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Vertebral compression model and comparison of augmentation agents.

ABSTRACT: STUDY DESIGN: Biomechanical study. OBJECTIVES: To evaluate the compression strengths of various bone fillers used in treating vertebral compression fractures using a third-generation sawbone model and to evaluate the viability of this novel model as an alternative to actual human or animal vertebrae for biomechanical testing of vertebral-filling materials. METHODS: Cavities were created in the osteoporotic vertebral body sawbone models and filled with PMMA, SRS, MIIGX3 HiVisc, and BoneSource fillers. These were cured according to manufacturers' recommendations and then tested to failure in the compression model. Elastic modulus was calculated and compared with the control group which was not augmented. RESULTS: The mean modulus of elasticity for the control group vertebrae was 92.44 ± 19.28 MPa. The mean modulus of elasticity was highest in the polymethylmethacrylate (PMMA) group (195.47 ± 2.33 MPa) and lowest in the MIIG group (25.79 ± 4.77 MPa). The results for the SRS-tricalcium phosphate group (79.14 ± 20.20 MPa) were closest to the control group, followed by the BoneSource group (57.49 ± 8.35 MPa). Statistical analysis, for comparison of individual group means, identified significant differences between the control group and all other groups (P < .05), with the exception of the SRS-tricalcium phosphate group (P = .65, versus control). The modulus of elasticity for the PMMA group was significantly higher than all other groups (P < .001). CONCLUSION: The third-generation osteoporotic sawbones model simulates in vitro physiological specimen function. It was effective for comparing which osteoconductive agents may provide adequate strength while minimizing potential adjacent level fracture. Increased stiffness was seen with PMMA compared with the unaugmented control as well as with calcium phosphate or calcium sulfate cements suggesting that these may reduce adjacent segment fractures.

SUBMITTER: Hill C

PROVIDER: S-EPMC3427969 | biostudies-other | 2011 Feb

REPOSITORIES: biostudies-other

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Project description:BackgroundMost patients with osteoporotic vertebral compression fracture (OVCF) obtain pain relief after vertebral augmentation, but some will experience residual back pain (RBP) after surgery. Although several risk factors of RBP have been reported, it is still difficult to estimate the risk of RBP preoperatively. Radiomics is helpful for disease diagnosis and outcome prediction by establishing complementary relationships between human-recognizable and computer-extracted features. However, musculoskeletal radiomics investigations are less frequently reported.ObjectiveThis study aims to establish a radiomics score (rad-score) based nomogram for the preoperative prediction of RBP in OVCF patients.MethodsThe training cohort of 731 OVCF patients was used for nomogram development, and the validation cohort was utilized for performance test. RBP was determined as the score of visual analogue scale ≥ 4 at both 3 and 30 days following surgery. After normalization, the RBP-related radiomics features were selected to create rad-scores. These rad-scores, along with the RBP predictors initially identified by univariate analyses, were included in the multivariate analysis to establish a nomogram for the assessment of the RBP risk in OVCF patients preoperatively.ResultsA total of 81 patients (11.2%) developed RBP postoperatively. We finally selected 8 radiomics features from 1316 features extracted from each segmented image to determine the rad-score. Multivariate analysis revealed that the rad-score plus bone mineral density, intravertebral cleft, and thoracolumbar fascia injury were independent factors of RBP. Our nomograms based on these factors demonstrated good discrimination, calibration, and clinical utility in both training and validation cohorts. Furthermore, it achieved better performance than the rad-score itself, as well as the nomogram only incorporating regular features.ConclusionWe developed and validated a nomogram incorporating the rad-score and regular features for preoperative prediction of the RBP risk in OVCF patients, which contributed to improved surgical outcomes and patient satisfaction.

Project description:BackgroundAccurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation.MethodsA total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models.ResultsThe two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model.ConclusionsThe integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.

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Vertebral compression model and comparison of augmentation agents.

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