The relationship between spinal alignment and activity of paravertebral muscle during gait in patients with adult spinal deformity: a retrospective study

Clinical Outcomes and Risk Factors Associated with Spinal Kyphotic Deformity Following Osteoporotic Vertebral Fracture

Background: Osteoporotic vertebral fractures (OVFs) often lead to poor global sagittal alignment (GSA) and reduced quality of life (QOL). While pseudarthrosis and kyphotic deformities are well-known predictors of conservative treatment failure, the impact of vertebral collapse, paraspinal muscle degeneration, sarcopenia, and nutritional status on GSA remains unclear. This study investigated the relationship between these factors and GSA in patients with conservatively treated OVFs. Methods: This post hoc analysis of a multicenter prospective observational study included 70 patients (single OVF; age ≥ 60 years; 12-month follow-up). Radiographic parameters, paraspinal muscle degeneration, and nutritional status were assessed. GSA was categorized based on the sagittal vertical axis (SVA [mm]): normal (SVA ≤ 40), moderate (40 ≤ SVA ≤ 95), and severe (SVA > 95). Clinical outcomes were assessed using the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire (JOABPEQ), Oswestry Disability Index (ODI), and visual analog scale (VAS). Results: At 12 months, 22.9% of patients had severe GSA and showed significantly lower JOABPEQ gait dysfunction scores (p = 0.01), higher ODI scores (p < 0.01), and reduced lower lumbar lordosis (p = 0.01). A higher prevalence of lower lumbar OVFs, including prior fractures, was observed in the severe group. No significant correlations were found between GSA and paraspinal muscle degeneration or nutritional status. Conclusions: OVFs in the lower lumbar spine significantly contributed to GSA deterioration. This indicates their critical role in sagittal alignment. Although paraspinal muscle degeneration and malnutrition are common in OVFs, their direct impact on GSA is limited. These findings highlight the need for targeted strategies to manage lumbar OVFs and prevent sagittal malalignment.

Return to Activities of Daily Living After Fusion to the Pelvis for Adult Spinal Deformity

STUDY DESIGN

Cross-sectional survey and retrospective review of prospectively collected data.

OBJECTIVE

To investigate return to activities among patients with adult spinal deformity (ASD) after long-segment fusion to the pelvis.

BACKGROUND

No prior studies have assessed the extent and timing of return to employment, driving, and walking among a single cohort of patients with ASD.

PATIENTS AND METHODS

Patients who underwent thoracolumbar ASD surgery between 2016 and 2021 with ≥ 1-year follow-up were included (posterior-only, ≥3 levels of fusion to pelvis). A cross-sectional survey was implemented to evaluate preoperative and postoperative activity tolerance. Patients were categorized into (1) better/unchanged or (2) worse groups based on their postoperative activity tolerance to allow for comparison of demographics and perioperative variables.

RESULTS

Ninety-five patients were included [mean age: 64.3 ± 10.1 yr; body mass index (BMI): 27.3 ± 6.1 kg/m2; levels fused: 8 (range: 3-16); follow-up: 43.5 mo]. Most patients endorsed improved capacity to walk (improved: 64.2%, unchanged: 17.9%, worse: 17.9%) and navigate stairs (improved: 52.6%, unchanged: 33.7%, worse: 13.7%) postoperatively. Seventy-five (97.4%) patients returned to driving (4.1 ± 10.8 mo) and 44 (88.0%) patients returned to work (5.4 ± 8.0 mo). Patients with decreased walking tolerance were more likely to have greater lumbar lordosis correction (37.2 ± 10.5° vs. 18.6 ± 16.7°, P = 0.02) and worse Patient-Reported Outcomes Measurement Information System-Physical Function at long-term follow-up (40.2 ± 11.0 vs. 48.0 ± 9.6, P = 0.03). Patients with decreased ability to navigate stairs were more likely to have undergone revision fusion (69.2% vs. 28.0%, P = 0.003) and have greater BMI (30.7 ± 5.8 vs. 26.7 ± 6.0 kg/m2, P = 0.04). Patients requiring the use of a postoperative assistive walking device were more likely to have undergone revision fusion (66.7% vs. 27.5%, P = 0.003), exhibit greater BMI (31.4 ± 7.2 vs. 26.5 ± 5.6 kg/m2, P = 0.004), longer operative times (285.1 ± 79.9 vs. 244.5 ± 63.4 min, P = 0.03), and worse Patient-Reported Outcomes Measurement Information System-Physical Function at long-term follow-up (39.9 ± 5.1 vs. 47.7 ± 10.5, P = 0.04).

CONCLUSION

Despite the reduced range of motion caused by spinopelvic fusion, a majority of patients are able to successfully return to activities of daily living after deformity surgery.

Two-stage video-based convolutional neural networks for adult spinal deformity classification

Introduction

Assessment of human gait posture can be clinically effective in diagnosing human gait deformities early in life. Currently, two methods-static and dynamic-are used to diagnose adult spinal deformity (ASD) and other spinal disorders. Full-spine lateral standing radiographs are used in the standard static method. However, this is a static assessment of joints in the standing position and does not include information on joint changes when the patient walks. Careful observation of long-distance walking can provide a dynamic assessment that reveals an uncompensated posture; however, this increases the workload of medical practitioners. A three-dimensional (3D) motion system is proposed for the dynamic method. Although the motion system successfully detected dynamic posture changes, access to the facilities was limited. Therefore, a diagnostic approach that is facility-independent, has low practice flow, and does not involve patient contact is required.

Methods

We focused on a video-based method to classify patients with spinal disorders either as ASD, or other forms of ASD. To achieve this goal, we present a video-based two-stage machine-learning method. In the first stage, deep learning methods are used to locate the patient and extract the area where the patient is located. In the second stage, a 3D CNN (convolutional neural network) device is used to capture spatial and temporal information (dynamic motion) from the extracted frames. Disease classification is performed by discerning posture and gait from the extracted frames. Model performance was assessed using the mean accuracy, F1 score, and area under the receiver operating characteristic curve (AUROC), with five-fold cross-validation. We also compared the final results with professional observations.

Results

Our experiments were conducted using a gait video dataset comprising 81 patients. The experimental results indicated that our method is effective for classifying ASD and other spinal disorders. The proposed method achieved a mean accuracy of 0.7553, an F1 score of 0.7063, and an AUROC score of 0.7864. Additionally, ablation experiments indicated the importance of the first stage (detection stage) and transfer learning of our proposed method.

Discussion

The observations from the two doctors were compared using the proposed method. The mean accuracies observed by the two doctors were 0.4815 and 0.5247, with AUROC scores of 0.5185 and 0.5463, respectively. We proved that the proposed method can achieve accurate and reliable medical testing results compared with doctors' observations using videos of 1 s duration. All our code, models, and results are available at https://github.com/ChenKaiXuSan/Walk_Video_PyTorch. The proposed framework provides a potential video-based method for improving the clinical diagnosis for ASD and non-ASD. This framework might, in turn, benefit both patients and clinicians to treat the disease quickly and directly and further reduce facility dependency and data-driven systems.