A Predictive Formula for Sagittal Vertical Axis in Japanese Women Using Spinopelvic-Hip Mismatch
Year: 2025
Type: article
Study design: Observational Study
Abstract: Introduction: To develop a novel and simplified predictive formula for sagittal vertical axis (SVA) in women by incorporating spinopelvic-hip mismatch, aiming to improve accuracy while considering hip joint alignment. Methods: A total of 257 female patients who underwent whole-spine radiography including the proximal femur were retrospectively analyzed. Patients were categorized into three cohorts: osteoporosis outpatients (n=152), conservatively treated adult spinal deformity (ASD) cases (n=32), and surgical ASD cases (n=73). Radiographic parameters including pelvic incidence (PI), pelvic tilt, thoracic kyphosis (TK) at T9-12, lumbar lordosis (LL), pelvic femoral angle (PFA), and derived indices such as PI-LL and spinopelvic-hip mismatch [PFA-(PI-LL)] were measured. The dataset was randomly divided into a training group (70%) and a validation group (30%). In the training group, Lasso regression with five-fold cross-validation was used for variable selection, followed by multiple linear regression to construct the predictive formula. The final model was then tested in the independent validation group to assess external validity. Results: PI, TK at T9-12, and [PFA-(PI-LL)] were identified as significant contributors to SVA prediction. The final formula was: SVA=627.588+0.8612×PI+2.637×TK (T9-12)-3.616×{PFA-(PI-LL)}. The model demonstrated a coefficient of determination of 0.858 in the training group and 0.851 in the external validation group. Conclusions: This study presents a novel, practical predictive formula for SVA in women that incorporates spinopelvic-hip mismatch and relies solely on parameters measurable from lateral radiographs. By reflecting hip extension capacity as a compensatory mechanism, this formula may enable more accurate SVA prediction and individualized surgical planning using standard lateral radiographs.
Authors: Kohei Takahashi, Ryo Fujita, Ko Hashimoto, Yu Mori, Kenichiro Yahata, Kazuyoshi Baba, Takahiro Onoki, Keisuke Ishikawa, Junya Kusakabe, Michiyoshi Sato, Takeshi Nakamura, Ryo Sugawara, Kyoichi Handa, Haruo Kanno, Hiroshi Ozawa, Yasuhisa Tanaka, Toshimi Aizawa
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7104027572