Correlation of CT Values and Bone Mineral Density in Elderly Chinese Patients with Proximal Humeral Fractures

[Fracture sequelae-when, how, what?]

The treatment of fracture sequelae following proximal humeral fractures presents a significant challenge, as they range from necrosis and pseudarthrosis to malunions. Classifications such as Boileau's facilitate diagnosis and treatment planning. Type 1 sequelae, characterized by necrosis, can be treated with either anatomical or reverse prostheses depending on the condition of the rotator cuff. Type 2 sequelae involve locked dislocations and may require bony reconstructions. Type 3 pseudarthrosis is managed either with joint-preserving techniques or prosthetic replacement, depending on the degree of degenerative changes. Type 4 malunions often benefit from corrective osteotomies and/or prosthesis implantation. Studies have demonstrated significant functional improvements following surgical intervention, with individual factors such as deformities or rotator cuff condition influencing treatment strategies.

Opportunistic Screening for Local Osteoporosis of the Proximal Humerus on the Basis of Hounsfield Units: A Study of Patients With Rotator Cuff Tears

OBJECTIVES

The accuracy of dual-energy x-ray absorptiometry (DXA) in predicting proximal humerus osteoporosis in patients with rotator cuff tears (RCTs) undergoing arthroscopic rotator cuff repair (ARCR) is uncertain. The aim of this study was to assess the correlation of computed tomography (CT) Hounsfield units (HU) and the deltoid tuberosity index (DTI) with bone mineral density (BMD), and to evaluate the predictive value of HU values for implant selection.

METHODS

This study analyzed the preoperative shoulder CT scans and x-rays of 184 patients who underwent ARCR. Preoperative CT scans were utilized to assess the multiple anatomical parts of the proximal humerus to ascertain the HU values, whereas preoperative x-rays were analyzed to derive the DTI. Among them, 104 patients with preoperative DXA data were grouped according to the WHO diagnostic criteria for osteoporosis to establish the threshold HU values. The correlation between HU values and DTI and the lowest T score on DXA was studied, and the relationship between the HU value and the type of implant selected was discussed. The Pearson correlation coefficient was employed to examine the relationship between HU values, DTI, and T scores. And the Spearman correlation coefficient was employed to examine the relationship between HU values and enhanced fixation.

RESULTS

There was a significant correlation between the HU values and DTI and the lowest T score (r HU&T score = 0.539-0.576, r DTI&T score = 0.288, p < 0.05). On the basis of the lowest T score grouping, the threshold HU values for diagnosing proximal humeral osteoporosis were obtained: proximal humerus = 47.44, humeral head = 97.62, greater tuberosity = 10.9, and lesser tuberosity = 5.38. There was no correlation between HU values and enhanced fixation (using 5.5-mm anchors or metal anchors instead of 4.5-mm anchors or all-suture anchors) (r = -0.143 to -0.027, p > 0.05).

CONCLUSION

Proximal humeral HU values were significantly correlated with DTI and the lowest T score on DXA. When the HU values in the proximal humerus in patients with RCTs fall below the thresholds, the surgery should be carefully planned. No correlation exists between HU values and enhanced fixation, thus further investigation through group studies is warranted.

LEVEL OF EVIDENCE

Level III, study of nonconsecutive patients, diagnostic study.

Predicting Proximal Humerus Fracture Mechanical Complications: Are Computed Tomography Hounsfield Units the Answer?

INTRODUCTION

The purpose was to determine whether computed tomography (CT) Hounsfield units (HU) as a proxy for bone quality can predict postoperative complications following surgical treatment of proximal humerus fractures.

