Spine · 2024
Prediction Model for Lumbar Curve Correction After Selective Thoracic Fusion in Lenke 1 and 2 Adolescent Idiopathic Scoliosis.
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Cai Zhenning, Liu Wanyou, Dai Yutong, Shi Benlong, Zhu Zezhang, Qiu Yong
Аннотация
STUDY DESIGN: A retrospective study.
OBJECTIVE: To identify independent risk factors and construct a prediction model for lumbar curve correction (LCC) after selective thoracic fusion (STF) in patients with Lenke 1 and 2 adolescent idiopathic scoliosis (AIS).
SUMMARY OF BACKGROUND DATA: STF has been widely applied to Lenke 1 and 2 AIS patients. However, LCC after STF is still controversial.
METHODS: One hundred twenty-eight patients undergoing STF with at least 2 years of follow-up were included. Cases were divided into a high-LCC group and a low-LCC group according to a rounded-up median of 65%. Forty-nine variables were taken into account. Logistic regression was applied to identify independent predictive factors. A prediction model was established by backward stepwise regression, and its evaluation was implemented on R.
RESULTS: Five parameters showed independent predictive value for low LCC: right shoulder higher before surgery (right shoulder higher versus balanced: odds ratio [OR]=0.244, P =0.014), postoperative Cobb angle of lumbar curve (LC) (OR=1.415, P =0.001, cutoff value=11°), lowest instrumented vertebra (LIV) distal to end vertebra (no vs. yes: OR=4.587, P =0.013), postoperative LIV tilt (OR=0.686, P =0.010, cutoff value=6.85°) and postoperative LIV+1 tilt (OR=1.522, P =0.005, cutoff value=6.25°). The prediction model included 6 variables: lumbar modifier, preoperative shoulder balance, postoperative Cobb angle of LC, LIV position, postoperative LIV tilt, and postoperative LIV+1 tilt. The model evaluation demonstrated satisfactory capability and stability (area under curve=0.890, 10-fold cross-validation accuracy=0.782).
CONCLUSION: Preoperative shoulder balance, Cobb angle of LC, LIV position, postoperative LIV and LIV+1 tilt could be used to prognosticate LCC after STF. A model with solid prediction ability was established, which could further our understanding of LCC and assist in making clinical decisions.
OBJECTIVE: To identify independent risk factors and construct a prediction model for lumbar curve correction (LCC) after selective thoracic fusion (STF) in patients with Lenke 1 and 2 adolescent idiopathic scoliosis (AIS).
SUMMARY OF BACKGROUND DATA: STF has been widely applied to Lenke 1 and 2 AIS patients. However, LCC after STF is still controversial.
METHODS: One hundred twenty-eight patients undergoing STF with at least 2 years of follow-up were included. Cases were divided into a high-LCC group and a low-LCC group according to a rounded-up median of 65%. Forty-nine variables were taken into account. Logistic regression was applied to identify independent predictive factors. A prediction model was established by backward stepwise regression, and its evaluation was implemented on R.
RESULTS: Five parameters showed independent predictive value for low LCC: right shoulder higher before surgery (right shoulder higher versus balanced: odds ratio [OR]=0.244, P =0.014), postoperative Cobb angle of lumbar curve (LC) (OR=1.415, P =0.001, cutoff value=11°), lowest instrumented vertebra (LIV) distal to end vertebra (no vs. yes: OR=4.587, P =0.013), postoperative LIV tilt (OR=0.686, P =0.010, cutoff value=6.85°) and postoperative LIV+1 tilt (OR=1.522, P =0.005, cutoff value=6.25°). The prediction model included 6 variables: lumbar modifier, preoperative shoulder balance, postoperative Cobb angle of LC, LIV position, postoperative LIV tilt, and postoperative LIV+1 tilt. The model evaluation demonstrated satisfactory capability and stability (area under curve=0.890, 10-fold cross-validation accuracy=0.782).
CONCLUSION: Preoperative shoulder balance, Cobb angle of LC, LIV position, postoperative LIV and LIV+1 tilt could be used to prognosticate LCC after STF. A model with solid prediction ability was established, which could further our understanding of LCC and assist in making clinical decisions.