Periostin increased by mechanical stress upregulates interleukin-6 expression in the ligamentum flavum.

Ligamentum flavum (LF) hypertrophy is a major cause of lumbar spinal canal stenosis. Although mechanical stress is thought to be a major factor involved in LF hypertrophy, the exact mechanism by which it causes hypertrophy has not yet been fully elucidated. Here, changes in gene expression due to long-term mechanical stress were analyzed using RNA-seq in a rabbit LF hypertrophy model. In combination with previously reported analysis results, periostin was identified as a molecule whose expression fluctuates due to mechanical stress. The expression and function of periostin were further investigated using human LF tissues and primary LF cell cultures. Periostin was abundantly expressed in human hypertrophied LF tissues, and periostin gene expression was significantly correlated with LF thickness. In vitro, mechanical stress increased gene expressions of periostin, transforming growth factor-ß1, α-smooth muscle actin, collagen type 1 alpha 1, and interleukin-6 (IL-6) in LF cells. Periostin blockade suppressed the mechanical stress-induced gene expression of IL-6 while periostin treatment increased IL-6 gene expression. Our results suggest that periostin is upregulated by mechanical stress and promotes inflammation by upregulating IL-6 expression, which leads to LF degeneration and hypertrophy. Periostin may be a pivotal molecule for LF hypertrophy and a promising therapeutic target for lumbar spinal stenosis.

Advanced glycation end products in musculoskeletal system and disorders.

The human population is ageing globally, and the number of old people is increasing yearly. Diabetes is common in the elderly, and the number of diabetic patients is also increasing. Elderly and diabetic patients often have musculoskeletal disorder, which are associated with advanced glycation end products (AGEs). AGEs are heterogeneous molecules derived from non-enzymatic products of the reaction of glucose or other sugar derivatives with proteins or lipids, and many different types of AGEs have been identified. AGEs are a biomarker for ageing and for evaluating disease conditions. Fluorescence, spectroscopy, mass spectrometry, chromatography, and immunological methods are commonly used to measure AGEs, but there is no standardized evaluation method because of the heterogeneity of AGEs. The formation of AGEs is irreversible, and they accumulate in tissue, eventually causing damage. AGE accumulation has been confirmed in neuromusculoskeletal tissues, including bones, cartilage, muscles, tendons, ligaments, and nerves, where they adversely affect biomechanical properties by causing charge changes and forming cross-linkages. AGEs also bind to receptors, such as the receptor for AGEs (RAGE), and induce inflammation by intracellular signal transduction. These mechanisms cause many varied aging and diabetes-related pathological conditions, such as osteoporosis, osteoarthritis, sarcopenia, tendinopathy, and neuropathy. Understanding of AGEs related pathomechanism may lead to develop novel methods for the prevention and therapy of such disorders which affect patients' quality of life. Herein, we critically review the current methodology used for detecting AGEs, and present potential mechanisms by which AGEs cause or exacerbate musculoskeletal disorders.

Relationship between body mass index and spinal pathology in community-dwelling older adults.

PURPOSE: To clarify the relationship between body mass index (BMI) and spinal pathologies including spinal sagittal balance, back extensor strength (BES), paraspinal muscle mass, prevalent vertebral fracture, disc degeneration, Modic changes, low back pain, and quality of life (QOL) in community-dwelling older adults. METHODS: This study included 380 participants (age: ≥ 65 years, male/female: 152/228) from the Shiraniwa Study. Multivariate nonlinear regression analysis was used to investigate the relationship between BMI and sagittal vertical axis (SVA), BES, paraspinal muscle mass, visual analog scale (VAS) for low back pain, Oswestry Disability Index (ODI), and EuroQoL-5 Dimension (EQ5D) score after adjusting for sex, age, Hospital Anxiety and Depression Scale score, and Charlson Comorbidity Index. In addition, multiple logistic regression analysis was used to investigate the association between BMI and prevalent vertebral fracture, disc degeneration, and Modic changes. RESULTS: BMI was significantly correlated with SVA, BES, paraspinal muscle mass, VAS, ODI, and EQ5D score. The increase in BMI was associated with the deterioration of all outcomes, which accelerated when the BMI increased from approximately 22-23 kg/m2. Moreover, overweight/obesity was significantly correlated with disc degeneration and Modic changes. CONCLUSION: Increased BMI is significantly associated with spinal pathologies such as SVA, BES, paraspinal muscle mass, VAS, QOL, disc degeneration, and Modic changes. The findings suggest that measures for controlling overweight and obesity among older adults can play an important role in the prevention and treatment of spinal pathologies.

