Simplified S1 Vertebral Bone Quality Score in the Assessment of Patients with Vertebral Fragility Fractures.

OBJECTIVE: To evaluate the effectiveness of the S1 vertebral bone quality (VBQ) score in assessing bone quality among patients with vertebral fragility fractures (VFF). Additionally, whether the combination of S1 VBQ and Hounsfield unit (HU) values improves the predictive accuracy of VFF. METHODS: Using lumbar noncontrast computed tomography and T1-weighted magnetic resonance imaging, we measured L1 HU values, S1 VBQ, and L1-L4 VBQ. To assess their predictive performance for VFF, we constructed receiver operating characteristic curves. We also compared the diagnostic efficacy of HU values with that of S1 VBQ and L1--L4 VBQ values for the joint diagnosis of VFF. The Delong test was used to compare the value of individual or combined predictions of VFF. RESULTS: In comparison to the nonfracture group, all patients exhibited markedly elevated S1 VBQ and L1--L4 VBQ and notably reduced HU values (P < 0.001). Multivariate analysis revealed that elevated S1 VBQ, increased L1--L4 VBQ, and decreased HU values independently correlated with VFF development. The areas under the curve for VFF prediction were 0.806 for S1 VBQ, 0.799 for L1--L4 VBQ, and 0.820 for HU values. According to the Delong test, the combination of HU values with S1 VBQ/L1--L4 VBQ significantly improved the diagnostic accuracy. CONCLUSIONS: The simplified S1 VBQ is a valuable tool for predicting the occurrence of VFF and can be used as an alternative to the L1--L4 VBQ. In addition, the combination of S1 VBQ and HU values can significantly improve the predictive value of VFF.

Vertebral fracture severity assessment on anteroposterior radiographs with a new semi-quantitative technique.

We developed a new tool to assess the severity of osteoporotic vertebral fracture using radiographs of the spine. Our technique can be used in patient care by helping to stratify patients with osteoporotic vertebral fractures into appropriate treatment pathways. It can also be used for research purposes. PURPOSE: The aim of our study was to propose a semi-quantitative (SQ) grading scheme for osteoporotic vertebral fracture (OVF) on anteroposterior (AP) radiographs. METHODS: On AP radiographs, the vertebrae are divided into right and left halves, which are graded (A) vertical rectangle, (B) square, (C) traverse rectangle, and (D) trapezoid; whole vertebrae are graded (E) transverse band or (F) bow-tie. Type A and B were compared with normal and Genant SQ grade 1 OVF, Type C and D with grade 2 OVF, and Type E and F with grade 3 OVF. Spine AP radiographs and lateral radiographs of 50 females were assessed by AP radiographs SQ grading. After training, an experienced board-certified radiologist and a radiology trainee assessed the 50 AP radiographs. RESULTS: The height-to-width ratio of the half vertebrae varied 1.32-1.48. On lateral radiographs, 84 vertebrae of the 50 patients had OVFs (38 grade 1, 24 grade 2, and 22 grade 3). On AP radiographs, the radiologist correctly assigned 84.2%, 91.7%, and 77.2% and the trainee correctly assigned 68.4%, 79.2%, and 81.8% of grade 1, 2, and 3 OVFs, respectively. Compared with lateral radiographs, the radiologist had a weighted Kappa of 0.944 including normal vertebrae and 0.883 not including normal vertebrae, while the corresponding Kappa values for the trainee were 0.891 and 0.830, respectively. CONCLUSION: We propose a new semi-quantitative grading system for vertebral fracture severity assessment on AP spine radiographs.

Adjusting FRAX Estimates of Fracture Probability Based on a Positive Vertebral Fracture Assessment.