METHODS

Sixty-six patients with 2-, 3-, or 4-part proximal humerus fractures who underwent surgical fixation at single institution and had complete radiographic data available were included. Radiographic measurements included the deltoid tuberosity index (DTI) on preoperative anterior-posterior shoulder radiographs, and the HU value from the surgical proximal humerus was determined by measuring the humeral head at the midaxial/coronal/sagittal CT image using a circle-type region of interest (≥35 mm 2 ). Postoperative complications recorded were implant failure, development of osteonecrosis, nonunion, and acute periprosthetic fracture. Patients with and without complications were statistically compared, and binary logistic regression was performed to determine whether preoperative proximal humerus CT HU were predictive of complications.

RESULTS

Eight patients (12.1%) developed 11 overall complications, with three patients experiencing multiple complications each. Complications included osteonecrosis (4), implant failure (5), nonunion (1), and acute periprosthetic fracture (1). No difference was observed in demographics or Neer or AO/OTA classification between those with and without complications. Patients with complications had markedly lower DTI and overall HU as well as HU in the coronal and sagittal planes. Regression analysis for average DTI demonstrated a higher DTI and had a 10 times decreased risk of complication ( P = 0.040, odds ratio = -10.5, 95% confidence interval, 0.000 to 0.616). Regression analysis for average total HU also found a higher HU associated with a decreased risk of complications ( P = 0.034, odds ratio = -0.020, 95% confidence interval, 0.980 to 0.962). Logistic regression analysis, including age, age-adjusted Charlson Comorbidity Index, mean DTI, and mean total HU, only found mean total HU to be notable within the model.

DISCUSSION

CT HU may identify patients with poorer bone quality and thus help predict postoperative complications.

LEVEL OF EVIDENCE

Diagnostic Level III.

A bidirectional framework for fracture simulation and deformation-based restoration prediction in pelvic fracture surgical planning

Pelvic fracture is a severe trauma with life-threatening implications. Surgical reduction is essential for restoring the anatomical structure and functional integrity of the pelvis, requiring accurate preoperative planning. However, the complexity of pelvic fractures and limited data availability necessitate labor-intensive manual corrections in a clinical setting. We describe in this paper a novel bidirectional framework for automatic pelvic fracture surgical planning based on fracture simulation and structure restoration. Our fracture simulation method accounts for patient-specific pelvic structures, bone density information, and the randomness of fractures, enabling the generation of various types of fracture cases from healthy pelvises. Based on these features and on adversarial learning, we develop a novel structure restoration network to predict the deformation mapping in CT images before and after a fracture for the precise structural reconstruction of any fracture. Furthermore, a self-supervised strategy based on pelvic anatomical symmetry priors is developed to optimize the details of the restored pelvic structure. Finally, the restored pelvis is used as a template to generate a surgical reduction plan in which the fragments are repositioned in an efficient jigsaw puzzle registration manner. Extensive experiments on simulated and clinical datasets, including scans with metal artifacts, show that our method achieves good accuracy and robustness: a mean SSIM of 90.7% for restorations, with translational errors of 2.88 mm and rotational errors of 3.18°for reductions in real datasets. Our method takes 52.9 s to complete the surgical planning in the phantom study, representing a significant acceleration compared to standard clinical workflows. Our method may facilitate effective surgical planning for pelvic fractures tailored to individual patients in clinical settings.

Can we screen opportunistically for low bone mineral density using CT scans of the shoulder and artificial intelligence?

OBJECTIVE

To evaluate whether the CT attenuation of bones seen on shoulder CT scans could be used to predict low bone mineral density (BMD) (osteopenia/osteoporosis), and to compare the performance of two machine learning models to predict low BMD.

METHODS

In this study, we evaluated 194 patients aged 50 years or greater (69.2 ± 9.1 years; 170 females) who underwent unenhanced shoulder CT scans and dual-energy X-ray absorptiometry within 1 year of each other between January 1, 2010, and December 31, 2021. The CT attenuation of the humerus, glenoid, coracoid, acromion, clavicle, first, second, and third ribs was obtained using 3D-Slicer. Support vector machines (SVMs) and k-nearest neighbours (kNN) were used to predict low BMD. DeLong test was used to compare the areas under the curve (AUCs).

