Prediction of abnormal bone mass with a pericoronary adipose tissue Attenuation model
Abstract Background The aim is to explore the value of pericoronary adipose tissue (PCAT) attenuation in predicting abnormal bone mass by establishing a prediction model. Materials and methods 361 patients with coronary computed tomography angiography (CCTA) and quantitative computed tomography (QCT...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
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| Series: | BMC Geriatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12877-025-05928-3 |
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| Summary: | Abstract Background The aim is to explore the value of pericoronary adipose tissue (PCAT) attenuation in predicting abnormal bone mass by establishing a prediction model. Materials and methods 361 patients with coronary computed tomography angiography (CCTA) and quantitative computed tomography (QCT) scans were retrospectively recruited. 311 patients from institution 1 from July 2021 to January 2023 were divided into a training cohort (n = 217) and an internal cohort (n = 94). The external cohort comprised 50 patients from institution 2 from January 2023 to August 2023. Clinical variables and PCAT attenuation of the major epicardial vessels were obtained. Univariate and multivariate logistic regression analyses were used to identify factors with statistical significance. Model 1 was constructed based on clinical variables. Model 2 was constructed by combining the clinical variables with the PCAT attenuation. The performances of the models were assessed using receiver operating characteristic curve analysis, calibration curves and decision curve analysis (DCA). Results Age, gender, coronary artery disease reporting and data system (CAD-RADS), statins and RCAPCAT were found to be significant predictors of abnormal bone mass. The area under the curve (AUC) of Model 2 was superior to that of Model 1 in the training cohort (AUC: 0.959 vs. 0.920), internal (AUC: 0.943 vs. 0.890) and external validation cohorts (AUC: 0.889 vs. 0.812). The calibration curves and DCA indicated that Model 2 had the higher clinical value. Conclusion The model incorporating clinical factors and RCAPCAT has good performance in predicting bone mass abnormalities. |
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| ISSN: | 1471-2318 |