Relationship between pain and vertebral motion in chronic low-back pain subjects

被引:58
作者
Dickey, JP [1 ]
Pierrynowski, MR
Bednar, DA
Yang, SX
机构
[1] Univ Guelph, Dept Human Biol & Nutr Sci, Guelph, ON N1G 2W1, Canada
[2] McMaster Univ, Sch Rehabil Sci, Hamilton, ON L8S 1C7, Canada
[3] McMaster Univ, Div Orthopaed Surg, Hamilton, ON L8S 1C7, Canada
[4] Univ Guelph, Sch Engn, Guelph, ON N1G 2W1, Canada
关键词
low-back disorders; range of motion; vertebrae; kinematics; lumbar spine; low-back pain; neural networks; external transpedicular fixation;
D O I
10.1016/S0268-0033(02)00032-3
中图分类号
R318 [生物医学工程];
学科分类号
0831 [生物医学工程];
摘要
Objectives. To investigate the relationship between intervertebral motion, intravertebral deformation and pain in chronic low-back pain patients. Design. This study measured vertebral motion of the lumbar spine and associated pain in a select group of chronic low-back pain patients as they performed a standard battery of motions in all planes. Background. Numerous studies have demonstrated that individuals with low-back pain have impaired spinal motion, yet few studies have examined the specific relationship between pain and motion parameters. Although it is accepted that the pain in mechanical low-back patients is due to specific spinal motions, no studies have related specific motions to pain measures. Methods. Percutaneous intra-pedicle screws were placed into the right and left L4 (or L5) and S1 segments of nine chronic low-back pain patients. The external fixator frame was removed following the clinical external fixation test. The 3D locations of the pedicle screws and the level of pain were recorded as the subjects performed a battery of motions. The relationship between the pain and motion parameters was assessed using linear discriminant analysis and neural network models. Results, The neural network model showed a strong relationship between observed and predicted pain (R-2 = 0.997). The discriminant analysis showed a weak relationship (R-2 = 0.5). Conclusions. Vertebral motion parameters are strongly predictive of pain in this select group of chronic low-back pain patients. The nature of the relationship is nonlinear and involves interactions neural networks are able to effectively describe these relationships.
引用
收藏
页码:345 / 352
页数:8
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