Defining Age-Adjusted PI-LL Targets for Surgical Realignment in Adult Degenerative Scoliosis: A Retrospective Cohort Study.
Haoran ZhangYuanpeng ZhuXiangjie YinDihan SunShengru WangTerry Jianguo ZhangPublished in: Journal of clinical medicine (2024)
Objectives: The purpose of this study was to investigate postoperative pelvic incidence minus lumbar lordosis mismatch (PI-LL) and health-related quality of life (HRQOL) outcomes to determine age-adjusted PI-LL targets. Method: The dataset encompassed a range of variables, including age, sex, body mass index, Charlson comorbidity index, presence of osteopenia, hospital stay, operative duration, blood loss, American Society of Anesthesiologists score, number of fusion levels, lumbar lordosis, sagittal vertical axis, pelvic incidence, and PI-LL. The non-linear relationship between PI-LL and clinical outcomes was examined using a curve analysis, with adjustments made for potential confounding variables. Upon identification of a non-linear relationship, a two-piecewise regression model was employed to determine the threshold effect. Results: A total of 280 patients were enrolled. In the fully adjusted model, the optimal PI-LL target for patients aged 45-54 years old was PI-LL < 10°, the optimal target for patients aged 55-74 was 10-20°, and the optimal target for patients older than 75 years was more suitable for PI-LL > 20°. In the curve-fitting graph, it could be seen that the relationship between PI-LL and HRQOL outcomes was not linear in each age group. The peaks of the curves within each group occurred at different locations. Higher and lower thresholds for optimal surgical goals were determined using the two-piecewise regression model from the SRS-22 score and the ODI score. Conclusions: This study showed that the optimal PI-LL after corrective surgery in adult degenerative scoliosis patients should be adjusted according to age.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- body mass index
- chronic kidney disease
- peritoneal dialysis
- minimally invasive
- public health
- physical activity
- patient reported outcomes
- metabolic syndrome
- acute coronary syndrome
- machine learning
- adipose tissue
- coronary artery disease
- climate change
- deep learning
- patient reported
- global health
- acute care