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1.
J Orthop Surg Res ; 19(1): 335, 2024 Jun 07.
Artigo em Inglês | MEDLINE | ID: mdl-38845012

RESUMO

BACKGROUND: Existing studies have shown that computed tomography (CT) attenuation and skeletal muscle tissue are strongly associated with osteoporosis; however, few studies have examined whether vertebral HU values and the pectoral muscle index (PMI) measured at the level of the 4th thoracic vertebra (T4) are strongly associated with bone mineral density (BMD). In this study, we demonstrate that vertebral HU values and the PMI based on chest CT can be used to opportunistically screen for osteoporosis and reduce fracture risk through prompt treatment. METHODS: We retrospectively evaluated 1000 patients who underwent chest CT and DXA scans from August 2020-2022. The T4 HU value and PMI were obtained using manual chest CT measurements. The participants were classified into normal, osteopenia, and osteoporosis groups based on the results of dual-energy X-ray (DXA) absorptiometry. We compared the clinical baseline data, T4 HU value, and PMI between the three groups of patients and analyzed the correlation between the T4 HU value, PMI, and BMD to further evaluate the diagnostic efficacy of the T4 HU value and PMI for patients with low BMD and osteoporosis. RESULTS: The study ultimately enrolled 469 participants. The T4 HU value and PMI had a high screening capacity for both low BMD and osteoporosis. The combined diagnostic model-incorporating sex, age, BMI, T4 HU value, and PMI-demonstrated the best diagnostic efficacy, with areas under the receiver operating characteristic curve (AUC) of 0.887 and 0.892 for identifying low BMD and osteoporosis, respectively. CONCLUSIONS: The measurement of T4 HU value and PMI on chest CT can be used as an opportunistic screening tool for osteoporosis with excellent diagnostic efficacy. This approach allows the early prevention of osteoporotic fractures via the timely screening of individuals at high risk of osteoporosis without requiring additional radiation.


Assuntos
Absorciometria de Fóton , Densidade Óssea , Osteoporose , Músculos Peitorais , Vértebras Torácicas , Tomografia Computadorizada por Raios X , Humanos , Feminino , Osteoporose/diagnóstico por imagem , Masculino , Vértebras Torácicas/diagnóstico por imagem , Estudos Retrospectivos , Pessoa de Meia-Idade , Tomografia Computadorizada por Raios X/métodos , Idoso , Absorciometria de Fóton/métodos , Músculos Peitorais/diagnóstico por imagem , Programas de Rastreamento/métodos , Idoso de 80 Anos ou mais , Radiografia Torácica/métodos , Adulto
2.
Orthop Surg ; 2024 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-39224927

RESUMO

OBJECTIVE: Hip fracture (HF) has been described as the "last fracture of life" in the elderly, so the assessment of HF risk is extremely important. Currently, few studies have examined the relationship between imaging data from chest computed tomography (CT) and HF. This study demonstrated that pectoral muscle index (PMI) and vertebral body attenuation values could predict HF, aiming to opportunistically assess the risk of HF in patients without bone mineral density (BMD) based on chest CT for other diseases. METHODS: In the retrospective study, 800 participants who had both BMD and chest CT were enrolled from January 2021 to January 2024. After exclusion, 472 patients were finally enrolled, divided into the healthy control (HC) group and the HF group. Clinical data were collected, and differences between the two groups were compared. A predictive model was constructed based on the PMI and CT value of the fourth thoracic vertebra (T4HU) by logistic regression analysis, and the predictive effect of the model was analyzed by using the receiver operating characteristic (ROC) curve. Finally, the clinical utility of the model was analyzed using decision curve analysis (DCA) and clinical impact curves. RESULTS: Both PMI and T4HU were lower in the HF group than in the HC group (p < 0.05); low PMI and low T4HU were risk factors for HF. The predictive model incorporating PMI and T4HU on the basis of age and BMI had excellent diagnostic efficacy with an area under the curve (AUC) of 0.865 (95% confidence interval [CI]: 0.830-0.894, p < 0.01), sensitivity and specificity of 0.820 and 0.754, respectively. The clinical utility of the model was validated using calibration curves and DCA. The AUC of the predictive model incorporating BMD based on age and BMI was 0.865 (95% CI: 0.831-0.895, p < 0.01), with sensitivity and specificity of 0.698 and 0.711, respectively. There was no significant difference in diagnostic efficacy between the two models (p = 0.967). CONCLUSIONS: PMI and T4HU are predictors of HF in patients. In the absence of dual-energy x-ray absorptiometry (DXA), the risk of HF can be assessed by measuring the PMI and T4HU on chest CT examination due to other diseases, and further treatment can be provided in time to reduce the incidence of HF.

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