Usefulness of the Trabecular Bone Score in Assessing the Risk of Vertebral Fractures in Patients with Cirrhosis.
Yui OgisoTatsunori HanaiKayoko NishimuraTakao MiwaToshihide MaedaKenji ImaiAtsushi SuetsuguKoji TakaiMasahito ShimizuPublished in: Journal of clinical medicine (2022)
The trabecular bone score (TBS), a surrogate measure of bone microarchitecture, provides complementary information to bone mineral density (BMD) in the assessment of osteoporotic fracture risk. This cross-sectional study aimed to determine whether TBS can identify patients with liver cirrhosis that are at risk of vertebral fractures. We enrolled 275 patients who completed evaluations for lumbar BMD, TBS, and vertebral fractures between November 2018 and April 2021. BMD was measured using dual-energy X-ray absorptiometry (DXA), TBS was calculated by analyzing DXA images using TBS iNsight software, and vertebral fractures were evaluated using Genant's semi-quantitative method with lateral X-ray images. Factors associated with vertebral fractures and their correlation with the TBS were identified using regression models. Of the enrolled patients, 128 (47%) were female, the mean age was 72 years, and 62 (23%) were diagnosed with vertebral fractures. The prevalence of vertebral fractures was higher in women than in men (33% vs. 14%; p < 0.001). The unadjusted odds ratio (OR) of the vertebral fractures for one standard deviation decrease in TBS and BMD was 2.14 (95% confidence interval [CI], 1.69-2.73) and 1.55 (95% CI, 1.26-1.90), respectively. After adjusting for age, sex, and BMD, the adjusted OR of the vertebral fractures in TBS was 2.26 (95% CI, 1.52-3.35). Multivariate linear regression analysis showed that TBS was independently correlated with age (β = -0.211), body mass index (β = -0.251), and BMD (β = 0.583). TBS can help identify patients with cirrhosis at risk of vertebral fractures.
Keyphrases
- bone mineral density
- postmenopausal women
- body composition
- dual energy
- healthcare
- deep learning
- magnetic resonance imaging
- end stage renal disease
- risk factors
- pregnant women
- metabolic syndrome
- insulin resistance
- health information
- newly diagnosed
- polycystic ovary syndrome
- convolutional neural network
- mass spectrometry
- data analysis
- patient reported outcomes