Importance: FRAX is the most widely used and validated fracture risk prediction tool worldwide. Vertebral fractures, which are an indicator of subsequent osteoporotic fractures, can be identified using dual-energy x-ray absorptiometry (DXA) vertebral fracture assessment (VFA). Objective: To assess the calibration of FRAX and develop a simple method for improving FRAX-predicted fracture probability in the presence of VFA-identified fracture. Design, Setting, and Participants: This prognostic study analyzed the DXA and VFA results of all individuals who underwent a VFA between March 31, 2010, and March 31, 2018, who were included in the Manitoba Bone Mineral Density Registry. These individuals were randomly assigned to either the development cohort or validation cohort. A modified algorithm-based qualitative approach was used by expert readers to code VFAs as positive (≥1 vertebral fractures detected) or negative (0 vertebral fracture detected). Statistical analysis was conducted from August 7, 2022, to May 22, 2023. Exposures: FRAX scores for major osteoporotic fracture (MOF) and hip fracture were calculated with or without VFA results. Main Outcomes and Measures: Incident fractures and death were ascertained using linked population-based health care provincial data. Cumulative incidence curves for MOF and hip fracture were constructed, including competing mortality, to predict the 10-year observed risk of fracture. The observed probability was compared with FRAX-predicted fracture probability with and without VFA results and recalibrated FRAX from derived multipliers. Results: The full cohort of 11 766 individuals was randomly allocated to the development cohort (n = 7854; 7349 females [93.6%]; mean [SD] age, 75.7 [6.8] years) or the validation cohort (n = 3912; 3713 females [94.9%]; mean [SD] age, 75.5 [6.9] years). Over a mean (SD) observation time of 3.8 (2.3) years, with the longest observation at 7.5 years, FRAX was well calibrated in subgroups with negative VFA results. For individuals without a prior clinical fracture but with a positive VFA result, the 10-year FRAX-predicted MOF probability was 16.3% (95% CI, 15.7%-16.8%) without VFA information and 23.4% (95% CI, 22.7%-24.1%) with VFA information. The observed 10-year probabilities were 26.9% (95% CI, 26.0%-27.8%) and 11.2% (95% CI, 10.3%-12.1%), respectively, resulting in recalibration multipliers of 1.15 (95% CI, 0.87-1.43) for MOF and 1.31 (95% CI, 0.75-1.87) for hip fracture. For individuals with a prior clinical fracture and a positive VFA result, the 10-year FRAX-predicted probabilities were 25.0% (95% CI, 24.2%-25.7%) for MOF and 9.3% (95% CI, 8.7%-10.0%) for hip fracture. The observed 10-year probabilities were 38.1% (95% CI, 37.0%-39.1%) for MOF and 16.4% (95% CI, 15.4%-17.4%) for hip fracture, resulting in a recalibration multiplier of 1.53 (95% CI, 1.10-1.96) for MOF and 1.76 (95% CI, 1.17-2.35) for hip fracture. Good calibration (>0.90) was confirmed using the derived multipliers in the validation cohort. Conclusions and Relevance: Results of this prognostic study suggest that FRAX underestimated fracture risk in patients with VFA-identified fractures. Simple multipliers could recover FRAX calibration in individuals with VFA-identified fractures.

Cost-effectiveness of opportunistic QCT-based osteoporosis screening for the prediction of incident vertebral fractures.

Objectives: Opportunistic quantitative computed tomography (oQCT) derived from non-dedicated routine CT has demonstrated high accuracy in diagnosing osteoporosis and predicting incident vertebral fractures (VFs). We aimed to investigate the cost-effectiveness of oQCT screening compared to dual-energy X-ray absorptiometry (DXA) as the standard of care for osteoporosis screening. Methods: Three screening strategies ("no osteoporosis screening", "oQCT screening", and "DXA screening") after routine CT were simulated in a state-transition model for hypothetical cohorts of 1,000 patients (women and men aged 65 years) over a follow-up period of 5 years (base case). The primary outcomes were the cumulative costs and the quality-adjusted life years (QALYs) estimated from a U.S. health care perspective for the year 2022. Cost-effectiveness was assessed based on a willingness-to-pay (WTP) threshold of $70,249 per QALY. The secondary outcome was the number of prevented VFs. Deterministic and probabilistic sensitivity analyses were conducted to test the models' robustness. Results: Compared to DXA screening, oQCT screening increased QALYs in both sexes (additional 2.40 per 1,000 women and 1.44 per 1,000 men) and resulted in total costs of $3,199,016 and $950,359 vs. $3,262,934 and $933,077 for women and men, respectively. As a secondary outcome, oQCT screening prevented 2.6 and 2.0 additional VFs per 1,000 women and men, respectively. In the probabilistic sensitivity analysis, oQCT screening remained cost-effective in 88.3% (women) and 90.0% (men) of iterations. Conclusion: oQCT screening is a cost-effective ancillary approach for osteoporosis screening and has the potential to prevent a substantial number of VFs if considered in daily clinical practice.

MRI-based radiomics assessment of the imminent new vertebral fracture after vertebral augmentation.

