# Demographically informed models for improving synthetic haematocrit and extracellular volume estimation in cardiac computed tomography

## TL;DR

This study develops a method to estimate blood-related values in heart scans without blood tests, improving accuracy by considering patient demographics like sex and BMI.

## Contribution

A novel method for predicting synthetic haematocrit using sex- and BMI-stratified models to improve ECV estimation in cardiac CT.

## Key findings

- A univariable linear regression model using Hounsfield units outperformed previous models in predicting haematocrit.
- Sex-specific BMI thresholds improved synthetic haematocrit and ECV prediction accuracy.
- The final model achieved a strong correlation (Pearson R 0.89) between predicted and actual extracellular volume.

## Abstract

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.

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.

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.

Graphical AbstractTrends between left ventricular blood pool Hounsfield units (HUblood) and serum haematocrit (Hct) were stratified by sex and body mass index (BMI), obtaining three separate regressions to optimize synthetic Hct (synHct) and extracellular volume (synECV) predictions.For image description, please refer to the figure legend and surrounding text.

Trends between left ventricular blood pool Hounsfield units (HUblood) and serum haematocrit (Hct) were stratified by sex and body mass index (BMI), obtaining three separate regressions to optimize synthetic Hct (synHct) and extracellular volume (synECV) predictions.