The decision of when to use selective thoracic fusion (STF) and the prediction of spontaneous lumbar curve correction (SLCC) remain difficult. Using a novel methodological approach, the authors yielded for a better prediction of SLCC and analyzed the efficacy of anterior scoliosis correction and fusion (ASF). A retrospective analysis of 273 patients treated with ASF for STF was performed. In total, 87 % of the patients showed a Lenke 1 curve pattern. The lumbar curve modifier was classified as A in 66 % of the patients, B in 21 % of the patients and C in 13 % of the patients. The fusion length averaged 6.7 levels. The analysis included an assessment of radiographic deformity and correction, surgery characteristics, complications and revisions and clinical outcomes to improve the prediction of SLCC. Patients with a Type A-L, Type B or Type C modifier were stratified into a target follow-up lumbar curve (LC) category of a parts per thousand currency sign20A degrees or > 20A degrees. Linear regression analyses were performed to assess the accuracy of predicting LC magnitude, and a multivariate logistic regression model was built using the following preoperative (preop) predictors: main thoracic curve (MTC), LC, MTC-bending and LC-bending. The output variable indicated whether a patient had an LC > 20A degrees at follow-up. A variable selection algorithm was applied to identify significant predictors. Two thresholds (cut-offs) were applied to the test sample to create high positive and negative prediction values. The data from 33 additional patients were gathered prospectively to create an independent test sample to learn how the model performed with independent data as a test of the generalizability of the model. The average patient age was 17 years, and the average follow-up period was 33 months. The MTC was 53.1A degrees A A +/- A 10.2A degrees preoperatively, 29.8A degrees A A +/- A 10.5A degrees with bending and was 25.4A degrees A A +/- A 9.7A degrees at follow-up (p < 0.01). The LC was 35.7A degrees A A +/- A 7.5A degrees preoperatively, 8.9A degrees A A +/- A 5.8A degrees with bending, and 21.8A degrees A A +/- A 7.0A degrees at follow-up (p < 0.01). After applying a variable selection algorithm, the preop LC [p < 0.02, odds ratio (OR) = 1.09] and preop LC-bending (p < 0.009, OR = 1.14) remained in the model as significant predictors. The performance of the linear regression model was tested in an independent test sample, and the difference between the observed and predicted values was only 1A degrees A A +/- A 4.5A degrees. Based on the test sample, the lower threshold was set to 25 %, and the upper threshold was set to 75 %. Patients with prediction values of 25-75 % were identified by the model, but by definition of the model, no prediction was made. In the test sample, 87 % of the patients were correctly classified as having an LC a parts per thousand currency sign20A degrees at follow-up, and 84 % of the patients were correctly classified as having an LC > 20A degrees. The model test in the independent test sample revealed that 100 % of the patients were correctly classified as having an LC a parts per thousand currency sign20A degrees, and 86 % of the patients were correctly classified as having an LC > 20A degrees. After analyzing a sufficiently large sample of 273 patients who underwent ASF for STF, significant predictors for SLCC were established and reported according to the surgical outcomes. Application of the prediction models can aid surgeons in the decision-making process regarding when to perform STF. Our results indicate that with stratification of outcomes into target curves (e.g., an LC < 20A degrees), future benchmarks for STF might be more conclusive.
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