Healthcare burden changes by restricted physical activities in lumbar spinal stenosis and spondylolisthesis: a retrospective large cohort study during the COVID-19 pandemic.

BACKGROUND: Lumbar spinal stenosis (LSS) and spondylolisthesis (SPL) are characterized as degenerative spinal pathologies and share considerable similarities. However, opinions vary on whether to recommend exercise or restrict it for these diseases. Few studies have objectively compared the effects of daily physical activity on LSS and SPL because it is impossible to restrict activities ethnically and practically. We investigated the effect of restricting physical activity due to social distancing (SoD) on LSS and SPL, focusing on the aspect of healthcare burden changes during the pandemic period. METHODS: We included first-visit patients diagnosed exclusively with LSS and SPL in 2017 and followed them up for two years before and after the implementation of the SoD policy. As controls, patients who first visited in 2015 and were followed for four years without SoD were analyzed. The common data model was employed to analyze each patient's diagnostic codes and treatments. Hospital visits and medical costs were analyzed by regression discontinuity in time to control for temporal effects on dependent variables. RESULTS: Among 33,484 patients, 2,615 with LSS and 446 with SPL were included. A significant decrease in hospital visits was observed in the LSS (difference, -3.94 times/month·100 patients; p = 0.023) and SPL (difference, -3.44 times/month·100 patients; p = 0.026) groups after SoD. This decrease was not observed in the data from the control group. Concerning medical costs, the LSS group showed a statistically significant reduction in median copayment (difference, -$45/month·patient; p < 0.001) after SoD, whereas a significant change was not observed in the SPL group (difference, -$19/month·patient; p = 0.160). CONCLUSION: Restricted physical activity during the SoD period decreased the healthcare burden for patients with LSS or, conversely, it did not significantly affect patients with SPL. Under circumstances of physical inactivity, patients with LSS may underrate their symptoms, while maintaining an appropriate activity level may be beneficial for patients with SPL.

Minimally invasive surgical technique for unstable supracondylar humerus fractures in children (Gartland type III or IV).

Background: Achieving and maintaining anatomical reduction during the treatment of pediatric humerus fractures, classified as Gartland type III or IV, presents a clinical challenge. Herein, we present a minimally invasive surgical approach using a novel and simple K-wire push technique that aids in achieving and maintaining anatomical reduction. Methods: We reviewed data of children receiving treatment for supracondylar fractures of the humerus at our hospital between January 2016 and December 2020. Patients were divided into two groups based on the method of treatment: Group 1 was treated with the K-wire push technique, and Group 2 was treated with the standard technique as described by Rockwood and Wilkins. The medical records and radiographic images were reviewed. In total, 91 patients with Gartland types III and IV fractures were included, with 37 and 54 patients in Groups 1 and 2, respectively. Results: The postoperative reduction radiographic parameters and Flynn scores at final follow-up were not significantly different between the two groups. Conclusion: The minimally invasive K-wire push technique for unstable supracondylar fractures in children is a safe and effective alternative for improving reduction. Using this technique, complications can be minimized, and the requirement for open reduction can be reduced.

Association between Percent Body Fat and Low High-Density Lipoproteinemia in Middle-Aged Men in Korea.

Background: Obesity is a significant health risk factor for cardiovascular diseases. Dyslipidemia, defined as a low high-density lipoprotein cholesterol (HDL-C) level, is associated with these risks. Recent bioelectrical impedance analysis (BIA) devices offer precise measurements of the percent body fat (PBF). We aimed to determine the association between PBF and HDL-C levels in middle-aged men in Korea. Methods: We conducted a cross-sectional sstudy of men aged 40-65 years who visited a health examination center. Body composition was analyzed using BIA. Health habits were assessed using a self-administered questionnaire. The participants were divided into four groups based on their PBF: group 1 (<21%), group 2 (21%-23.99%), group 3 (24%-28.99%), and group 4 (≥29%). Logistic regression was used to obtain the odds ratio (OR) between the PBF group and the low HDL-C level and adjusted for other variables. Results: In this study, 2,685 men were analyzed. The number of individuals diagnosed with low HDL-C levels increased significantly as the group-specific PBF increased. Group 4 showed a 5.5-fold greater association with low HDL-C compared to group 1 (P<0.01), whereas group 3 and group 2 showed an OR of 4.38 and 2.95 (P<0.01 and P<0.01), respectively. Conclusion: These results suggest that if middle-aged men are able to decrease their body fat by <5%, their HDL-C levels will increase. We suggest that 3%-5% PBF is a useful guideline for general body fat reduction in Korean middle- aged men in primary care.

BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification.

Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.

Single-position oblique lumbar interbody fusion with navigation: improved efficiency and screw accuracy compared to dual-position with fluoroscopy.

Dual-position oblique lumbar interbody fusion with fluoroscopy (D-OLIF) requires repositioning the patient to a prone position for pedicle screw insertion. Recently, single-position surgery with navigation has been introduced. However, there are concerns regarding pedicle screw accuracy and achieving appropriate sagittal balance in single-position OLIF with navigation (S-OLIF). The purpose of this study is to evaluate the clinical and radiological outcomes of S-OLIF compared to D-OLIF. A retrospective analysis was conducted on 102 patients who underwent single-level OLIF at a single institution. The patients were divided into two groups: 55 in the S-OLIF group and 47 in the D-OLIF group. The numeric rating scale for back and leg, Oswestry disability index, and walking distance improvements showed no significant difference. However, the EuroQol 5-dimension 5-level index showed higher improvement in the S-OLIF (P = 0.029). The segmental lordosis, lumbar lordosis, and C7 sagittal vertical axis showed no significant difference. S-OLIF had significantly fewer cases of pedicle screw malposition (P = 0.045). Additionally, the surgery time was shorter in the S-OLIF (P = 0.002). In conclusion, S-OLIF exhibited clinical and radiological outcomes comparable to D-OLIF, with the added advantages of reduced surgery time and enhanced accuracy in pedicle screw placement.

Human-airway surface mesh smoothing based on graph convolutional neural networks.

BACKGROUND AND OBJECTIVE: A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations. METHOD: The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties. RESULTS: In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method. CONCLUSIONS: The airway model constructed by the AMSL method reproduces branch diameters accurately without any shrinkage, especially in the case of smaller airways. The accurate estimation of airway geometry using a smoothing method is critical for estimating flow properties in CFD simulations.

Screening patients requiring secondary lumbar surgery for degenerative lumbar spine diseases: a nationwide sample cohort study.

This study aims to identify healthcare costs indicators predicting secondary surgery for degenerative lumbar spine disease (DLSD), which significantly impacts healthcare budgets. Analyzing data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC) database of Republic of Korea (ROK), the study included 3881 patients who had surgery for lumbar disc herniation (LDH), lumbar spinal stenosis without spondylolisthesis (LSS without SPL), lumbar spinal stenosis with spondylolisthesis (LSS with SPL), and spondylolysis (SP) from 2006 to 2008. Patients were categorized into two groups: those undergoing secondary surgery (S-group) and those not (NS-group). Surgical and interim costs were compared, with S-group having higher secondary surgery costs ($1829.59 vs $1618.40 in NS-group, P = 0.002) and higher interim costs ($30.03; 1.86% of initial surgery costs vs $16.09; 0.99% of initial surgery costs in NS-group, P < 0.0001). The same trend was observed in LDH, LSS without SPL, and LSS with SPL (P < 0.0001). Monitoring interim costs trends post-initial surgery can effectively identify patients requiring secondary surgery.

Trends in degenerative lumbar spinal surgery during the early COVID-19 pandemic in Republic of Korea: A national study utilizing the national health insurance database.

