between the AP group and the non-AP group, and predictive formulas for LL based on PI were created in both groups. In addition, changes in sagittal parameters from standing to sitting were compared in the AP group and a PI-matched control group.
RESULTS: Of the 565 participants, 171 (30.3%) had pelvic anteversion. In comparison with the non-AP group, the AP group presented with larger LL, a larger SS, and a smaller PT, with relatively small PI. The predictive formulas for LL were LL=0.60° × PI+21.60° (R2=0.268; p<.001) in the whole cohort, LL=0. 83×PI+18.75° (R2=0.427; p<.001) in AP group, and LL=0.79°×PI+9.66° (R2=0.451; p<.001) in the non-AP group. In moving from standing to sitting, the AP group presented with a larger decrease in SS and LL compared with the control group, indicating different patterns of spinopelvic motion.
CONCLUSIONS: In the cohort examined, 30.3% present with pelvic anteversion. Those with AP present with unique characteristics of spinopelvic alignment. In moving from standing to sitting, they exhibit different patterns of spinopelvic motion. We found that identifying the degree of anteversion in each person improves the accuracy of linear models for predicting the degree of LL, which in turn can make plans for spine surgery more accurate.
OBJECTIVE: To build linear predictive formulas for LL that take pelvic anteversion into consideration and to explore the dynamic characteristics of an AP subgroup.
METHODS: Monocentric, cross-sectional study.
METHODS: Five hundred and sixty-five asymptomatic Chinese men and women between the ages of 18 and 80 years.
METHODS: Sagittal parameters including LL, lumbar lordosis minus thoracic kyphosis (LL-TK), PI, pelvic tilt (PT), pelvic incidence minus lumbar lordosis (PI-LL), sacral slope (SS), sacral slope divided by pelvic incidence (SS/PI), sagittal vertical axis (SVA), thoracic kyphosis (TK), and T1 (first thoracic vertebra) pelvic angle (TPA) were measured on whole spine radiographs obtained with participants in standing and sitting positions.
METHODS: All participants underwent radiography in the standing position; 235 of them underwent additional radiography in the sitting position to allow measurement of sagittal parameters. The participants with pelvic anteversion were placed in an AP (anteverted pelvis) group. Sagittal parameters were compared between the AP group and the non-AP group, and predictive formulas for LL based on PI were created in both groups. In addition, changes in sagittal parameters from standing to sitting were compared in the AP group and a PI-matched control group.
RESULTS: Of the 565 participants, 171 (30.3%) had pelvic anteversion. In comparison with the non-AP group, the AP group presented with larger LL, a larger SS, and a smaller PT, with relatively small PI. The predictive formulas for LL were LL=0.60° × PI+21.60° (R2=0.268; p<.001) in the whole cohort, LL=0. 83×PI+18.75° (R2=0.427; p<.001) in AP group, and LL=0.79°×PI+9.66° (R2=0.451; p<.001) in the non-AP group. In moving from standing to sitting, the AP group presented with a larger decrease in SS and LL compared with the control group, indicating different patterns of spinopelvic motion.
CONCLUSIONS: In the cohort examined, 30.3% present with pelvic anteversion. Those with AP present with unique characteristics of spinopelvic alignment. In moving from standing to sitting, they exhibit different patterns of spinopelvic motion. We found that identifying the degree of anteversion in each person improves the accuracy of linear models for predicting the degree of LL, which in turn can make plans for spine surgery more accurate.
摘要：
背景：骨盆前倾患者的一个亚组可以表现出异常大的腰椎前凸（LL），一个高度倾斜的骶骨，和相对较小的骨盆发病率（PI）。在腰椎手术之前,考虑这种独特的矢状排列是很重要的。然而,直到现在,缺乏考虑不同骨盆排列的预测模型.此外，前向骨盆(AP)亚组的动态特征也不清楚.