BACKGROUND: Imminent new vertebral fracture (NVF) is highly prevalent after vertebral augmentation (VA). An accurate assessment of the imminent risk of NVF could help to develop prompt treatment strategies. PURPOSE: To develop and validate predictive models that integrated the radiomic features and clinical risk factors based on machine learning algorithms to evaluate the imminent risk of NVF. MATERIALS AND METHODS: In this retrospective study, a total of 168 patients with painful osteoporotic vertebral compression fractures treated with VA were evaluated. Radiomic features of L1 vertebrae based on lumbar T2-weighted images were obtained. Univariate and LASSO-regression analyses were applied to select the optimal features and construct radiomic signature. The radiomic signature and clinical signature were integrated to develop a predictive model by using machine learning algorithms including LR, RF, SVM, and XGBoost. Receiver operating characteristic curve and calibration curve analyses were used to evaluate the predictive performance of the models. RESULTS: The radiomic-XGBoost model with the highest AUC of 0.93 of the training cohort and 0.9 of the test cohort among the machine learning algorithms. The combined-XGBoost model with the best performance with an AUC of 0.9 in the training cohort and 0.9 in the test cohort. The radiomic-XGBoost model and combined-XGBoost model achieved better performance to assess the imminent risk of NVF than that of the clinical risk factors alone (p < 0.05). CONCLUSION: Radiomic and machine learning modeling based on T2W images of preoperative lumbar MRI had an excellent ability to evaluate the imminent risk of NVF after VA.

Machine learning for abdominal aortic calcification assessment from bone density machine-derived lateral spine images.

BACKGROUND: Lateral spine images for vertebral fracture assessment can be easily obtained on modern bone density machines. Abdominal aortic calcification (AAC) can be scored on these images by trained imaging specialists to assess cardiovascular disease risk. However, this process is laborious and requires careful training. METHODS: Training and testing of model performance of the convolutional neural network (CNN) algorithm for automated AAC-24 scoring utilised 5012 lateral spine images (2 manufacturers, 4 models of bone density machines), with trained imaging specialist AAC scores. Validation occurred in a registry-based cohort study of 8565 older men and women with images captured as part of routine clinical practice for fracture risk assessment. Cox proportional hazards models were used to estimate the association between machine-learning AAC (ML-AAC-24) scores with future incident Major Adverse Cardiovascular Events (MACE) that including death, hospitalised acute myocardial infarction or ischemic cerebrovascular disease ascertained from linked healthcare data. FINDINGS: The average intraclass correlation coefficient between imaging specialist and ML-AAC-24 scores for 5012 images was 0.84 (95% CI 0.83, 0.84) with classification accuracy of 80% for established AAC groups. During a mean follow-up 4 years in the registry-based cohort, MACE outcomes were reported in 1177 people (13.7%). With increasing ML-AAC-24 scores there was an increasing proportion of people with MACE (low 7.9%, moderate 14.5%, high 21.2%), as well as individual MACE components (all p-trend <0.001). After multivariable adjustment, moderate and high ML-AAC-24 groups remained significantly associated with MACE (HR 1.54, 95% CI 1.31-1.80 & HR 2.06, 95% CI 1.75-2.42, respectively), compared to those with low ML-AAC-24. INTERPRETATION: The ML-AAC-24 scores had substantial levels of agreement with trained imaging specialists, and was associated with a substantial gradient of risk for cardiovascular events in a real-world setting. This approach could be readily implemented into these clinical settings to improve identification of people at high CVD risk. FUNDING: The study was supported by a National Health and Medical Research Council of Australia Ideas grant and the Rady Innovation Fund, Rady Faculty of Health Sciences, University of Manitoba.

Healthcare costs associated with opportunistically identifiable vertebral fractures.

PURPOSE: Vertebral fractures (VFs) are often available on radiological imaging undertaken during daily clinical work, yet the healthcare cost burden of these opportunistically identifiable fractures has not previously been reported. In this study, we examine the direct healthcare costs of subjects with vertebral fractures available for identification on routine CT scans. METHODS: Thoracolumbar vertebral fractures were identified from 2000 routine CT scans. Subjects with VF on the scan were matched 1:2 against subjects with no VF on the scan, and similarly in a 1:3-ratio against a general population cohort. We excluded those subjects who received treatment with osteoporosis medication(s) in the year prior to baseline. Direct healthcare costs, identified from the national Danish registers, were accrued over up to 6 years of follow-up, and reported per day at risk and per year. RESULTS: In subjects undergoing a CT scan, costs were initially high, yet declined over time. Comparing subjects with prevalent vertebral fracture (n = 321) against those subjects with no vertebral fracture (n = 606), mean total healthcare costs per day at risk was numerically higher in the first three years after baseline, while healthcare costs per year were similar between the cohorts. No differences reached statistical significance. When compared to the general population cohort, costs were significantly higher in the vertebral fracture cohort. CONCLUSION: Subjects with vertebral fractures available for identification on routine CT scans incur substantially higher healthcare costs than matched subjects representing the general population, and numerically, albeit non-significantly, higher healthcare costs per day at risk in the short term, as compared to subjects with no visible VF on the CT scan.

Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist's assessment.

OBJECTIVE: To test the diagnostic performance of a deep-learning Two-Stream Compare and Contrast Network (TSCCN) model for differentiating benign and malignant vertebral compression fractures (VCFs) based on MRI. METHODS: We tested a deep-learning system in 123 benign and 86 malignant VCFs. The median sagittal T1-weighted images (T1WI), T2-weighted images with fat suppression (T2WI-FS), and a combination of both (thereafter, T1WI/T2WI-FS) were used to validate TSCCN. The receiver operator characteristic (ROC) curve was analyzed to evaluate the performance of TSCCN. The accuracy, sensitivity, and specificity of TSCCN in differentiating benign and malignant VCFs were calculated and compared with radiologists' assessments. Intraclass correlation coefficients (ICCs) were tested to find intra- and inter-observer agreement of radiologists in differentiating malignant from benign VCFs. RESULTS: The AUC of the ROC plots of TSCCN according to T1WI, T2WI-FS, and T1WI/T2WI-FS images were 99.2%, 91.7%, and 98.2%, respectively. The accuracy of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was 95.2%, 90.4%, and 96.2%, respectively, greater than that achieved by radiologists. Further, the specificity of T1W, T2WI-FS, and T1W/T2WI-FS based on TSCCN was higher at 98.4%, 94.3%, and 99.2% than that achieved by radiologists. The intra- and inter-observer agreements of radiologists were 0.79-0.85 and 0.79-0.80 for T1WI, 0.65-0.72 and 0.70-0.74 for T2WI-FS, and 0.83-0.88 and 0.83-0.84 for T1WI/T2WI-FS. CONCLUSION: The TSCCN model showed better diagnostic performance than radiologists for automatically identifying benign or malignant VCFs, and is a potentially helpful tool for future clinical application. CLINICAL RELEVANCE STATEMENT: TSCCN-assisted MRI has shown superior performance in distinguishing benign and malignant vertebral compression fractures compared to radiologists. This technology has the value to enhance diagnostic accuracy, sensitivity, and specificity. Further integration into clinical practice is required to optimize patient management. KEY POINTS: • The Two-Stream Compare and Contrast Network (TSCCN) model showed better diagnostic performance than radiologists for identifying benign vs malignant vertebral compression fractures. • The processing of TSCCN is fast and stable, better than the subjective evaluation by radiologists in diagnosing vertebral compression fractures. • The TSCCN model provides options for developing a fully automated, streamlined artificial intelligence diagnostic tool.

Routine Use of Lateral Vertebral Assessment With DXA Scan for Detection of Silent But Debilitating Vertebral Fractures.

PURPOSE OF THE REPORT: Reduced bone mineral density is a major public health dilemma with high prevalence. Vertebral fracture (VF) is an independent risk factor for fragility fracture. Lateral vertebral assessment (LVA) in dual-energy x-ray absorptiometry is a reliable, low-radiation, accurate, and cost-effective method for VF assessment. PATIENTS AND METHODS: Five hundred seventy-five scans of oncologic and nononcologic patients were retrospectively reviewed irrespective of age or sex. Patients' symptoms, bone mineral density, and risk factors were also evaluated. Scans in which LVA was not acquired or had previously known VFs were excluded. RESULTS: The mean age of patients was 66 ± 11.5 years. Eleven percent of patients had VFs on LVA, of which 7 were excluded due to known VFs. Ten percent had new VFs, most of whom were women (n = 42). The most common risk factor was secondary osteoporosis in women and rheumatoid arthritis in men. Sixty-eight percent of the patients had solitary fractures, whereas 32% had multiple fractures. Most of these patients had underlying osteopenia (n = 19). FRAX was calculated twice: once with the history of personal fracture marked and the other time unmarked as these would not have been discovered if LVA was not acquired. Statistically significant mean percent difference of 5.4% was found in probability of major osteoporotic fracture and 2.1% in the mean risk of hip fracture. CONCLUSIONS: In our population, 10% patients had unsuspected VFs on LVA in dual-energy x-ray absorptiometry scan. Most of these were nononcologic patients with associated risk factors. Based on the FRAX tool, there is a significant difference in the 10-year risk of fracture when unsuspected fractures discovered on LVA are marked.

