Evaluation of perivascular fat attenuation with coronary CT angiography in cardiac transplantation patients: an imaging biomarker candidate for prediction of cardiac mortality and re-transplantation.

In cardiac transplant recipients, non-invasive allograft surveillance for identifying patients at risk for graft failure remains challenging. The fat attenuation index (FAI) of the perivascular adipose tissue in coronary computed tomography angiography (CCTA) predicts outcomes in coronary artery disease in non-transplanted hearts; however, it has not been evaluated in cardiac transplant patients. We followed 39 cardiac transplant patients with two or more CCTAs obtained between 2010 and 2021. We performed FAI measurements around the proximal 4 cm segments of the left anterior descending (LAD), right coronary artery (RCA), and left circumflex artery (LCx) using a previously validated methodology. The FAI was analyzed at a threshold of - 30 to - 190 Hounsfield units. FAI measurements were completed in 113 CCTAs, obtained on two same-vendor CT models. Within each CCTA, the FAI values between coronary vessels were strongly correlated (RCA and LAD R = 0.67 (p < 0.0001), RCA and LCx R = 0.58 (p < 0.0001), LAD and LCx R = 0.67 (p < 0.0001)). The FAIs of each coronary vessel between the patient's first and last CCTA completed at 120 kV were also correlated (RCA R = 0.73 (p < 0.0001), LAD R = 0.81 (p < 0.0001), LCx R = 0.55 (p = 0.0069). Finally, a high mean FAI value of all three coronary vessels at baseline (mean ≥  - 71 HU) was predictive of cardiac mortality or re-transplantation, however, not predictive of all cause-mortality. High baseline FAI values may identify a higher-risk cardiac transplant population; thus, FAI may support the implementation of CCTA in post-transplant surveillance. • Perivascular fat attenuation measured with coronary CT is feasible in cardiac transplant patients and may predict cardiac mortality or need for re-transplantation.

Moser PT ，Schernthaner R ，Loewe C ，Strassl A ，Denzinger F ，Faby S ，Wels M ，Nizhnikava V ，Uyanik-Uenal K ，Zuckermann A ，Stelzmueller ME ，Beitzke D ... -  《-》

Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data.

Coronary artery inflammation inhibits adipogenesis in adjacent perivascular fat. A novel imaging biomarker-the perivascular fat attenuation index (FAI)-captures coronary inflammation by mapping spatial changes of perivascular fat attenuation on coronary computed tomography angiography (CTA). However, the ability of the perivascular FAI to predict clinical outcomes is unknown. In the Cardiovascular RISk Prediction using Computed Tomography (CRISP-CT) study, we did a post-hoc analysis of outcome data gathered prospectively from two independent cohorts of consecutive patients undergoing coronary CTA in Erlangen, Germany (derivation cohort) and Cleveland, OH, USA (validation cohort). Perivascular fat attenuation mapping was done around the three major coronary arteries-the proximal right coronary artery, the left anterior descending artery, and the left circumflex artery. We assessed the prognostic value of perivascular fat attenuation mapping for all-cause and cardiac mortality in Cox regression models, adjusted for age, sex, cardiovascular risk factors, tube voltage, modified Duke coronary artery disease index, and number of coronary CTA-derived high-risk plaque features. Between 2005 and 2009, 1872 participants in the derivation cohort underwent coronary CTA (median age 62 years [range 17-89]). Between 2008 and 2016, 2040 patients in the validation cohort had coronary CTA (median age 53 years [range 19-87]). Median follow-up was 72 months (range 51-109) in the derivation cohort and 54 months (range 4-105) in the validation cohort. In both cohorts, high perivascular FAI values around the proximal right coronary artery and left anterior descending artery (but not around the left circumflex artery) were predictive of all-cause and cardiac mortality and correlated strongly with each other. Therefore, the perivascular FAI measured around the right coronary artery was used as a representative biomarker of global coronary inflammation (for prediction of cardiac mortality, hazard ratio [HR] 2·15, 95% CI 1·33-3·48; p=0·0017 in the derivation cohort, and 2·06, 1·50-2·83; p<0·0001 in the validation cohort). The optimum cutoff for the perivascular FAI, above which there is a steep increase in cardiac mortality, was ascertained as -70·1 Hounsfield units (HU) or higher in the derivation cohort (HR 9·04, 95% CI 3·35-24·40; p<0·0001 for cardiac mortality; 2·55, 1·65-3·92; p<0·0001 for all-cause mortality). This cutoff was confirmed in the validation cohort (HR 5·62, 95% CI 2·90-10·88; p<0·0001 for cardiac mortality; 3·69, 2·26-6·02; p<0·0001 for all-cause mortality). Perivascular FAI improved risk discrimination in both cohorts, leading to significant reclassification for all-cause and cardiac mortality. The perivascular FAI enhances cardiac risk prediction and restratification over and above current state-of-the-art assessment in coronary CTA by providing a quantitative measure of coronary inflammation. High perivascular FAI values (cutoff ≥-70·1 HU) are an indicator of increased cardiac mortality and, therefore, could guide early targeted primary prevention and intensive secondary prevention in patients. British Heart Foundation, and the National Institute of Health Research Oxford Biomedical Research Centre.