Machine-learning-based approach for nonunion prediction following osteoporotic vertebral fractures.

PURPOSE: An osteoporotic vertebral fracture (OVF) is a common disease that causes disabilities in elderly patients. In particular, patients with nonunion following an OVF often experience severe back pain and require surgical intervention. However, nonunion diagnosis generally takes more than six months. Although several studies have advocated the use of magnetic resonance imaging (MRI) observations as predictive factors, they exhibit insufficient accuracy. The purpose of this study was to create a predictive model for OVF nonunion using machine learning (ML). METHODS: We used datasets from two prospective cohort studies for OVF nonunion prediction based on conservative treatment. Among 573 patients with acute OVFs exceeding 65 years in age enrolled in this study, 505 were analyzed. The demographic data, fracture type, and MRI observations of both studies were analyzed using ML. The ML architecture utilized in this study included a logistic regression model, decision tree, extreme gradient boosting (XGBoost), and random forest (RF). The datasets were processed using Python. RESULTS: The two ML algorithms, XGBoost and RF, exhibited higher area under the receiver operating characteristic curves (AUCs) than the logistic regression and decision tree models (AUC = 0.860 and 0.845 for RF and XGBoost, respectively). The present study found that MRI findings, anterior height ratio, kyphotic angle, BMI, VAS, age, posterior wall injury, fracture level, and smoking habit ranked as important features in the ML algorithms. CONCLUSION: ML-based algorithms might be more effective than conventional methods for nonunion prediction following OVFs.

Neck pain and related factors in patients with rheumatoid arthritis.

OBJECTIVES: In rheumatoid arthritis, neck pain can be caused by inflammatory reactions or cervical lesions, but the prevalence and associated factors have not been well studied. This study aimed to investigate the prevalence of neck pain in patients with rheumatoid arthritis and elucidate the related factors. METHODS: This study included 146 patients with rheumatoid arthritis. Neck pain, quality of life, and levels of anxiety and depression were evaluated using a questionnaire. Cervical lesions and spinal alignment were evaluated using plain radiograph and magnetic resonance imaging. Factors associated with neck pain were analysed using a logistic regression model. RESULTS: Fifty-six per cent of the patients had neck pain, and the quality of life scores were significantly worse in these patients. Multivariate analysis revealed age, C7 sagittal vertical axis, upper cervical lesion, and endplate erosion as factors associated with neck pain in patients with rheumatoid arthritis. CONCLUSIONS: More than half the patients with rheumatoid arthritis suffer from neck pain, and neck pain affects the quality of life and activities of daily living. Neck pain was associated with upper cervical lesion and endplate erosion suggesting the importance of radiological examination in patients with rheumatoid arthritis and neck pain.

Incidence of and risk factors for spondylolisthesis, scoliosis, and vertebral fracture in rheumatoid arthritis.

INTRODUCTION: Although lumbar lesions such as spondylolisthesis, scoliosis, and vertebral fracture are not specific to rheumatoid arthritis (RA), the prevalence is high in RA patients. However, no longitudinal study has evaluated lumbar lesions in RA. This study aimed to investigate the incidence of and risk factors for lumbar lesions in RA by a prospective longitudinal cohort study. MATERIALS AND METHODS: The study cohort comprised 110 patients with RA from the 'analysis of factors for RA spinal disorders (AFFORD)' study who completed the secondary survey at a single orthopaedic outpatient RA clinic. Radiological examination included standing radiographs and magnetic resonance imaging (MRI) of the lumbar spine. New development of spondylolisthesis, scoliosis, and vertebral fracture were assessed between baseline and secondary survey. RESULTS: The incidences of spondylolisthesis, scoliosis, and vertebral fracture were 42%, 16%, and 12%, respectively, during a mean follow-up of 7 years. The independent risk factor for de novo scoliosis was poor control of RA (adjusted odds ratio [aOR] 4.81, p = 0.011), while the independent risk factors for new vertebral fracture was use of glucocorticoid at secondary survey (aOR 14.87, p = 0.012). Patients with de novo scoliosis exhibited more severe low back pain and lower quality of life than those without. CONCLUSION: The incidence of scoliosis was related in patients with poor control of RA, while new vertebral fracture was more common in patients with use of glucocorticoid. Control of disease activity might be important in preventing radiological lumbar disorders in RA.

