Lower SMI is a risk factor for dysphagia in Japanese hospitalized patients with osteoporotic vertebral and hip fracture: A retrospective study

Suseki, Kaoru; Yamashita, Masaomi; Kojima, Yoshiaki; Minegishi, Yojiro; Komiya, Koichiro; Takaso, Masashi

doi:10.1016/j.afos.2022.11.001

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…In contrast to clearly identifiable traumatic vertebral fractures, osteoporotic vertebral fractures (OVFs) are typically more subtle and often remain undetected. Inadequate treatment of OVFs can compromise spinal stability and balance, potentially resulting in severe neurological damage and an elevated risk of mortality ( 30 ). Accurate classification of OVFs is crucial not only for precise diagnosis but also for guiding appropriate clinical interventions.…”

Section: Discussionmentioning

confidence: 99%

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

Zhang,

Xia,

Liu

et al. 2024

Front. Endocrinol.

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PurposeTo develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs).Material and methodsThe study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the “One-vs-Rest” strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model.ResultsFollowing pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary “One-vs-Rest” strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively.ConclusionThe DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

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Section: Discussionmentioning

confidence: 99%

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

Zhang,

Xia,

Liu

et al. 2024

Front. Endocrinol.

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Negative impact of computed tomography-based low skeletal muscle mass on swallowing recovery in patients with post-stroke dysphagia

Moriyama

Tokunaga

Ochi

et al. 2023

Clinical Neurology and Neurosurgery

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Effect of osteosarcopenia on feeding status in hospitalized patients with suspected dysphagia

Miyagi,

Sekiya,

Ebihara

2024

PLoS ONE

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Objectives Osteosarcopenia is a combination of sarcopenia and osteoporosis that increases mortality rates among older people compared with either alone. This study aimed to identify the contribution of osteosarcopenia to the development and severity of dysphagia. Methods We retrospectively reviewed the medical charts of 211 patients aged ≥ 65 years who were referred to the dysphagia rehabilitation team. Based on Functional Oral Intake Scale (FOIS) scores, we classified the patients with (FOIS scores 1–5) and without (FOIS scores 6, 7) dysphagia as Type A and those with (FOIS scores 1, 2) and without (FOIS score 3–7) enteral feeding as Type B. Based on chest computed tomography (CT) findings we then defined patients with T4 (MI) and pectoralis (PMI) muscle indexes, L1 attenuation, and T4MI, PMI, and L1 attenuation below the cutoff values as having sarcopenia, osteoporosis, and osteosarcopenia, respectively. Results The FOIS scores were significantly lower among patients with osteosarcopenia than among those without sarcopenia or osteoporosis. Moreover, PMI and FOIS scores significantly and positively correlated, and PMI was significantly lower in the group with, than without, enteral feeding. Osteoporosis and osteosarcopenia were significant in the patients who were fed enterally (p = 0.032 and 0.047, respectively). Conclusions Patients with sarcopenia and osteoporosis undergoing swallowing rehabilitation tended to have severe dysphagia that required much medical attention.

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Lower SMI is a risk factor for dysphagia in Japanese hospitalized patients with osteoporotic vertebral and hip fracture: A retrospective study

Cited by 3 publications

References 31 publications

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images

Negative impact of computed tomography-based low skeletal muscle mass on swallowing recovery in patients with post-stroke dysphagia

Effect of osteosarcopenia on feeding status in hospitalized patients with suspected dysphagia

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