Should vertebral fracture assessment be performed in Fracture Liaison Service patients with non-vertebral fracture?

Prior non-vertebral fractures, except of the ankle, are associated with increased likelihood of vertebral fracture. As knowledge of vertebral fracture presence may alter care, vertebral fracture assessment (VFA) is indicated in patients with prior fracture. INTRODUCTION: Vertebral fractures are often unappreciated. It was recently advocated that all Fracture Liaison Service (FLS) patients have densitometric VFA performed. We evaluated the likelihood of vertebral fracture identification with VFA in patients with prior fracture using the Manitoba Bone Density database. METHODS : VFA was performed in patients with T-scores below - 1.5 and age 70 + (or younger with height loss or glucocorticoid use) obtaining bone densitometry in Manitoba from 2010 to 2018. Those with prior clinical vertebral fracture, pathologic fracture, or uninterpretable VFA were excluded. Vertebral fractures were identified using the modified ABQ method. Health records were assessed for non-vertebral fracture (excluding head, neck, hand, foot) diagnosis codes unassociated with trauma prior to DXA. Multivariable odds ratios (ORs) for vertebral fracture were estimated without and with adjustment for age, sex, body mass index, ethnicity, area of residence, income level, comorbidity score, diabetes mellitus, falls in the last year, glucocorticoid use, and lowest BMD T-score. RESULTS: The study cohort consisted of 12,756 patients (94.4% women) with mean (SD) age 75.9 (6.8) years. Vertebral fractures were identified in 1925 (15.1%) overall. Vertebral fractures were significantly more likely (descending order) in those with prior pelvis, hip, humerus, other sites, and forearm, but not ankle fracture. There was modest attenuation with covariate adjustment but statistical significance was maintained. CONCLUSIONS: Prior hip, humerus, pelvis, forearm, and other fractures are associated with an increased likelihood of previously undiagnosed vertebral fracture, information useful for risk stratification and monitoring. These data support recommending VFA in FLS patients who are age 70 + with low BMD.

Assessment of Instability in Thoracolumbar Burst Fractures Using a New Bone Scan Scoring System.

Background and Objectives: Unstable thoracolumbar burst fractures require surgical management as they can result in neurological deficits if left untreated. This study aimed to evaluate whether a new bone scan scoring system could accurately assess instability in thoracolumbar burst fractures. Materials and Methods: Fifty-two patients with thoracolumbar burst fractures who underwent bone scans and magnetic resonance imaging prior to surgery between January 2015 and August 2017 at Ulsan University Hospital were selected for inclusion. Instability was determined by clinical assessment and imaging, and the Thoracolumbar Injury Classification and Severity score was determined. Bone scans were visually evaluated using a new bone scan scoring system. Bone scan findings of vertebral body (BB) and posterior column (BP) were scored separately and were summed to produce BTS {BTS (total score) = BB (body score, 5 points) + BP (posterior score, 2 points)}. The diagnostic performance of the scoring system for identifying unstable then thoracolumbar burst fractures were assessed. Results: Of the 52 thoracolumbar burst fractures, 34 (65.4%) were unstable and 31 (59.6%) had a Thoracolumbar Injury Classification and Severity score ≥ 5. The diagnostic performance of using BTS ≥ 4 to identify unstable thoracolumbar burst fractures and those with a Thoracolumbar Injury Classification and Severity score ≥ 5 was as follows: sensitivity, 61.8% and 58.1%; specificity, 94.4% and 81.0%; positive predictive value, 95.5% and 81.8%; and negative predictive value, 56.7% and 56.7%, respectively. Conclusions: The proposed bone scan scoring system has a high specificity and positive predictive value for identifying thoracolumbar burst fractures that are unstable or have a Thoracolumbar Injury Classification and Severity score ≥ 5. This scoring system may help to inform decisions regarding surgical management.

Biomechanical Assessment of Fracture Loads and Patterns of the Odontoid Process.