During the first year of the COVID-19 pandemic, the Republic of Korea (ROK) experienced three epidemic waves in February, August, and November 2020. These waves, combined with the overarching pandemic, significantly influenced trends in spinal surgery. This study aimed to investigate the trends in degenerative lumbar spinal surgery in ROK during the early COVID-19 pandemic, especially in relation to specific epidemic waves. Using the National Health Information Database in ROK, we identified all patients who underwent surgery for degenerative lumbar spinal diseases between January 1, 2019 and December 31, 2020. A joinpoint regression was used to assess temporal trends in spinal surgeries over the first year of the COVID-19 pandemic. The number of surgeries decreased following the first and second epidemic waves (p<0.01 and p = 0.34, respectively), but these were offset by compensatory increases later on (p<0.01 and p = 0.05, respectively). However, the third epidemic wave did not lead to a decrease in surgical volume, and the total number of surgeries remained comparable to the period before the pandemic. When compared to the pre-COVID-19 period, average LOH was reduced by 1 day during the COVID-19 period (p<0.01), while mean hospital costs increased significantly from 3,511 to 4,061 USD (p<0.01). Additionally, the transfer rate and the 30-day readmission rate significantly decreased (both p<0.01), while the reoperation rate remained stable (p = 0.36). Despite the impact of epidemic waves on monthly surgery numbers, a subsequent compensatory increase was observed, indicating that surgical care has adapted to the challenges of the pandemic. This adaptability, along with the stable total number of operations, highlights the potential for healthcare systems to continue elective spine surgery during public health crises with strategic resource allocation and patient triage. Policies should ensure that surgeries for degenerative spinal diseases, particularly those not requiring urgent care but crucial for patient quality of life, are not unnecessarily halted.

Immunogenic Extracellular Vesicles Derived from Endoplasmic Reticulum-Stressed Tumor Cells: Implications as the Therapeutic Cancer Vaccine.

Tumor-derived extracellular vesicles (TDEs) have potential for therapeutic cancer vaccine applications since they innately possess tumor-associated antigens, mediate antigen presentation, and can incorporate immune adjuvants for enhanced vaccine efficacy. However, the original TDEs also contain immune-suppressive proteins. To address this, we proposed a simple yet powerful preconditioning method to improve the overall immunogenicity of the TDEs. This approach involved inducing endoplasmic reticulum (ER) stress on parental tumor cells via N-glycosylation inhibition with tunicamycin. The generated immunogenic TDEs (iTDEs) contained down-regulated immunosuppressive proteins and up-regulated immune adjuvants, effectively activating dendritic cells (DCs) in vitro. Furthermore, in vivo evidence from a tumor-bearing mouse model showed that iTDEs activated DCs, enabling cytotoxic T lymphocytes (CTLs) to target tumors, and eventually established a systemic antitumor immune response. Additionally, iTDEs significantly delayed tumor recurrence in a postsurgery model compared with control groups. These findings highlight the immense potential of our strategy for utilizing TDEs to develop effective cancer vaccines.

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9-91.8%) for internal validation. The AUROC of the MP-CNN model were 91.3% (95% CI 90.7-91.9%) and 79.5% (95% CI 78.2-80.8%) for the extra-internal and external validation, respectively. The MP-CNN based heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.

The utility of intraoperative ultrasonography for spinal cord surgery.

OBJECTIVES: Intraoperative ultrasonography (IOUS) offers the advantage of providing real-time imaging features, yet it is not generally used. This study aims to discuss the benefits of utilizing IOUS in spinal cord surgery and review related literature. MATERIALS AND METHODS: Patients who underwent spinal cord surgery utilizing IOUS at a single institution were retrospectively collected and analyzed to evaluate the benefits derived from the use of IOUS. RESULTS: A total of 43 consecutive patients were analyzed. Schwannoma was the most common tumor (35%), followed by cavernous angioma (23%) and ependymoma (16%). IOUS confirmed tumor extent and location before dura opening in 42 patients (97.7%). It was particularly helpful for myelotomy in deep-seated intramedullary lesions to minimize neural injury in 13 patients (31.0% of 42 patients). IOUS also detected residual or hidden lesions in 3 patients (7.0%) and verified the absence of hematoma post-tumor removal in 23 patients (53.5%). In 3 patients (7.0%), confirming no intradural lesions after removing extradural tumors avoided additional dural incisions. IOUS identified surrounding blood vessels and detected dural defects in one patient (2.3%) respectively. CONCLUSIONS: The IOUS can be a valuable tool for spinal cord surgery in identifying the exact location of the pathologic lesions, confirming the completeness of surgery, and minimizing the risk of neural and vascular injury in a real-time fashion.