Expression and function of fibroblast growth factor 1 in the hypertrophied ligamentum flavum of lumbar spinal stenosis.

BACKGROUND: Fibrosis is one of the main pathologies caused by hypertrophy of the ligamentum flavum (LF), which leads to lumbar spinal stenosis (LSS). The fibroblast growth factor (FGF) family is a key mediator of fibrosis. However, acidic fibroblast growth factor (FGF-1) expression and function are not well understood in LF. This study sought to evaluate FGF-1 expression in the hypertrophied and non-hypertrophied human LF, and to investigate its function using primary human LF cell cultures. METHODS: We obtained hypertrophied lumbar LF from LSS patients and non-hypertrophied lumbar LF from control patients during surgery. Immunohistochemistry and qPCR were performed to evaluate FGF-1 expression in LF tissue. The function of FGF-1 and transforming growth factor beta 1 (TGF-ß1) was also investigated using primary LF cell culture. The effects on cell morphology and cell proliferation were examined using a crystal violet staining assay and MTT assay, respectively. Immunocytochemistry, western blotting, and qPCR were performed to evaluate the effect of FGF-1 on TGF-ß1-induced myofibroblast differentiation and fibrosis. RESULTS: Immunohistochemistry and qPCR showed higher FGF-1 expression in hypertrophied LF compared to control LF. Crystal violet staining and MTT assay revealed that FGF-1 decreases LF cell size and inhibits their proliferation in a dose-dependent manner, whereas TGF-ß1 increases cell size and promotes proliferation. Immunocytochemistry and western blotting further demonstrated that TGF-ß1 increases, while FGF-1 decreases, α-SMA expression in LF cells. Moreover, FGF-1 also caused downregulation of collagen type 1 and type 3 expression in LF cells. CONCLUSION: FGF-1 is highly upregulated in the LF of LSS patients. Meanwhile, in vitro, FGF-1 exhibits antagonistic effects to TGF-ß1 by inhibiting cell proliferation and decreasing LF cell size as well as the expression of fibrosis markers. These results suggest that FGF-1 has an anti-fibrotic role in the pathophysiology of LF hypertrophy.

The health-related quality of life of patients with musculoskeletal disorders after the COVID-19 pandemic.

PURPOSE: To verify changes in the health-related quality of life (HRQOL) of patients with musculoskeletal disorders after the coronavirus disease 2019 (COVID-19) pandemic and to assess the relationship between the patients' change in several activities of daily living and in the HRQOL to discover factors related to the deterioration in HRQOL. METHODS: A multi-centre cross-sectional questionnaire survey was administered between November 1, 2020, and December 31, 2020, in Japan. The participants included those who visited the orthopaedics clinic within the survey period and had experienced the first and second waves of COVID-19 in Japan and the first stay-at-home order issued by the government. Patients' HRQOL at the two different time points (pre-outbreak and post-second wave of COVID-19) was assessed with the EuroQoL-5 dimensions 5-level (EQ-5D). RESULTS: The survey was completed by 1254 patients (average age: 52.5 ± 21.9 years; 644 women). Among them, 431 patients (34.3%) reported a decrease in the EQ-5D index after the pandemic. The largest decrease was in the pain domain followed by the mobility domain. Multivariate logistic regression analysis revealed that the patients with decreased regular exercise habits were significantly related to deterioration in HRQOL compared with those with stable regular exercise (adjusted odds ratio = 1.76, p < 0.001) independently from age, sex, and change of symptoms. CONCLUSIONS: Up to 35% of patients with musculoskeletal disorders reported deterioration of HRQOL after the COVID-19 pandemic. Pain and mobility rather than anxiety were the two leading factors of the HRQOL decrease. The decrease in regular exercise was related to the HRQOL decrease.

Risk factors for low back pain increase in rheumatoid arthritis: Analysis of a 7-year follow-up study.

