Can semirigid fixation of the rostral instrumented segments prevent proximal junctional kyphosis in the case of long thoracolumbar fusions? A finite element study.

OBJECTIVE: Proximal junctional kyphosis (PJK) is a relatively common complication following long instrumented posterior spinal fusion. Although several risk factors have been identified in the literature, previous biomechanical studies suggest that one of the leading causes is the sudden change in mobility between the instrumented and noninstrumented segments. The current study aims to assess the biomechanical effect of 1 rigid and 2 semirigid fixation techniques (SFTs) on developing PJK. METHODS: Four T7-L5 finite element (FE) models were developed: 1) intact spine; 2) 5.5-mm titanium rod from T8 to L5 (titanium rod fixation [TRF]); 3) multiple rods from T8 to T9 connected with titanium rod from T9 to L5 (multiple-rod fixation [MRF]); and 4) polyetheretherketone rod from T8 to T9 connected with titanium rod from T9 to L5 (PEEK rod fixation [PRF]). A modified multidirectional hybrid test protocol was used. First, a pure bending moment of 5 Nm was applied to measure the intervertebral rotation angles. Second, the TRF technique's displacement from the first loading step was applied to the instrumented FE models to compare the pedicle screw stress values in the upper instrumented vertebra (UIV). RESULTS: In the load-controlled step, at the upper instrumented segment, the intervertebral rotation values relative to TRF increased by 46.8% and 99.2% for flexion, by 43.2% and 87.7% for extension, by 90.1% and 137% for lateral bending, and by 407.1% and 585.2% for axial rotation, in the case of MRF and PRF, respectively. In the displacement-controlled step, maximum pedicle screw stress values at the UIV level were highest in the case of TRF (37.26 MPa, 42.13 MPa, 44.4 MPa, and 44.59 MPa for flexion, extension, lateral bending, and axial rotation, respectively). Compared to TRF, in the case of MRF and PRF, the screw stress values were reduced by 17.3% and 27.7% for flexion, by 26.6% and 36.7% for extension, by 6.8% and 34.3% for lateral bending, and by 49.1% and 59.8% for axial rotation, respectively. CONCLUSIONS: FE analysis has shown that the SFTs increase the mobility at the upper instrumented segment and therefore provide a more gradual transition in motion between the instrumented and rostral noninstrumented segments of the spine. In addition, SFTs decrease the screw loads at the UIV level and hence could help reduce the risk for PJK. However, further investigations are recommended to evaluate the long-term clinical usefulness of these techniques.

3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images.

Computational medicine aims at employing personalised computational models in diagnosis and treatment planning. The use of such models to help physicians in finding the best treatment for low back pain (LBP) is becoming popular. One of the challenges of creating such models is to derive patient-specific anatomical and tissue models of the lumbar intervertebral discs (IVDs), as a prior step. This article presents a segmentation scheme that obtains accurate results irrespective of the degree of IVD degeneration, including pathological discs with protrusion or herniation. The segmentation algorithm, employing a novel feature selector, iteratively deforms an initial shape, which is projected into a statistical shape model space at first and then, into a B-Spline space to improve accuracy.The method was tested on a MR dataset of 59 patients suffering from LBP. The images follow a standard T2-weighted protocol in coronal and sagittal acquisitions. These two image volumes were fused in order to overcome large inter-slice spacing. The agreement between expert-delineated structures, used here as gold-standard, and our automatic segmentation was evaluated using Dice Similarity Index and surface-to-surface distances, obtaining a mean error of 0.68 mm in the annulus segmentation and 1.88 mm in the nucleus, which are the best results with respect to the image resolution in the current literature.

Primary Spinal Tumor Mortality Score (PSTMS): a novel scoring system for predicting poor survival.

BACKGROUND CONTEXT: Although the surgical and oncological therapies of primary spinal tumors (PSTs) have changed significantly over the last few decades, the prognosis of this rare disease is still poor. The decision-making process in the multidisciplinary management is handicapped by the lack of large-scale population-based prognostic studies. PURPOSE: The objective of the present study was to investigate preoperative factors associated with PST mortality and to develop a predictive scoring system of poor survival. STUDY DESIGN: This is a large-scale ambispective cohort study. PATIENT SAMPLE: The study included 323 consecutive patients with PSTs, treated surgically over an 18-year period at a tertiary care spine referral center for a population of 10 million. OUTCOME MEASURE: Survival was the outcome measure. METHODS: Patients were randomly divided into a training cohort (n=273) and a validation cohort (n=50). In the training cohort, 12 preoperative factors were investigated using Cox proportional hazard models. Based on the mortality-related variables, a simple scoring system of mortality was created, and three groups of patients were identified. Kaplan-Meier and log-rank analyses were used to compare the survival in the three groups. The model performance was assessed by measuring the discriminative ability (c-index) of the model and by applying a pseudo-R(2) goodness-of-fit test (Nagelkerke R(2), RN(2)). Internal validation was performed using bootstrapping in the training cohort and assessing the discrimination and explained variation of the model in the validation cohort. RESULTS: Patient age, spinal region, tumor grade, spinal pain, motor deficit, and myelopathy/cauda equina syndrome were significantly associated with poor survival in the multivariate analysis (p<.001, RN(2)=0.799). Based on these variables, we developed the Primary Spinal Tumor Mortality Score (PSTMS), where an eight-point scale was divided into three categories (low, medium, and high mortality). The three PSTMS categories were significantly associated with the overall survival (p<.001, RN(2)=0.811, c=0.82). The model performance remained similarly high in the validation cohort (RN(2)=0.831, c=0.81). CONCLUSIONS: The present study identifies six predictive variables for mortality in PSTs. Using these six variables, an easy-to-use scoring system was developed that can be applied to the estimation of postoperative survival in all types of PST patients.