RESULTS

A CT attenuation of 195.4 Hounsfield Units of the clavicle had a sensitivity of 0.577, specificity of 0.781, and AUC of 0.701 to predict low BMD. In the test dataset, the SVM had sensitivity of 0.686, specificity of 1.00, and AUC of 0.857, while the kNN model had sensitivity of 0.966, specificity of 0.200, and AUC of 0.583. The SVM was superior to the CT attenuation of the clavicle (P = .003) but not better than the kNN model (P = .098).

CONCLUSION

The CT attenuation of the clavicle was best for predicting low BMD; however, a multivariable SVM was superior for predicting low BMD.

ADVANCES IN KNOWLEDGE

SVM utilizing the CT attenuations at many sites was best for predicting low BMD.

Association between Deltoid Muscle Density and Proximal Humeral Fracture in Elderly Patients

Background

The potential role of deltoid muscle density in the occurrence of proximal humeral fractures remains uncertain. Therefore, the primary objective of this study was to examine the correlation between deltoid muscle density, as measured by CT attenuation value in Hounsfield units (HU), and the incidence of proximal humeral fractures in elderly patients. By investigating this association, we aim to shed light on the possible influence of deltoid muscle density on fracture risk in this specific population.

Methods

A total of 68 patients with computed tomography (CT) images were retrospectively reviewed. Among them, 34 patients presented with fractures following low-energy injuries, while the remaining 34 patients served as controls and underwent CT scans after low-energy injuries without any fractures. The muscle density of the deltoid muscles was assessed at the approximate tubercle of humerus. We compared these parameters between the two groups and conducted analyses considering factors such as age, sex, laterality, and deltoid muscle density of the shoulders.

Results

The demographic factors related to the shoulder did not exhibit any significant association with proximal humeral fracture. However, we observed a noteworthy difference in deltoid muscle density between patients with fractures (40.85 ± 1.35) and the control group (47.08 ± 1.61) (p = 0.0042), indicating a lower muscle density in the fracture group.

Conclusion

Based on the findings of this study, we can conclude that there exists a negative correlation between deltoid muscle density and the incidence of proximal humeral fractures. These results suggest that lower deltoid muscle density may be associated with an increased risk of proximal humeral fractures in the elderly population under investigation.

Semi-automatic proximal humeral trabecular bone density assessment tool: technique application and clinical validation

PURPOSE

This study aimed to apply a newly developed semi-automatic phantom-less QCT (PL-QCT) to measure proximal humerus trabecular bone density based on chest CT and verify its accuracy and precision.

METHODS

Subcutaneous fat of the shoulder joint and trapezius muscle were used as calibration references for PL-QCT BMD measurement. A self-developed algorithm based on a convolution map was utilized in PL-QCT for semi-automatic BMD measurements. CT values of ROIs used in PL-QCT measurements were directly used for phantom-based quantitative computed tomography (PB-QCT) BMD assessment. The study included 376 proximal humerus for comparison between PB-QCT and PL-QCT. Two sports medicine doctors measured the proximal humerus with PB-QCT and PL-QCT without knowing each other's results. Among them, 100 proximal humerus were included in the inter-operative and intra-operative BMD measurements for evaluating the repeatability and reproducibility of PL-QCT and PB-QCT.

RESULTS

A total of 188 patients with 376 shoulders were involved in this study. The consistency analysis indicated that the average bias between proximal humerus BMDs measured by PB-QCT and PL-QCT was 1.0 mg/cc (agreement range - 9.4 to 11.4; P > 0.05, no significant difference). Regression analysis between PB-QCT and PL-QCT indicated a good correlation (R-square is 0.9723). Short-term repeatability and reproducibility of proximal humerus BMDs measured by PB-QCT (CV: 5.10% and 3.41%) were slightly better than those of PL-QCT (CV: 6.17% and 5.64%).

