Factors associated with pain-related disorders and gait disturbance scores from the Japanese orthopedic association back pain evaluation questionnaire and Oswestry Disability Index in patients with osteoporosis

Miyagi, Masayuki; Inoue, Gen; Murata, Kei; Koyama, Tomohisa; Kuroda, Akiyoshi; Kawakubo, Ayumu; Yokozeki, Yuji; Mimura, Yusuke; Nanri, Yuta; Inage, Kazuhide; Akazawa, Tsutomu; Ohtori, Seiji; Uchida, Katsuhisa; Takaso, Masashi

doi:10.1007/s11657-021-01045-x

Cited by 11 publications

(6 citation statements)

References 32 publications

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“…A previous report indicated the trunk muscle mass was significantly related with spinal alignment, ODI scores, and EQ5D scores when adjusted for appendicular skeletal muscle mass 29 . Furthermore, a decreased trunk muscle mass might be a possible risk factor for LBP in osteoporosis patients 30 . Thus, trunk muscles might be an important factor for maintaining posture and improving clinical findings and should be considered when treating patients with spinal diseases.…”

Section: Discussionmentioning

confidence: 99%

Muscle strength rather than appendicular skeletal muscle mass might affect spinal sagittal alignment, low back pain, and health-related quality of life

Tanaka

Miyagi

Inoue

et al. 2023

Sci Rep

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Sarcopenia is defined as decreasing in muscle strength and mass, and dynapenia is defined as decreasing in muscle strength and maintained muscle mass. This study elucidated the prevalence and characteristics of sarcopenia and dynapenia and evaluate in elderly spinal disorders patients. 1039 spinal disorders patients aged ≥ 65 years were included. We measured age, grip strength, muscle mass, spinal sagittal alignment parameters, low back pain (LBP) scores and health-related quality of life (HR-QoL) scores. Based on the previous reports, patients were categorised into normal group: NG, pre-sarcopenia group: PG, dynapenia group: DG, and sarcopenia group: SG. Pre-sarcopenia, dynapenia, and sarcopenia were found in 101 (9.7%), 249 (19.2%), and 91 (8.8%) patients, respectively. The spinal sagittal alignment parameters, trunk muscle mass, LBP, and HR-QoL scores were significantly worse in DG and SG compared with those in PG and NG. Spinal alignment, trunk muscle mass, and clinical outcomes, including LBP and HR-QoL scores, were maintained in the PG and poor in the DG and SG. Thus, intervention for muscle strength may be a treatment option for changes of spinal sagittal alignment and low back pain.

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Section: Discussionmentioning

confidence: 99%

Muscle strength rather than appendicular skeletal muscle mass might affect spinal sagittal alignment, low back pain, and health-related quality of life

Tanaka

Miyagi

Inoue

et al. 2023

Sci Rep

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“…In this study, the proportion of patients with osteoporosis in group B was significantly higher than that in group A, and the difference was statistically significant, indicating that osteoporosis was also an important factor affecting the improvement rate of the lumbar spine JOA score after TMD. The study by Miyagi et al ( 29 ) showed that osteoporosis had a strong correlation with the lumbar spine JOA score, and high bone turnover in patients with osteoporosis was a risk factor for gait disturbance in JOA. Although the mechanism is unclear, osteoporosis treatment may improve the patient's gait ability.…”

Section: Discussionmentioning

confidence: 99%

Development, validation, and visualization of a web-based nomogram to predict the effect of tubular microdiscectomy for lumbar disc herniation

Chen

Lin

et al. 2023

Front. Surg.

