Prediction Model for Lumbar Curve Correction After Selective Thoracic Fusion in Lenke 1 and 2 Adolescent Idiopathic Scoliosis.

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. [ABSTRACT FROM AUTHOR]