Surgical Strategy for Dumbbell-Shaped Cervical Schwannoma at the Vicinity of the Vertebral Artery: The Utilization of Anatomic Layer.

BACKGROUND AND OBJECTIVES: In cases where dumbbell-shaped cervical schwannoma encases the vertebral artery (VA), there is a risk of VA injury during surgery. The objective of this study is to propose a strategy for preserving the VA during the surgical excision of tumors adjacent to the VA through the utilization of anatomic layers. METHODS: A retrospective analysis was conducted on 37 patients who underwent surgery for dumbbell-shaped cervical schwannoma with contacting VA from January 2004 to July 2023. The VA encasement group consisted of 12 patients, and the VA nonencasement group included 25 patients. RESULTS: The perineurium acted as a protective barrier from direct VA exposure or injury during surgery. However, in the VA encasement group, 1 patient was unable to preserve the perineurium while removing a tumor adjacent to the VA, resulting in VA injury. The patient had the intact dominant VA on the opposite side, and there were no new neurological deficits or infarctions after the surgery. Gross total resection was achieved in 25 patients (67.6%), while residual tumor was confirmed in 12 patients (32.4%). Four patients (33.3% of 12 patients) underwent reoperation because of the regrowth of the residual tumor within the neural foramen. In the case of the 8 patients (66.7% of 12 patients) whose residual tumor was located outside the neural foramen, no regrowth was observed, and there was no recurrence of the tumor within the remaining perineurium after total resection. CONCLUSION: In conclusion, when resecting a dumbbell-shaped cervical schwannoma contacting VA, subperineurium dissection prevents VA injury because the perineurium acts as a protective barrier.

Development and Validation of an Online Calculator to Predict Proximal Junctional Kyphosis After Adult Spinal Deformity Surgery Using Machine Learning.

OBJECTIVE: Although adult spinal deformity (ASD) surgery aims to restore and maintain alignment, proximal junctional kyphosis (PJK) may occur. While existing scoring systems predict PJK, they predominantly offer a generalized 3-tier risk classification, limiting their utility for nuanced treatment decisions. This study seeks to establish a personalized risk calculator for PJK, aiming to enhance treatment planning precision. METHODS: Patient data for ASD were sourced from the Korean spinal deformity database. PJK was defined a proximal junctional angle (PJA) of ≥ 20° at the final follow-up, or an increase in PJA of ≥ 10° compared to the preoperative values. Multivariable analysis was performed to identify independent variables. Subsequently, 5 machine learning models were created to predict individualized PJK risk post-ASD surgery. The most efficacious model was deployed as an online and interactive calculator. RESULTS: From a pool of 201 patients, 49 (24.4%) exhibited PJK during the follow-up period. Through multivariable analysis, postoperative PJA, body mass index, and deformity type emerged as independent predictors for PJK. When testing machine learning models using study results and previously reported variables as hyperparameters, the random forest model exhibited the highest accuracy, reaching 83%, with an area under the receiver operating characteristics curve of 0.76. This model has been launched as a freely accessible tool at: (https://snuspine.shinyapps.io/PJKafterASD/). CONCLUSION: An online calculator, founded on the random forest model, has been developed to gauge the risk of PJK following ASD surgery. This may be a useful clinical tool for surgeons, allowing them to better predict PJK probabilities and refine subsequent therapeutic strategies.