CONCLUSIONS

We evaluated the bone quality of the proximal humeral using chest CT through the semi-automatic PL-QCT system for the first time. Comparison between it and PB-QCT indicated that it could be a reliable shoulder BMD assessment tool with acceptable accuracy and precision. This study developed and verify a semi-automatic PL-QCT for assessment of proximal humeral bone density based on CT to assist in the assessment of proximal humeral osteoporosis and development of individualized treatment plans for shoulders.

Quantification of bone quality and distribution of the proximal humerus with dual-energy computed tomography

Background

The proximal humerus is a common site of osteoporotic fractures, and bone quality is a predictor of surgical reduction quality. Dual-energy computed tomography (DECT) is assuming an increasingly important role in the quantification of bone mineral density (BMD) due it is ability to perform three-material decomposition. We aimed to analyze the bone quality and distribution of the proximal humerus with DECT quantitatively.

Methods

Sixty-five consecutive patients (average age 49.5±15.2 years; male: female ratio 32:33) without proximal humerus fractures who had undergone DECT were retrospectively selected. The humeral head was divided into 4 regions on a cross section in the medial plane between the greater tuberosity and the surgical neck. The quantitative parameters, including virtual noncalcium (VNCa) value, computed tomography value of calcium (CaCT), computed tomography value of mixed-energy images (regular CT value) (rCT), and relative calcium density (rCaD), were measured. The correlations between the quantitative parameters and age and body mass index (BMI) were analyzed, and the correlations of age, sex, BMI, region of the humeral head, and VNCa value on CaCT were evaluated.

Results

The differences in CaCT, rCT, and rCaD between the 4 regions of proximal humerus were statistically significant (P<0.001), while the difference in VNCa values was not (P=0.688). The calcium concentration (CaCT and rCaD) was the densest in the posteromedial zone. The differences of CaCT, rCT, and rCaD between males and females in the 4 regions of proximal humerus were statistically significant (P<0.05), while those of the posterolateral zone were not (rCT; P>0.05). The differences in VNCa values between males and females were also not significant (P>0.05). Multivariable linear regression analysis indicated that sex, age, BMI, regions, and VNCa were significant (P<0.05) predictors of the CaCT value.

Conclusions

The concentration of calcium was the densest in the posteromedial region of proximal humerus, and the VNCa value of DECT may be used for quantifying the BMD of the proximal humerus.

Morphological Analysis of Fractures of the Proximal Humerus by the Fracture Mapping Technique

OBJECTIVE

Fractures of different parts of the proximal humerus may lead to different postoperative functional deficits, but there are few studies on the morphology and related functions of the proximal humerus. The purpose of this study was to analyze the fracture pattern of the proximal humerus by the three-dimensional (3-D) fracture mapping technique and to further evaluate its clinical utility.

METHODS

Patients with proximal humeral fractures admitted to Pudong Hospital, Fudan University, from January 2018 to December 2020, were analyzed. Three surgeons divided the fractures into groups according to the 3-D CT imaging technique and mapped the fractures on a 3-D template according to the fracture line of each fracture. Finally, the humeral head inversion angle and the functional score were recorded in different fracture types.

RESULTS

A total of 312 cases of humeral fractures were included. Among them, there were 90 patients (28.8%) in the simple greater tuberosity + lesser tuberosity + medial cortex group, with typical fracture features of surgical neck fractures of the humerus + greater tuberosity fractures. Eighty-seven patients (27.9%) in the greater tuberosity + isolated fragment lesser tuberosity + medial cortex group had typical "four-part fractures." There were 45 patients (14.4%) in the greater tuberosity + lesser tuberosity + medial isolated fragment group. Moreover, more patients in this group had medial comminution due to varus displacement of the femoral head. There were 66 patients (21.1%) in the isolated greater tuberosity group, 21 patients (6.7%) in the greater tuberosity + lesser tuberosity group, and three patients (1.0%) in the greater tuberosity + medial cortex group. In addition, the humeral head inversion angle and other statistical differences were observed in the greater tuberosity + lesser tuberosity + medial isolated fragment group.