OBJECTIVE: Several studies have demonstrated that low back pain (LBP) is related to disease activity in patients with rheumatoid arthritis (RA). However, there is no longitudinal research. This study aimed to determine the impacts and risk factors for LBP increase in RA in a longitudinal cohort study. METHODS: The study evaluated 113 patients with RA who completed the secondary survey. LBP increase was defined as ≥1 standard deviation of mean change in visual analogue scale (VAS) between the baseline and secondary surveys. The impacts of LBP increase on quality of life (QOL) and psychological status were evaluated. Risk factors were assessed among patient demographic characteristics and radiological changes. RESULTS: Mean change in VAS for LBP was -0.8 ± 30.4 mm during a mean 7-year follow-up. LBP increase was defined as ≥30-mm increase in VAS for LBP. Patients with LBP increase had significantly lower QOL and worse mental status than patients without it. Poor control of RA was identified as an independent risk factor for LBP increase (odds ratio, 9.82, p = .001). CONCLUSION: Patients with poor control of RA were likely to experience LBP increase in the long term. Control of RA disease activity is important for control of LBP, QOL, and mental status.

Classification and prognostic factors of residual symptoms after minimally invasive lumbar decompression surgery using a cluster analysis: a 5-year follow-up cohort study.

PURPOSE: Residual symptoms indicating incomplete remission of lower leg numbness or low back pain may occur after spine surgery. The purpose was to elucidate the pattern of residual symptoms 5 years after minimally invasive lumbar decompression surgery using a cluster analysis. METHODS: The study comprised 193 patients with lumbar spinal stenosis (LSS) (108 men, 85 women) ranging in age from 40 to 86 years (mean, 67.9 years). Each patient underwent 5-year follow-up. The Japanese Orthopedic Association score and visual analog scale scores for low back pain, leg pain, and leg numbness at 5 years were entered into the cluster analysis to characterize postoperative residual symptoms. Other clinical data were analyzed to detect the factors significantly related to each cluster. RESULTS: The analysis yielded four clusters representing different patterns of residual symptoms. Patients in cluster 1 (57.0%) were substantially improved and had few residual symptoms of LSS. Patients in cluster 2 (11.4%) were poorly improved and had major residual symptoms. Patients in cluster 3 (17.6%) were greatly improved but had mild residual low back pain. Patients in cluster 4 (14.0%) were improved but had severe residual leg numbness. Prognostic factors of cluster 2 were a short maximum walking distance, motor weakness, resting lower leg numbness, cofounding scoliosis, and high sagittal vertical axis. CONCLUSIONS: This is the first study to identify specific patterns of residual symptoms of LSS after decompression surgery. Our results will contribute to acquisition of preoperative informed consent and identification of patients with the best chance of postoperative improvement.

Predictors of dropout from cohort study due to deterioration in health status, with focus on sarcopenia, locomotive syndrome, and frailty: From the Shiraniwa Elderly Cohort (Shiraniwa) study.

BACKGROUND: Conditions of the elderly like sarcopenia, locomotive syndrome, and frailty have been attracting attention recently. However, the relationship of these 3 conditions and the difference in the magnitude of influence each has on deterioration in health status remain unclear. The purpose of this study was to investigate the prevalence and relationship of sarcopenia, locomotive syndrome, and frailty, and to clarify their influence on the dropout from cohort study due to deterioration in health status. METHODS: The 1st survey of the Shiraniwa Elderly Cohort (Shiraniwa) study was conducted in 2016. We examined 409 participants (aged ≥ 65 years; 164 males, 245 females) and assessed sarcopenia, locomotive syndrome, and frailty. Those who could not attend the 2nd survey (2017) due to deterioration in health status were defined as the Dropout group. We investigated the predictors of Dropout using multiple logistic regression analysis. RESULTS: The prevalence of sarcopenia, locomotive syndrome stage 2, and frailty were 4.4%, 40.1%, and 14.2%, respectively; 89.7% of participants with frailty were also diagnosed with locomotive syndrome stage 2. From the results of the 2nd survey, 46 people were classified into the Dropout group. Independent predictors of the Dropout were locomotive syndrome stage 2 (adjusted odds ratio [OR]: 2.42, 95% confidence interval [CI]: 1.11-5.31) and frailty (adjusted OR: 3.37, 95%CI: 1.56-7.30). CONCLUSIONS: Locomotive syndrome stage 2 was the most common condition in the elderly, and most people with frailty also had locomotive syndrome stage 2. Locomotive syndrome stage 2 and frailty independently influenced the dropout from cohort study due to deterioration in health status. These results suggest that screening for locomotive syndrome is useful for early detection of the elderly with a risk of deterioration in health status, and screening for frailty may contribute to detecting the elderly with higher risk of deterioration in health status.

Does sagittal imbalance impact the surgical outcomes of short-segment fusion for lumbar spinal stenosis associated with degenerative lumbar scoliosis?

