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Concomitant and previous osteoporotic vertebral fractures.

ABSTRACT: Background and purpose - Patients with osteoporosis who present with an acute onset of back pain often have multiple fractures on plain radiographs. Differentiation of an acute osteoporotic vertebral fracture (AOVF) from previous fractures is difficult. The aim of this study was to investigate the incidence of concomitant AOVFs and previous OVFs in patients with symptomatic AOVFs, and to identify risk factors for concomitant AOVFs. Patients and methods - This was a prospective epidemiological study based on the Registry of Pathological Osteoporotic Vertebral Fractures (REPAPORA) with 1,005 patients and 2,874 osteoporotic vertebral fractures, which has been running since February 1, 2006. Concomitant fractures are defined as at least 2 acute short-tau inversion recovery (STIR-) positive vertebral fractures that happen concomitantly. A previous fracture is a STIR-negative fracture at the time of initial diagnostics. Logistic regression was used to examine the influence of various variables on the incidence of concomitant fractures. Results - More than 99% of osteoporotic vertebral fractures occurred in the thoracic and lumbar spine. The incidence of concomitant fractures at the time of first patient contact was 26% and that of previous fractures was 60%. The odds ratio (OR) for concomitant fractures decreased with a higher number of previous fractures (OR =0.86; p = 0.03) and higher dual-energy X-ray absorptiometry T-score (OR =0.72; p = 0.003). Interpretation - Concomitant and previous osteoporotic vertebral fractures are common. Risk factors for concomitant fractures are a low T-score and a low number of previous vertebral fractures in cases of osteoporotic vertebral fracture. An MRI scan of the the complete thoracic and lumbar spine with STIR sequence reduces the risk of under-diagnosis and under-treatment.

SUBMITTER: Lenski M

PROVIDER: S-EPMC5385115 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Concomitant and previous osteoporotic vertebral fractures.

Lenski Markus M Büser Natalie N Scherer Michael M

Acta orthopaedica 20170106 2

Background and purpose - Patients with osteoporosis who present with an acute onset of back pain often have multiple fractures on plain radiographs. Differentiation of an acute osteoporotic vertebral fracture (AOVF) from previous fractures is difficult. The aim of this study was to investigate the incidence of concomitant AOVFs and previous OVFs in patients with symptomatic AOVFs, and to identify risk factors for concomitant AOVFs. Patients and methods - This was a prospective epidemiological st ...[more]

PMID: 28056595

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Project description: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.

Project description:BACKGROUND:One factor related to disability in people with spinal deformity is decreased postural control and increased risk of falling. However, little is known about the effect of osteoporotic vertebral compression fractures (OVCFs) and their recovery on gait and stability. Walking characteristics of older adults with and without vertebral fractures have not yet been compared. AIMS:The purpose of the current study was to examine the spatiotemporal gait parameters and their variability in patients with an OVCF and healthy participants during treadmill walking at baseline and after 6 months of recovery. METHODS:Twelve female patients suffering a symptomatic OVCF were compared to 11 matched controls. Gait analysis was performed with a dual-belt instrumented treadmill with a 180° projection screen providing a virtual environment (computer-assisted rehabilitation environment). Results of patients with an OVCF and healthy participants were compared. Furthermore, spatiotemporal gait parameters were assessed over 6 months following the fracture. RESULTS:Patients suffering from an OVCF appeared to walk with significantly shorter, faster and wider strides compared to their healthy counterparts. Although stride time and length improved over time, the majority of the parameters analysed remained unchanged after 6 months of conservative treatment. DISCUSSION:Since patients do not fully recover to their previous level of mobility after 6 months of conservative treatment for OVCF, it appears of high clinical importance to add balance and gait training to the treatment algorithm of OVCFs. CONCLUSIONS:Patients suffering from an OVCF walk with shorter, faster and wider strides compared to their healthy counterparts adopt a less stable body configuration in the anterior direction, potentially increasing their risk of forward falls if perturbed. Although stride time and stride length improve over time even reaching healthy levels again, patients significantly deviate from normal gait patterns (e.g. in stability and step width) after 6 months of conservative treatment.

Project description:Rationale: Poly (methyl methacrylate) (PMMA) bone cement is one of the most commonly used biomaterials for augmenting/stabilizing osteoporosis-induced vertebral compression fractures (OVCFs), such as percutaneous vertebroplasty (PVP) and balloon kyphoplasty (BKP). However, its clinical applications are limited by its poor performance in high compressive modulus and weak bonding to bone. To address these issues, a bioactive composite bone cement was developed for the treatment of osteoporotic vertebral compression fractures, in which mineralized collagen (MC) was incorporated into the PMMA bone cement (MC-PMMA). Methods: The in vitro properties of PMMA and MC-PMMA composite bone cement were determined, including setting time, compressive modulus, adherence, proliferation, and osteogenic differentiation of rat bone mesenchymal stem cells. The in vivo properties of both cements were evaluated in an animal study (36 osteoporotic New Zealand female rabbits divided equally between the two bone cement groups; PVP at L5) and a small-scale and short-term clinical study (12 patients in each of the two bone cement groups; follow-up: 2 years). Results: In terms of value for PMMA bone cement, the handling properties of MC-PMMA bone cement were not significantly different. However, both compressive strength and compressive modulus were found to be significantly lower. In the rabbit model study, at 8 and 12 weeks post-surgery, bone regeneration was more significant in MC-PMMA bone cement (cortical bone thickness, osteoblast area, new bone area, and bone ingrowth %; each significantly higher). In the clinical study, at a follow-up of 2 years, both the Visual Analogue Score and Oswestry Disability Index were significantly reduced when MC-PMMA cement was used. Conclusions: MC-PMMA bone cement demonstrated good adaptive mechanical properties and biocompatibility and may be a promising alternative to commercial PMMA bone cements for the treatment of osteoporotic vertebral fractures in clinical settings. While the present results for MC-PMMA bone cement are encouraging, further study of this cement is needed to explore its viability as an ideal alternative for use in PVP and BKP.

Dataset Information

Concomitant and previous osteoporotic vertebral fractures.

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Concomitant and previous osteoporotic vertebral fractures.

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