Study Design: A retrospective study. Objectives: To investigate the incidence, management and outcome of delayed deep surgical site infection (SSI) after the spinal deformity surgery. Methods: This study reviewed 5044 consecutive patients who underwent spinal deformity corrective surgery and had been followed over 2 years. Delayed deep SSI were defined as infection involving fascia and muscle and occurring >3 months after the initial procedure. An attempt to retain the implant were initially made for all patients. If the infection failed to be eradicated, the implant removal should be put off until solid fusion was confirmed, usually more than 2 years after the initial surgery. Radiographic data at latest follow-up were compared versus that before implant removal. Results: With an average follow-up of 5.3 years, 56 (1.1%) patients were diagnosed as delayed deep SSI. Seven (12.5%) patients successfully retained instrumentation and there were no signs of recurrence during follow-up (average 3.4 years). The remaining patients, because of persistent or recurrent infection, underwent implant removal 2 years or beyond after the primary surgery, and solid fusion was detected in any case. However, at a minimum 1-year follow-up (average 3.9 years), an average loss of 9° in the thoracic curve and 8° in the thoracolumbar/lumbar curves was still observed. Conclusions: Delayed deep SSI was rare after spinal deformity surgery. To eradicate infection, complete removal of implant may be required in the majority of delayed SSI. Surgeons must be aware of high likelihood of deformity progression after implant removal, despite radiographic solid fusion.

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 access the original high dimensional simulation model when evaluating each sub-solution and thus requires 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 obtained via decomposition and employs it to evaluate corresponding sub-solutions. The original simulation model is only adopted to reevaluate some good sub-solutions selected by surrogate models, and these real evaluated sub-solutions will be in turn employed to update surrogate models. By this means, the computation cost could be greatly reduced without significantly sacrificing evaluation quality. To show the efficiency of SACC, this study uses radial basis function (RBF) and success-history based adaptive differential evolution (SHADE) as surrogate model and optimizer, respectively. RBF and SHADE have been proved to be effective on small and medium scale problems. This study first scales them up to LSOPs of 1000 dimensions under the SACC framework, where they are tailored to a certain extent for adapting to the characteristics of LSOP and SACC. Empirical studies on IEEE CEC 2010 benchmark functions demonstrate that SACC significantly enhances the evaluation efficiency on sub-solutions, and even with much fewer computation resource, the resultant RBF-SHADE-SACC algorithm is able to find much better solutions than traditional CC algorithms. Keywords:Cooperative coevolution (CC); Large scale optimization problem (LSOP); Surrogate model; Radial basis function (RBF); Success-history based adaptive differential evolution (SHADE) Nowadays, large scale optimization problems (LSOPs) are becoming more and more popular in scientific research and engineering applications with the rapid development of big data techniques [1,2]. Since this kind of problems generally possesses black-box characteristics, the gradient-free evolutionary algorithms (EAs) are often employed to tackle them.However, the performance of conventional EAs rapidly deteriorates as the problem dimension increases. This is the so-called 'curse of dimensionality' [3,4], the main reason for which consists in that the solution space of a problem exponentially grows with the increase of its dimension and conventional EAs cannot adequately explore the solution space of a LSOP within acceptable computation time.Taking the idea of 'divide-and-conquer', cooperative coevolution (CC) provides a natural way for solving LSOPs [5]. It first decomposes an original LSOP into several smaller and simpler sub-problems, and then solves the LSOP by cooperatively optimizing all the sub-problems with a conventional EA. It is understandable that decomposition plays a fundamental ...

Study Design. A retrospective study Objective. The aim of this study was to investigate the ability of Global Alignment and Proportion (GAP) score to predict the occurrence of adjacent segment degeneration (ASD) after fusion surgery for lumbar degenerative diseases. Summary of Background Data. The recently developed GAP score was applied to predict postoperative complications for adult spinal deformity, as well as to facilitate future outcome-based research on optimal treatment for various spinal conditions. However, it remains unclear whether reconstruction of alignment according to GAP score can reduce the ASD rates. Methods. This study retrospectively reviewed 126 consecutive patients who had undergone lumbar fusion and had been followed over 2 years. Pre- and postoperative radiographs and MRI were analyzed for ASD. GAP scores were calculated based on the early postoperative spinopelvic parameters. Cochran-Armitage test of trend was performed to investigate the association between GAP score and the occurrence of ASD. Receiver-operating characteristic curves were used to analyze the predictive accuracy of the GAP score for ASD. Results. Radiographical ASD (R-ASD) and symptomatic ASD (S-ASD) were diagnosed in 44 (34.9%) patients and in 13 (10.3%) patients, respectively. The patients with a proportioned spinopelvic state according to the GAP score had significantly lower rates of ASD (R-ASD and S-ASD) or S-ASD than those with a moderately or severely disproportioned spinopelvic state. The area under curve for the GAP score predicting ASD and S-ASD was 0.691 (95% confidence interval [CI]: 0.596∼0.785, P < 0.01) and 0.865 (95% CI: 0.771∼0.958, P < 0.01), respectively. Conclusion. Our study revealed a significant association between postoperative GAP score and occurrence of ASD after lumbar fusion surgery. Setting surgical goals according to the GAP score may help reduce the occurrence of ASD, especially for S-ASD. Level of Evidence: 4