Sex- and BMI-stratified models predicted synthetic haematocrit accurately, achieving ECV Pearson R 0.89 (P < 0.001), outperforming previous models in cardiac CT.
Does a sex- and BMI-stratified model improve synthetic haematocrit and extracellular volume estimation in patients undergoing cardiac computed tomography?
A sex- and BMI-stratified model improves the accuracy of synthetic haematocrit estimation from cardiac CT, enabling more accurate non-invasive extracellular volume calculation without concurrent blood testing.
Tasa de eventos absoluta: 0% vs 0%
Abstract Aims Cardiac computed tomography-derived extracellular volume (CCT-ECV) is a promising biomarker for non-invasive quantification of myocardial fibrosis. However, serum haematocrit (Hct) is required for accurate CCT-ECV calculation, posing a potential barrier to clinical implementation. This study aims to develop a method for predicting synthetic Hct to derive accurate ECV values without blood testing and investigate the impact of clinical factors on model performance. Methods and results A total of 108 patients 70% male, body mass index (BMI) 27.2 (7.4) kg/m2, age 81.9 (8.6) years undergoing CCT prior to clinically indicated transcatheter aortic valve implantation for severe aortic stenosis were recruited. A non-contrast baseline scan, electrocardiogram (ECG)-gated CT angiography, and a late iodine-enhanced scan were performed on the same day as blood tests for serum Hct and used to compute voxel-wise ECV in the left ventricle. A univariable linear regression model was developed to predict Hct from Hounsfield units at the centre of the blood pool, outperforming previous models in literature. Sex stratification improved accuracy, with a significant difference in models for men at a BMI threshold of 30.7 (P = 0.035). In females, restricting to BMI 22.4 improved performance. Age, estimated glomerular filtration rate, and creatinine did not improve predictions. The final model with combined sex and BMI stratification demonstrated better performance (ECV Pearson R 0.89, P 0.001) than univariable and literature models. Conclusion This study highlights the necessity for sex-specific models to estimate Hct and accurately estimate ECV from CCT. Sex-specific BMI stratification further improves predictions; however, more research is required for females with a low or very high BMI.
Allampalli et al. (Thu,) reported a other. Sex- and BMI-stratified models predicted synthetic haematocrit accurately, achieving ECV Pearson R 0.89 (P < 0.001), outperforming previous models in cardiac CT.