CONCLUSIONS

This morphological study helps to further identify the characteristics of proximal humerus fracture patterns, which may be closely related to different clinical outcomes. Further relevant studies are needed to verify the reliability of their clinical application and the potential value in surgical planning and postoperative functional rehabilitation.

Effects of anti-osteoporosis therapy on the risk of secondary fractures and surgical complications following surgical fixation of proximal humerus fracture in older people

BACKGROUND

age-related fragility fractures cause significant burden of disease. Within an ageing society, fracture and complication prevention will be essential to balance health expenditure growth.

OBJECTIVE

to assess the effect of anti-osteoporotic therapy on surgical complications and secondary fractures after treatment of fragility fractures.

PATIENTS AND METHODS

retrospective health insurance data from January 2008 to December 2019 of patients ≥65 years with proximal humeral fracture (PHF) treated using locked plate fixation (LPF) or reverse total shoulder arthroplasty were analysed. Cumulative incidences were calculated by Aalen-Johansen estimates. The influence of osteoporosis and pharmaceutical therapy on secondary fractures and surgical complications were analysed using multivariable Fine and Gray Cox regression models.

RESULTS

a total of 43,310 patients (median age 79 years, 84.4% female) with a median follow-up of 40.9 months were included. Five years after PHF, 33.4% of the patients were newly diagnosed with osteoporosis and only 19.8% received anti-osteoporotic therapy. A total of 20.6% (20.1-21.1%) of the patients had at least one secondary fracture with a significant reduction of secondary fracture risk by anti-osteoporotic therapy (P < 0.001). An increased risk for surgical complications (hazard ratio: 1.35, 95% confidence interval: 1.25-1.47, P < 0.001) after LPF could be reversed by anti-osteoporotic therapy. While anti-osteoporotic therapy was more often used in female patients (35.3 vs 19.1%), male patients showed significantly stronger effects reducing the secondary fracture and surgical complication risk.

CONCLUSIONS

a significant number of secondary fractures and surgical complications could be prevented by consequent osteoporosis diagnosis and treatment particularly in male patients. Health-politics and legislation must enforce guideline-based anti-osteoporotic therapy to mitigate burden of disease.

Hounsfield units predicts the occurrence but not the patterns of proximal humerus fracture in the elderly patients

BACKGROUND

Increased incidence of fragility fractures of the proximal humerus has been reported. Proximal humerus Hounsfield unit (HU) measurements based on computed tomography (CT) scans of the shoulder can be used to evaluate bone mineral density (BMD). It is unknown whether HU values can predict the risk of proximal humerus osteoporotic fracture and /or fracture patterns. Therefore, the objectives of this study were to identify whether the HU value is associated with proximal humeral osteoporotic fracture risk, and whether or not it has an impact on the complexity of the fracture.

METHODS

We identified 60 + years old patients' CT scans between 2019 and 2021 according to the inclusion and exclusion criteria. All patients were divided into two groups based on the presence or lack of a fracture in the proximal humerus, meanwhile, patients with fractures were stratified into simple and comminuted fractures based on the Neer classification. HU values were calculated within the proximal humerus and compared between groups using the Student t-test, and receiver operating characteristic (ROC) curve analysis was used to determine the ability of HU values to predict fracture.

RESULTS

A total of 138 patients with proximal humerus fracture (PHF) including 62 simple PHFs and 76 complex PHFs and 138 non-fracture patients were enrolled in the study. The HU values decreased as age increased among all patients. Both male and female patients with PHF had significantly lower HU values compared with non-fracture patients, the area under the curve (AUC) of the ROC curve for males and females was 0.8 and 0.723 respectively. Nevertheless, no significant differences were found between simple and complex fractures of the proximal humerus in the HU values.

CONCLUSION

Decreasing HU values on CT may be an early warning sign of fracture potential, however, it was not a predictive factor for comminuted fracture of the proximal humerus.