BACKGROUND: The degenerative lumbar scoliosis (DLS) patients who mainly complained about neurogenic claudication due to spinal canal stenosis are well-indicated for short segment fusion (SSF) at the affecting levels. However, it is unclear whether we should consider global sagittal balance or not. The aim of this study was to evaluate the impact of sagittal balance on the surgical outcomes of degenerative lumbar scoliosis (DLS) patients who underwent SSF. METHODS: We retrospectively reviewed 70 DLS patients who underwent SSF (less than 3 levels) and could be followed for at least 2 years. The PI-LL, PT, SVA, and T1 pelvic angle (TPA) were measured using standing whole spine X-rays preoperatively (PreO) and at final follow-up (FFU). Surgical outcomes were assessed with the improvement in Japanese Orthopaedic Association score (JOAs) for low back pain (LBP), and the level of LBP was measured using the visual analogue scale (LBP-VAS). We analysed the relationships between the radiographic parameters and the surgical outcomes. RESULTS: We divided the patients into the three groups (poor/fair/good) based on the JOAs. The analysis with the Jonckheere-Terpstra trend test indicated that the following radiographic parameters had a significant trend with surgical outcomes in each group: (poor/fair/good; p value); PreO PI-LL (26/20/17°; P = 0.04), SVA (46/75/35.5 mm; P = 0.02), TPA (28/27/23°; p = 0.04), FFU PI-LL (33/25/8.5°; P = 0.004), SVA (93/90.5/32.5 mm; P = 0.001), and TPA (33/29/25°; P = 0.007). Additionally, LBP-VAS had a significant correlation between the three groups at final follow-up (P = 0.004). There were significant correlations between improvement in JOAs and PI-LL, SVA, and TPA both PreO and at FFU (P < 0.05). CONCLUSIONS: Sagittal spinal imbalance and spinopelvic malalignment significantly impact the surgical outcomes of SSF for DLS. Preoperative evaluation of spinopelvic alignment and sagittal balance is of critical importance when SSF are performed for DLS patients.

Short-Term Risk Factors for Distal Junctional Kyphosis after Spinal Reconstruction Surgery in Patients with Osteoporotic Vertebrae.

STUDY DESIGN: Level 3 retrospective cohort case-control study. PURPOSE: This study aimed to investigate the risk factors for distal junctional kyphosis (DJK) caused by osteoporotic vertebral fractures following spinal reconstruction surgery, with a focus on the sagittal stable vertebra. OVERVIEW OF LITERATURE: Despite the rarity of reports on DJK in this setting, DJK was reported to reduce when the lower instrumented vertebra (LIV) was extended to the sagittal stable vertebra in the posterior corrective fixation for Scheuermann's disease. METHODS: This study included 46 patients who underwent spinal reconstruction surgery for thoracolumbar osteoporotic vertebral fractures and kyphosis and were followed up for 1 year postoperatively. DJK was defined as an advanced kyphosis angle >10° between the LIV and one lower vertebra. The patients were divided into groups with and without DJK. The risk factors of the two groups, such as patient background, surgery-related factors, radiographic parameters, and clinical outcomes, were analyzed. RESULTS: The DJK and non-DJK groups included 14 and 32 patients, respectively, without significant differences in patient background. Those with instability in the distal adjacent LIV disc had a significantly higher risk of DJK occurrence (28.6% vs. 3.2%, p=0.027). DJK occurrence significantly increased in those with the sagittal stable vertebra not included in the fixation range (57.1% vs. 18.8%, p=0.020). Other preoperative radiographic parameters were not significantly different. Instability in the distal adjacent LIV disc (adjusted odds ratio, 14.50; p=0.029) and the exclusion of the sagittal stable vertebra from the fixation range (adjusted odds ratio, 5.29; p=0.020) were significant risk factors for DJK occurrence. CONCLUSIONS: Regarding spinal reconstruction surgery in patients with osteoporotic vertebral fractures, instability in the distal adjacent LIV disc and the exclusion of the sagittal stable vertebra from the fixation range were risk factors for DJK occurrence in the short term.