STUDY DESIGN: Laboratory study. OBJECTIVE: This study aimed to investigate the biomechanical competence and fracture characteristics of the odontoid process. SUMMARY OF BACKGROUND DATA: Odontoid fractures of the second cervical vertebra (C2) represent the most common spine fracture type in the elderly. However, very little is known about the underlying biomechanical fracture mechanisms. MATERIALS AND METHODS: A total of 42 C2 human anatomic specimens were scanned via computed tomography, divided in six groups, and subjected to combined quasistatic loading at -15°, 0°, and 15° in sagittal plane and -50° and 0° in transverse plane until fracturing. Bone mineral density (BMD), height, fusion state of the ossification centers, stiffness, yield load, and ultimate load were assessed. RESULTS: While lowest values for stiffness, yield load, and ultimate load were observed at load inclination of 15° in sagittal plane, no statistically significant differences were observed between the study groups ( P ≥0.235). BMD correlated positively with yield load ( r2 =0.350, P <0.001) and ultimate load ( r2 =0.955, P <0.001) but not with stiffness ( r2 =0.082, P =0.07). The specimens with clearly distinguishable fusion of the ossification centers revealed less data scattering of the biomechanical outcomes. CONCLUSION: Load direction plays a subordinate role in traumatic fractures of the odontoid process. BMD was associated with significant correlation to the biomechanical outcomes. Thus, odontoid fractures appear to result from of an interaction between the load magnitude and bone quality.

Thoracic Spine CT Hidden Treasures: Lung Assessment and Extraspinal Findings in Patients with Vertebral Fractures Studied with Full FOV during Breath Hold: Technical Note.

Thoracic spine CTs are usually performed during free breathing and with a narrow field of view; this common practice systematically excludes the assessment of lungs and other extraspinal structures, even if these have been irradiated during the examination. At our institution we perform thoracic spine CT during breath hold with additional full FOV reconstructions; this allows us to also evaluate lungs and extraspinal pathologies in the same examination with no added costs or further radiation exposure. If this simple and costless technical change is routinely applied to thoracic spine CT many concomitant extraspinal pathologies can be ruled out, from neoplasms to pneumonia; the suggested modification also allows an early diagnosis and avoids recalling and re-irradiating the patient in case these findings are partially included in the study. This practice can be further useful during the current pandemic in order to screen any lung opacities suspicious for COVID-19.

Understanding current UK practice for the incidental identification of vertebral fragility fractures from CT scans: an expert elicitation study.

BACKGROUND: There is an emerging interest in using automated approaches to enable the incidental identification of vertebral fragility fractures (VFFs) on existing medical images visualising the spine. AIM: To quantify values, and the degree of uncertainty associated with them, for the incidental identification of VFFs from computed tomography (CT) scans in current practice. METHODS: An expert elicitation exercise was conducted to generate point estimates and measures of uncertainty for four values representing the probability of: VFF being correctly reported by the radiologist; the absence of VFF being correctly assessed by the radiologist; being referred for management when a VFF is identified; having a dual-energy X-ray absorptiometry (DXA) scan after general practitioner (GP) referral. Data from a sample of seven experts in the diagnosis and management of people with VFFs were pooled using mathematical aggregation. RESULTS: The estimated mean values for each probability parameter were: VFF being correctly reported by the radiologist = 0.25 (standard deviation (SD): 0.21); absence of VFF being correctly assessed by the radiologist = 0.89 (0.10); being referred for management when a VFF is identified by the radiologist = 0.15 (0.12); having a DXA scan after GP referral = 0.66 (0.28). DISCUSSION: These estimates could be used to facilitate the subsequent early economic evaluation of potential new approaches to improve the health outcomes of people with VFFs. CONCLUSION: In the absence of epidemiological studies, this study produced point estimates and measures of uncertainty for key parameters needed to describe current pathways for the incidental diagnosis of VFFs.

Bone Texture Assessment on Lateral VFAs Using the Texture Research Investigational Platform (TRIP) and its Fracture Discrimination Ability.