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ObjectiveThe purpose of this study was to retrospectively collect the relevant clinical data of lumbar disc herniation (LDH) patients treated with the tubular microdiscectomy (TMD) technique, and to develop and validate a prediction model for predicting the treatment improvement rate of TMD in LDH patients at 1 year after surgery.MethodsRelevant clinical data of LDH patients treated with the TMD technology were retrospectively collected. The follow-up period was 1 year after surgery. A total of 43 possible predictors were included, and the treatment improvement rate of the Japanese Orthopedic Association (JOA) score of the lumbar spine at 1 year after TMD was used as an outcome measure. The least absolute shrinkage and selection operator (LASSO) method was used to screen out the most important predictors affecting the outcome indicators. In addition, logistic regression was used to construct the model, and a nomogram of the prediction model was drawn.ResultsA total of 273 patients with LDH were included in this study. Age, occupational factors, osteoporosis, Pfirrmann classification of intervertebral disc degeneration, and preoperative Oswestry Disability Index (ODI) were screened out from the 43 possible predictors based on LASSO regression. A total of 5 predictors were included while drawing a nomogram of the model. The area under the ROC curve (AUC) value of the model was 0.795.ConclusionsIn this study, we successfully developed a good clinical prediction model that can predict the effect of TMD for LDH. A web calculator was designed on the basis of the model (https://fabinlin.shinyapps.io/DynNomapp/).

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“…In this study, by reviewing similar articles and consulting with a specialist physician, and distributing and collecting relevant questionnaires from the subjects, the most significant clinical factors of osteoporosis were prepared. In addition, in a review of relevant books and articles, clinical factors related to osteoporosis were extracted 24 . Finally, 19 input features for women and 17 input features for men (excluding menopause and menopausal age) were selected as the input of the algorithms.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols

Fasihi

Tartibian

Eslami

et al. 2022

Sci Rep

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Osteoporosis (OP) is characterized by diminished bone mass and deteriorating bone structure that increases the chance of fractures in the spine, hips, and wrists. In this paper, a novel data processing method of artificial intelligence (AI) is used for evaluating, predicting, and classifying OP risk factors in clinical data of men and women separately. Additionally, artificial intelligence was used to suggest the most appropriate sports programs for treatment. Data was obtained from dual-energy x-ray absorption scanning center of Ayatollah Kashani, Milad, and Khatam al-Anbia hospitals in Tehran, Iran. The subjects included 1224 men and women. Models were developed using decision tree, random forest (RF), k-nearest neighbor, support vector machine, gradient boosting (GB), Extra trees, Ada Boost (AB), and artificial neural network multilayer perceptron analysis to predict osteoporosis and to recommend sports programs. Data was divided into training (80%) and test dataset (20%). The results were obtained on a 20% test dataset. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. To predict healthy individuals, osteopenia and osteoporosis, the FR algorithm with AUROC 0.91 performed best in men and the GB algorithm with AUROC 0.95 performed best in women compared to other classification algorithms. Prediction of RF algorithm in women and men with AUROC 0.96 and 0.99, respectively, showed the highest performance in diagnosing the type of exercise for healthy individuals and those with osteopenia and OP. Eight AI algorithms were developed and compared to accurately predict osteoporosis risk factors and classify individuals into three categories: healthy, osteopenia, and OP. In addition, the AI algorithms were developed to recommend the most appropriate sports programs as part of treatment. Applying the AI algorithms in a clinical setting could help primary care providers classify patients with osteoporosis and improve treatment by recommending appropriate exercise programs.

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Factors associated with pain-related disorders and gait disturbance scores from the Japanese orthopedic association back pain evaluation questionnaire and Oswestry Disability Index in patients with osteoporosis

Cited by 11 publications

References 32 publications

Muscle strength rather than appendicular skeletal muscle mass might affect spinal sagittal alignment, low back pain, and health-related quality of life

Muscle strength rather than appendicular skeletal muscle mass might affect spinal sagittal alignment, low back pain, and health-related quality of life

Development, validation, and visualization of a web-based nomogram to predict the effect of tubular microdiscectomy for lumbar disc herniation

Artificial intelligence used to diagnose osteoporosis from risk factors in clinical data and proposing sports protocols

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