目的：建立考虑骨盆前倾的LL线性预测公式，并探讨AP亚组的动态特征。
方法：单中心，横断面研究。
方法：565名年龄在18至80岁之间的无症状中国男性和女性。
方法：矢状参数，包括LL，腰椎前凸减去胸椎后凸(LL-TK),(PI),骨盆倾斜(PT),骨盆发病率减去腰椎前凸（PI-LL），骶骨斜坡(SS),骶骨斜率除以骨盆发生率（SS/PI），矢状垂直轴(SVA),胸椎后凸(TK),和T1（第一胸椎）骨盆角（TPA）在参与者站立和坐位获得的整个脊柱X光片上测量。
方法：所有参与者都在站立姿势下进行了X线摄影；其中235人在坐姿下进行了额外的X线摄影，以允许测量矢状参数。将骨盆前倾的参与者置于AP（前倾骨盆）组中。比较AP组和非AP组的矢状参数,并在两组中创建基于PI的LL预测公式。此外,在AP组和PI匹配的对照组中,比较了从站立到坐位的矢状参数变化.
结果：在565名参与者中，171（30.3％）患有骨盆前倾。与非AP组相比，AP组出现了更大的LL，更大的SS，和一个较小的PT，相对较小的PI。在整个队列中，LL的预测公式为LL=0.60°×PI21.60°（R2=0.268；P<0.001）。LL=0。AP组83×PI+18.75°(R2=0.427,P<0.001),非AP组LL=0.79°×PI+9.66°(R2=0.451;P<0.001)。从站立到坐，与对照组相比，AP组的SS和LL下降幅度更大，指示不同模式的脊椎骨盆运动。
结论：在所检查的队列中，30.3%存在骨盆前倾。患有AP的患者具有独特的脊柱骨盆排列特征。从站立到坐，它们表现出不同的脊柱骨盆运动模式。我们发现，识别每个人的前倾程度可以提高线性模型预测LL程度的准确性，这反过来可以使脊柱手术的计划更准确。
目的：建立考虑骨盆前倾的LL线性预测公式，并探讨AP亚组的动态特征。
方法：单中心，横断面研究。
方法：565名年龄在18至80岁之间的无症状中国男性和女性。
方法：矢状参数，包括LL，腰椎前凸减去胸椎后凸(LL-TK),(PI),骨盆倾斜(PT),骨盆发病率减去腰椎前凸（PI-LL），骶骨斜坡(SS),骶骨斜率除以骨盆发生率（SS/PI），矢状垂直轴(SVA),胸椎后凸(TK),和T1（第一胸椎）骨盆角（TPA）在参与者站立和坐位获得的整个脊柱X光片上测量。
方法：所有参与者都在站立姿势下进行了X线摄影；其中235人在坐姿下进行了额外的X线摄影，以允许测量矢状参数。将骨盆前倾的参与者置于AP（前倾骨盆）组中。比较AP组和非AP组的矢状参数,并在两组中创建基于PI的LL预测公式。此外,在AP组和PI匹配的对照组中,比较了从站立到坐位的矢状参数变化.
结果：在565名参与者中，171（30.3％）患有骨盆前倾。与非AP组相比，AP组出现了更大的LL，更大的SS，和一个较小的PT，相对较小的PI。在整个队列中，LL的预测公式为LL=0.60°×PI21.60°（R2=0.268；P<0.001）。LL=0。AP组83×PI+18.75°(R2=0.427,P<0.001),非AP组LL=0.79°×PI+9.66°(R2=0.451;P<0.001)。从站立到坐，与对照组相比，AP组的SS和LL下降幅度更大，指示不同模式的脊椎骨盆运动。
结论：在所检查的队列中，30.3%存在骨盆前倾。患有AP的患者具有独特的脊柱骨盆排列特征。从站立到坐，它们表现出不同的脊柱骨盆运动模式。我们发现，识别每个人的前倾程度可以提高线性模型预测LL程度的准确性，这反过来可以使脊柱手术的计划更准确。