Texture Research Imaging Platform applies trabecular bone score (TBS) measurement principles to images acquired with multiple modalities to assess bone texture at various skeletal sites. This study aimed to assess the bone texture score in dual-energy X-ray absorptiometry-acquired lateral vertebral fracture assessment (VFA) images (BTSVFA), evaluate its reproducibility, and vertebral fracture discrimination ability. Subjects included 178 VF cases and 178 non-VF controls, 136 women and 42 men in each group, age 55-92 years, from two research centers. Cases and controls were matched for age (±5 years), body mass index (±5 kg/m2) and TBS. All participants underwent dual-energy X-ray absorptiometry TBS assessment from standard posterior-anterior lumbar spine scans and BTSVFA assessment. VF presence was determined using VFA images applying the Genant's method. BTSVFA was lower among fractured women compared to non-fractured (0.626 ± 0.109 vs 0.675 ± 0.099, p < 0.01), but not among men. In a binary logistic regression adjusted for study center and sex, for each SD lower BTSVFA, there was a 40% increase (OR 1.40, 95% CI (1.13-1.74)) in the risk of having a prevalent VF; area under the curve (95% CI) 0.616 (0.557-0.675). Inter-assessor and inter-centers ICCs for BTSVFA measurements were very good; 0.96 (0.64-0.99) and 0.98 (0.95-0.99), respectively. The BTSVFA precision (coefficient of variation) was 2.42%. This feasibility study demonstrates the potential to assess trabecular bone texture in lateral VFA images with good reproducibility. BTSVFA can discriminate between fractured and non-fractured women independent of their age, body mass index and TBS. In conclusion, BTSVFA, a potential trabecular texture assessment that excludes the posterior elements, may have value in fracture prediction or as a novel approach to be further investigated in spine surgery planning.

Development of a manufacturer-independent convolutional neural network for the automated identification of vertebral compression fractures in vertebral fracture assessment images using active learning.

BACKGROUND: Convolutional neural networks (CNNs) can identify vertebral compression fractures in GE vertebral fracture assessment (VFA) images with high balanced accuracy, but performance against Hologic VFAs is unknown. To obtain good classification performance, supervised machine learning requires balanced and labeled training data. Active learning is an iterative data annotation process with the ability to reduce the cost of labeling medical image data and reduce class imbalance. PURPOSE: To train CNNs to identify vertebral fractures in Hologic VFAs using an active learning approach, and evaluate the ability of CNNs to generalize to both Hologic and GE VFA images. METHODS: VFAs were obtained from the OsteoLaus Study (labeled Hologic Discovery A, n = 2726), the Manitoba Bone Mineral Density Program (labeled GE Prodigy and iDXA, n = 12,742), and the Canadian Longitudinal Study on Aging (CLSA, unlabeled Hologic Discovery A, n = 17,190). Unlabeled CLSA VFAs were split into five equal-sized partitions (n = 3438) and reviewed sequentially using active learning. Based on predicted fracture probability, 17.6% (n = 3032) of the unlabeled VFAs were selected for expert review using the modified algorithm-based qualitative (mABQ) method. CNNs were simultaneously trained on Hologic, GE dual-energy and GE single-energy VFAs. Two ensemble CNNs were constructed using the maximum and mean predicted probability from six separately trained CNNs that differed due to stochastic variation. CNNs were evaluated against the OsteoLaus validation set (n = 408) during the active learning process; ensemble performance was measured against the OsteoLaus test set (n = 819). RESULTS: The baseline CNN, prior to active learning, achieved 55.0% sensitivity, 97.9% specificity, 57.9% positive predictive value (PPV), F1-score 56.4%. Through active learning, 2942 CLSA Hologic VFAs (492 fractures) were added to the training data-increasing the proportion of Hologic VFAs with fractures from 4.2% to 12.5%. With active learning, CNN performance improved to 80.0% sensitivity, 99.7% specificity, 94.1% PPV, F1-score 86.5%. The CNN maximum ensemble achieved 91.9% sensitivity (100% for grade 3 and 95.5% for grade 2 fractures), 99.0% specificity, 81.0% PPV, F1-score 86.1%. CONCLUSION: Simultaneously training on a composite dataset consisting of both Hologic and GE VFAs allowed for the development of a single manufacturer-independent CNN that generalized to both scanner types with good classification performance. Active learning can reduce class imbalance and produce an effective medical image classifier while only labeling a subset of available unlabeled image data-thereby reducing the time and cost required to train a machine learning model.

A clinical tool to identify older women with back pain at high risk of osteoporotic vertebral fractures (Vfrac): a population-based cohort study with exploratory economic evaluation.