Degeneration of Cervical Multifidus Muscles Negatively Affects Physical Activity-related Quality of Life After Laminoplasty for Degenerative Cervical Myelopathy.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The study aimed to investigate the related factors affecting physical activity-related quality of life (QOL) after 2 years of cervical laminoplasty for degenerative cervical myelopathy (DCM), focusing on the degree of preoperative degeneration of the cervical multifidus muscles. SUMMARY OF BACKGROUND DATA: The association between paraspinal muscle degeneration and clinical outcomes after spinal surgery is being investigated. The effect of preoperative degeneration of the cervical multifidus muscles in patients undergoing cervical laminoplasty is ambiguous. METHODS: Patients who underwent laminoplasty for DCM and followed up for more than 2 years were reviewed. To evaluate physical QOL, the physical component summary (PCS) of the 36-Item Short-Form Health Survey (SF-36) was recorded at 2 years postoperatively. The degree of preoperative degeneration in the multifidus muscles at the C4 and C7 levels on axial T2-weighted magnetic resonance imaging (MRI) was categorized according to the Goutallier grading system. The correlation between 2-year postoperative PCS and each preoperative clinical outcome, radiographic parameter, and MRI finding, including Goutallier classification, was analyzed. Variables with a P value <0.10 in univariate analysis were included in multiple linear regression analysis. RESULTS: In total, 106 consecutive patients were included. The 2-year postoperative PCS demonstrated significant correlation with age (R=-0.358, P=0.002), preoperative JOA score (R=0.286, P=0.021), preoperative PCS (R=0.603, P<0.001), C2-C7 lordotic angle (R=-0.284, P=0.017), stenosis severity (R=-0.271, P=0.019), and Goutallier classification at the C7 level (R=-0.268, P=0.021). In multiple linear regression analysis, sex (ß=-0.334, P=0.002), age (ß=-0.299, P=0.013), preoperative PCS (ß=0.356, P=0.009), and Goutallier classification at the C7 level (ß=-0.280, P=0.018) were significantly related to 2-year postoperative PCS. CONCLUSIONS: Increased degeneration of the multifidus muscle at the C7 level negatively affected physical activity-related QOL postoperatively. These results may guide spine surgeons in predicting physical activity-related QOL in patients with DCM after laminoplasty. LEVEL OF EVIDENCE: Level III.

Deep learning-based detection of lumbar spinal canal stenosis using convolutional neural networks.

BACKGROUND CONTEXT: Lumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by primary care physicians or orthopedic surgeons who are not spine surgery specialists. Magnetic resonance imaging (MRI) is useful in the diagnosis of LSCS, but the equipment is often not available or difficult to read. LSCS patients with progressive neurologic deficits have difficulty with recovery if surgical treatment is delayed. So, early diagnosis and determination of appropriate surgical indications are crucial in the treatment of LSCS. Convolutional neural networks (CNNs), a type of deep learning, offers significant advantages for image recognition and classification, and work well with radiographs, which can be easily taken at any facility. PURPOSE: Our purpose was to develop an algorithm to diagnose the presence or absence of LSCS requiring surgery from plain radiographs using CNNs. STUDY DESIGN: Retrospective analysis of consecutive, nonrandomized series of patients at a single institution. PATIENT SAMPLE: Data of 150 patients who underwent surgery for LSCS, including degenerative spondylolisthesis, at a single institution from January 2022 to August 2022, were collected. Additionally, 25 patients who underwent surgery at 2 other hospitals were included for extra external validation. OUTCOME MEASURES: In annotation 1, the area under the curve (AUC) computed from the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated. In annotation 2, correlation coefficients were used. METHODS: Four intervertebral levels from L1/2 to L4/5 were extracted as region of interest from lateral plain lumbar spine radiographs totaling 600 images were obtained. Based on the date of surgery, 500 images derived from the first 125 cases were used for internal validation, and 100 images from the subsequent 25 cases used for external validation. Additionally, 100 images from other hospitals were used for extra external validation. In annotation 1, binary classification of operative and nonoperative levels was used, and in annotation 2, the spinal canal area measured on axial MRI was labeled as the output layer. For internal validation, the 500 images were divided into each 5 dataset on per-patient basis and 5-fold cross-validation was performed. Five trained models were registered in the external validation prediction performance. Grad-CAM was used to visualize area with the high features extracted by CNNs. RESULTS: In internal validation, the AUC and accuracy for annotation 1 ranged between 0.85-0.89 and 79-83%, respectively, and the correlation coefficients for annotation 2 ranged between 0.53 and 0.64 (all p<.01). In external validation, the AUC and accuracy for annotation 1 were 0.90 and 82%, respectively, and the correlation coefficient for annotation 2 was 0.69, using 5 trained CNN models. In the extra external validation, the AUC and accuracy for annotation 1 were 0.89 and 84%, respectively, and the correlation coefficient for annotation 2 was 0.56. Grad-CAM showed high feature density in the intervertebral joints and posterior intervertebral discs. CONCLUSIONS: This technology automatically detects LSCS from plain lumbar spine radiographs, making it possible for medical facilities without MRI or nonspecialists to diagnose LSCS, suggesting the possibility of eliminating delays in the diagnosis and treatment of LSCS that require early treatment.

