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Abstract Purpose The poor prognosis of adult patients with spinal deformity following long-segment spinal fusion surgery remains a major concern. Our study aims to investigate the impact of an Enhanced Recovery After Surgery (ERAS) protocol on the prognosis of adult patients with spinal deformity. Methods This study focused on a retrospective review of a database of previous adult spinal deformity. Adult patients with spinal deformity who underwent long-segment fusion surgery from July 2016 to July 2022 were evaluated, from July 2016 to July 2019 for the pre-ERAS patient group and from July 2019 to July 2022 for the ERAS group. Demographic data, radiological sagittal parameters, and intraoperative data were collected from all patients. The length of hospital stay, postoperative complications, and 90-day readmission rates were compared between the two groups. Additionally, multivariate regression models were used to analyze the predictors of postoperative length of stay, postoperative complications, and 90-day readmission rates. Results A total of 215 patients were included in this study, 102 patients in the pre-ERAS group and 113 patients in the ERAS group. Postoperative outcomes in the ERAS group included significantly lower postoperative length of stay (LOS) (13.09 ± 4.57 vs. 11.13 ± 4.16, P = 0.001); significantly lower rate of postoperative complications (52.0% vs. 29.2%, P

Functional magnetic resonance imaging (fMRI) have been widely adopted to explore the underlying neural mechanisms between psychiatric disorders which share common neurobiology and clinical manifestations. However, the existing studies mainly focus on linear relationships and ignore nonlinear contributions. To address the above issues, we propose a new method named multi-frequency decomposition entropy (MDE) learning for inferring nonlinear functional connectivity between brain regions. Firstly, the variational mode decomposition was used to divide fMRI data into five groups of frequency. Next, the copula entropy was used to calculate the nonlinear relationship between brain regions in each frequency group, and then the best important nonlinear relationships were screen out by using statistical t-test. Lastly, a gyrus importance index was proposed to reflect the distribution trend of gyri in different frequency groups. The results of applying MDE for the fMRI data analysis of schizophrenia, bipolar disorder, and attention-deficit hyperactivity disorder showed that the difference between the three groups of patient and healthy control is large at the hub nodes, and the nonlinear relationship between the patient groups is weak when they are at the same hub node. In addition, each disease exhibits unique characteristics compared with other diseases and healthy control. In a word, the nonlinear functional connectivity of different frequency groups reflect the differences and commonalities between diseases and reveal possible discriminating biomarkers among mental diseases.