BACKGROUND: osteoporotic vertebral fractures (OVFs) identify people at high risk of future fractures, but despite this, less than a third come to clinical attention. The objective of this study was to develop a clinical tool to aid health care professionals decide which older women with back pain should have a spinal radiograph. METHODS: a population-based cohort of 1,635 women aged 65+ years with self-reported back pain in the previous 4 months were recruited from primary care. Exposure data were collected through self-completion questionnaires and physical examination, including descriptions of back pain and traditional risk factors for osteoporosis. Outcome was the presence/absence of OVFs on spinal radiographs. Logistic regression models identified independent predictors of OVFs, with the area under the (receiver operating) curve calculated for the final model, and a cut-point was identified. RESULTS: mean age was 73.9 years and 209 (12.8%) had OVFs. The final Vfrac model comprised 15 predictors of OVF, with an AUC of 0.802 (95% CI: 0.764-0.840). Sensitivity was 72.4% and specificity was 72.9%. Vfrac identified 93% of those with more than one OVF and two-thirds of those with one OVF. Performance was enhanced by inclusion of self-reported back pain descriptors, removal of which reduced AUC to 0.742 (95% CI: 0.696-0.788) and sensitivity to 66.5%. Health economic modelling to support a future trial was favourable. CONCLUSIONS: the Vfrac clinical tool appears to be valid and is improved by the addition of self-reported back pain symptoms. The tool now requires testing to establish real-world clinical and cost-effectiveness.

Assessment of clinical, imaging, surgical risk factors for subsequent fracture following vertebral augmentation in osteoporotic patients.

INTRODUCTION: Currently, the risk factors for subsequent fracture following vertebral augmentation remain incomplete and controversial. To provide clinicians with accurate information for developing a preventive strategy, we carried out a comprehensive evaluation of previously controversial and unexplored risk factors. METHODS: We retrospectively reviewed patients with osteoporotic vertebral compression fracture in lumbar spine who received vertebral augmentation between January 2019 and December 2020. Based on whether refracture occurred, patients were assigned to refracture and non-refracture group. The clinical characteristics, imaging parameters (severity of vertebral compression, spinal sagittal alignment, degeneration of paraspinal muscles), and surgical indicators (cement distribution and leakage, correction of spinal sagittal alignment) were collected and analyzed. RESULTS: There were 128 patients and 16 patients in non-refracture and refracture group. The incidence of previous fracture, multiple fractures, and cement leakage were notably higher, relative cross-sectional area of psoas (r-CSAPS) was significantly smaller, CSA ratio, fatty infiltration of erector spinae plus multifidus (FIES+MF), FIPS, postoperative lumbar lordosis (post-LL), correction of body angel (BA), and LL were significantly greater in refracture group. Binary logistic regression analysis revealed previous fracture, cement leakage, post-LL, and correction of BA were independent risk factors. According to the ROC curve, correction of BA showed the highest prediction accuracy, and the critical value was 3.45°. CONCLUSIONS: The occurrence of subsequent fracture might be the consequence of multiple factors. Previous fracture, cement leakage, post-LL, and correction of BA were identified as independent risk factors. Furthermore, the correction of BA should not exceed 3.45°, especially in patients with risk factors.

Utility of bedside assessment to evaluate for cervical-spine fracture post ground-level fall for patients 65 years and older.

OBJECTIVES: The effectiveness of current assessment tools for cervical fracture are mixed with respect to elderly patients. We aim to examine utility of history and physical exam to assess for cervical fracture for elderly patients suffering a ground-level fall. METHODS: Retrospective cohort from a tertiary-care ED for patients ≥65 years, including dementia, after ground-level fall. Logistic regression was used to examine predictability of various clinical factors. Neurologic deficits were considered a hard sign for imaging and were not assessed. RESULTS: Of 1035 patient encounters analyzed, 683 had CT cervical-spine (C-spine) imaging (66.0%) and 16 (1.5%) had cervical fracture. C-spine tenderness (OR 4.7, 95% CI 1.5-14.1), neck pain (OR 10.5, 95% CI 3.4-32.5), altered mental status (AMS) (OR 5.1, 95% CI 1.7-15.6), and external trauma above the clavicles (ETC) (OR 3.8, 95% CI 1.2-12.3) predicted cervical fracture. C-spine tenderness and neck pain were collinear and run-in separate models. Dementia (OR 0.2, 95% CI 0.4-0.9) did not predict cervical fracture in this population. A combination of ETC, C-spine tenderness, and AMS had a sensitivity = 100% and specificity = 40.0% for detection of cervical fracture. ETC was found in all but two fractures requiring intervention with negative predictive value = 99.3%. CONCLUSIONS: Clinical assessment for elderly patients without neurologic signs, together with the absence of ETC, cervical tenderness, and AMS may be reliable in ruling out cervical fracture after a ground-level fall, including patients with history of dementia. Fractures requiring intervention were rare in patients without ETC. However, findings are retrospective and prospective validation is required.