A decision tree analysis to predict clinical outcome of minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis.

BACKGROUND CONTEXT: Implementing machine learning techniques, such as decision trees, known as prediction models that use logical construction diagrams, are rarely used to predict clinical outcomes. PURPOSE: To develop a clinical prediction rule to predict clinical outcomes in patients who undergo minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis using a decision tree model. STUDY DESIGN/SETTING: A retrospective analysis of prospectively collected data. PATIENT SAMPLE: This study included 331 patients who underwent minimally invasive surgery for lumbar spinal stenosis and were followed up for ≥2 years at 1 institution. OUTCOME MEASURES: Self-report measures: The Japanese Orthopedic Association (JOA) scores and low back pain (LBP)/leg pain/leg numbness visual analog scale (VAS) scores. Physiologic measures: Standing sagittal spinopelvic alignment, computed tomography, and magnetic resonance imaging results. METHODS: Low achievement in clinical outcomes were defined as the postoperative JOA score at the 2-year follow-up <25 points. Univariate and multiple logistic regression analysis and chi-square automatic interaction detection (CHAID) were used for analysis. RESULTS: The CHAID model for JOA score <25 points showed spontaneous numbness/pain as the first decision node. For the presence of spontaneous numbness/pain, sagittal vertical axis ≥70 mm was selected as the second decision node. Then lateral wedging, ≥6° and pelvic incidence minus lumbar lordosis (PI-LL) ≥30° followed as the third decision node. For the absence of spontaneous numbness/pain, sex and lateral olisthesis, ≥3mm and American Society of Anesthesiologists physical status classification system score were selected as the second and third decision nodes. The sensitivity, specificity, and the positive predictive value of this CHAID model was 65.1, 69.8, and 64.7% respectively. CONCLUSIONS: The CHAID model incorporating basic information and functional and radiologic factors is useful for predicting surgical outcomes.

Deep Learning Algorithm for Identifying Cervical Cord Compression Due to Degenerative Canal Stenosis on Radiography.

STUDY DESIGN: Cross-sectional study. OBJECTIVE: Validate the diagnostic accuracy of a deep-learning algorithm for cervical cord compression due to degenerative canal stenosis on radiography. SUMMARY OF BACKGROUND DATA: The diagnosis of degenerative cervical myelopathy is often delayed, resulting in improper management. Screening tools for suspected degenerative cervical myelopathy would help identify patients who require detailed physical evaluation. MATERIALS AND METHODS: Data from 240 patients (120 with cervical stenosis on magnetic resonance imaging and 120 age and sex-matched controls) were randomly divided into training (n = 198) and test (n = 42) data sets. The deep-learning algorithm, designed to identify the suspected stenosis level on radiography, was constructed using a convolutional neural network model called EfficientNetB2, and radiography and magnetic resonance imaging data from the training data set. The accuracy and area under the curve of the receiver operating characteristic curve were calculated for the independent test data set. Finally, the number of correct diagnoses was compared between the algorithm and 10 physicians using the test cohort. RESULTS: The diagnostic accuracy and area under the curve of the deep-learning algorithm were 0.81 and 0.81, respectively, in the independent test data set. The rate of correct responses in the test data set was significantly higher for the algorithm than for the physician's consensus (81.0% vs . 66.2%; P = 0.034). Furthermore, the accuracy of the algorithm was greater than that of each individual physician. CONCLUSIONS: We developed a deep-learning algorithm capable of suggesting the presence of cervical spinal cord compression on cervical radiography and highlighting the suspected levels on radiographic imaging when cord compression is identified. The diagnostic accuracy of the algorithm was greater than that of spine physicians. LEVEL OF EVIDENCE: Level IV.

A deep learning algorithm to identify cervical ossification of posterior longitudinal ligaments on radiography.

