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Study design A retrospective case series. Objective To investigate whether unmatched rod contouring at the proximal end predisposed to the occurrence of proximal junctional kyphosis (PJK) in early-onset scoliosis (EOS) patients after traditional growing rods (TGR) treatment. Summary of background data TGR treatment has become a mainstay of treatment for EOS patients. PJK is one of the most common alignment-related complications. Methods A consecutive series of EOS patients who had undergone TGR treatment were retrospectively reviewed. They were divided into PJK and non-PJK groups according to the occurrence of PJK or not. Demographic data, surgical strategies, and radiographic parameters were recorded and compared between groups. Proximal junctional angle (PJA) was defined as the angle between the caudal endplate of the UIV and the cephalad endplate of the second supradjacent vertebra above the UIV, while proximal rod contouring angle (PRCA) was defined as the angle of proximal rod contouring, which was represented by the angle between the cephalad endplate of the UIV and the caudal endplate of the second vertebra caudal to the UIV. Unmatched proximal rod contouring was regarded if the postoperative PRCA-PJA difference was greater than 5°. Results This study finally included 73 patients. The mean age at the index surgery was 6.5 ± 2.2 years (range, 2–10 years). Mean follow-up lasted 5.0 ± 1.7 years (range, 2–9 years). They received mean 4.6 ± 1.6 lengthening procedures. There were 13 patients who were observed with PJK (18%). In comparison with the non-PJK group, the PJK group showed a larger preoperative major curve (82 ± 21° vs 70 ± 17°, P = 0.041) and global kyphosis (57 ± 6° vs. 44 ± 15°, P = 0.044). In addition, the PJK group had significantly larger postoperative PJA (10 ± 3 vs. 5 ± 3, PP = 0.031). The proportion of patients with unmatched proximal rod contouring in PJK group was significantly higher than that in the non-PJK group (69% vs. 25%). Multiple logistic regression showed that preoperative GK>50°, postoperative PJA>10 and postoperative unmatched proximal rod contouring were the risk factors in predicting PJK after TGR treatment. Conclusion Approximately 18% EOS patients experienced PJK after TGR treatment. Unmatched proximal rod contouring may be an independent risk factor of PJK occurrence, in addition to greater preoperative GK and larger postoperative PJA. Level of evidence 3.

It has been shown that cooperative coevolution (CC) can effectively deal with large scale optimization problems (LSOPs) through a ‘divide-and-conquer’ strategy. However, its performance is severely restricted by the current context-vector-based sub-solution evaluation method, since this method needs to invoke the original high dimensional simulation model when evaluating each sub-solution, thus requiring many computation resources. To alleviate this issue, this study proposes a novel surrogate model assisted cooperative coevolution (SACC) framework. SACC constructs a surrogate model for each sub-problem and employs it to evaluate corresponding sub-solutions. The original simulation model is only adopted to reevaluate a small number of promising sub-solutions selected by surrogate models, and these really evaluated sub-solutions will in turn be employed to update surrogate models. By this means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. By taking the radial basis function (RBF) and the success-history based adaptive differential evolution (SHADE) as surrogate model and optimizer, respectively, this study further designs a concrete SACC algorithm named RBF-SHADE-SACC. RBF and SHADE have only been proved to be effective on small and medium scale problems. This study scales them up to LSOPs under the SACC framework, where they are tailored to a certain extent for adapting to the characteristics of LSOPs and SACC. Empirical studies on IEEE CEC 2010 benchmark functions demonstrate that SACC can significantly enhance the sub-solution evaluation efficiency, and even with much fewer computation resources, RBF-SHADE-SACC can find much better solutions than traditional CC algorithms.