The cervical ossification of the posterior longitudinal ligament (cOPLL) is sometimes misdiagnosed or overlooked on radiography. Thus, this study aimed to validate the diagnostic yield of our deep learning algorithm which diagnose the presence/absence of cOPLL on cervical radiography and highlighted areas of ossification in positive cases and compare its diagnostic accuracy with that of experienced spine physicians. Firstly, the radiographic data of 486 patients (243 patients with cOPLL and 243 age and sex matched controls) who received cervical radiography and a computer tomography were used to create the deep learning algorithm. The diagnostic accuracy of our algorithm was 0.88 (area under curve, 0.94). Secondly, the numbers of correct diagnoses were compared between the algorithm and consensus of four spine physicians using 50 independent samples. The algorithm had significantly more correct diagnoses than spine physicians (47/50 versus 39/50, respectively; p = 0.041). In conclusion, the accuracy of our deep learning algorithm for cOPLL diagnosis was significantly higher than that of experienced spine physicians. We believe our algorithm, which uses different diagnostic criteria than humans, can significantly improve the diagnostic accuracy of cOPLL when radiography is used.

Improvements in Mental Well-Being and its Predictive Factors in Patients who Underwent Cervical versus Lumbar Decompression Surgery.

INTRODUCTION: Mental well-being is essential for patient satisfaction. Therefore, a better understanding of the changes in the mental well-being of patients following spinal surgery can be useful to surgeons. We compared the 2-year postoperative change in the mental well-being of patients who underwent cervical and lumbar decompression surgery. Additionally, the predictive factors for improvement in mental well-being associated with both methods were evaluated. METHODS: The patients who underwent spinal decompression surgery and were followed >2 years postoperatively were enrolled (lumbar cohort: n=111, cervical cohort: n=121). The 36-item Short-Form Health Survey (SF-36) mental component summary (MCS) was set as the mental well-being parameter, and the minimal clinically important difference (MCID) was defined as 4.0. After adjusting the cervical and lumbar cohorts using propensity scores, the improvements in the MCS were compared between the groups using a mixed-effect model. To identify predictors for improvements, the correlation between the MCS changes and preoperative clinical scores was evaluated. Subsequently, multivariate linear regression was applied, which included variables with p<0.10 in the former analysis as explanatory variables, and the change of MCS as the objective variable. RESULTS: There were no significant differences in the MCS improvement between the adjusted cervical and lumbar cohorts; 47% and 49%, respectively, had MCS improvement score >MCIDs. However, predictors for the improvement were different between the two cohorts: SF-36 Social functioning in cervical surgery and lower back pain and SF-36 Role physical in lumbar surgery. CONCLUSIONS: Although there was no significant difference in the improvement in the mental well-being between patients who underwent either cervical or lumbar decompression surgery, less than half of the patients in both groups achieved a meaningful improvement. Preoperative back pain and personal activity were independent predictors in the lumbar cohort, while social functioning was the only predictor in the cervical cohort. Level of evidence: III.

Impact of the COVID-19 Pandemic on Elderly Patients with Spinal Disorders.

During the ongoing coronavirus disease 2019 (COVID-19) pandemic, home-quarantine has been necessary, resulting in lifestyle changes that might negatively affect patients with spinal disorders, including a reduction in their quality of life (QoL) and activities of daily living (ADLs). However, studies on this impact are lacking. This study aimed to investigate the age-related changes in QoL and ADLs in patients with spinal disorders, and also identify factors associated with decline in ADLs. This multicenter cross-sectional study included patients who visited four private spine clinics for any symptoms. The study participants either had a clinic reservation, were first-time clinic visitors, or had a return visit to the clinic. The participants completed several questionnaires at two points: pre-pandemic and post-second wave. Changes in patient symptoms, exercise habits, ADLs, and health-related QoL were assessed. A logistic regression model was used to calculate the odds ratio (OR) of each variable for decline in ADLs. We included 606 patients; among them, 281 and 325 patients were aged <65 and ≥65 years, respectively. Regarding exercise habits, 46% and 48% of the patients in the <65 and ≥65-year age groups, respectively, did not change their exercise habits. In contrast, 40% and 32% of the patients in the <65 and ≥65-year age groups, respectively, decreased their exercise habits. In the multivariate analysis, the adjusted ORs for sex (female), decreased exercise habit, and age >65 years were 1.7 (1.1-2.9), 2.4 (1.4-3.9), and 2.7 (1.6-4.4), respectively. In conclusion, there was a decline in the ADLs and QoL after the COVID-19 outbreak in patients with spinal disorders. Aging, reduction of exercise habits, and female sex were independent factors related to decline in ADLs.