Oikonomou EK ，Marwan M ，Desai MY ，Mancio J ，Alashi A ，Hutt Centeno E ，Thomas S ，Herdman L ，Kotanidis CP ，Thomas KE ，Griffin BP ，Flamm SD ，Antonopoulos AS ，Shirodaria C ，Sabharwal N ，Deanfield J ，Neubauer S ，Hopewell JC ，Channon KM ，Achenbach S ，Antoniades C ... -  《-》

Towards reference values of pericoronary adipose tissue attenuation: impact of coronary artery and tube voltage in coronary computed tomography angiography.

Ma R ，Ties D ，van Assen M ，Pelgrim GJ ，Sidorenkov G ，van Ooijen PMA ，van der Harst P ，van Dijk R ，Vliegenthart R ... -  《-》

Standardized measurement of coronary inflammation using cardiovascular computed tomography: integration in clinical care as a prognostic medical device.

Coronary computed tomography angiography (CCTA) is a first-line modality in the investigation of suspected coronary artery disease (CAD). Mapping of perivascular fat attenuation index (FAI) on routine CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which integrates standardized FAI mapping together with clinical risk factors and plaque metrics to provide individualized cardiovascular risk prediction. The study included 3912 consecutive patients undergoing CCTA as part of clinical care in the USA (n = 2040) and Europe (n = 1872). These cohorts were used to generate age-specific nomograms and percentile curves as reference maps for the standardized interpretation of FAI. The first output of CaRi-Heart® is the FAI-Score of each coronary artery, which provides a measure of coronary inflammation adjusted for technical, biological, and anatomical characteristics. FAI-Score is then incorporated into a risk prediction algorithm together with clinical risk factors and CCTA-derived coronary plaque metrics to generate the CaRi-Heart® Risk that predicts the likelihood of a fatal cardiac event at 8 years. CaRi-Heart® Risk was trained in the US population and its performance was validated externally in the European population. It improved risk discrimination over a clinical risk factor-based model [Δ(C-statistic) of 0.085, P = 0.01 in the US Cohort and 0.149, P < 0.001 in the European cohort] and had a consistent net clinical benefit on decision curve analysis above a baseline traditional risk factor-based model across the spectrum of cardiac risk. Mapping of perivascular FAI on CCTA enables the non-invasive detection of coronary artery inflammation by quantifying spatial changes in perivascular fat composition. We now report the performance of a new medical device, CaRi-Heart®, which allows standardized measurement of coronary inflammation by calculating the FAI-Score of each coronary artery. The CaRi-Heart® device provides a reliable prediction of the patient's absolute risk for a fatal cardiac event by incorporating traditional cardiovascular risk factors along with comprehensive CCTA coronary plaque and perivascular adipose tissue phenotyping. This integration advances the prognostic utility of CCTA for individual patients and paves the way for its use as a dual diagnostic and prognostic tool among patients referred for CCTA.

Oikonomou EK ，Antonopoulos AS ，Schottlander D ，Marwan M ，Mathers C ，Tomlins P ，Siddique M ，Klüner LV ，Shirodaria C ，Mavrogiannis MC ，Thomas S ，Fava A ，Deanfield J ，Channon KM ，Neubauer S ，Desai MY ，Achenbach S ，Antoniades C ... -  《-》

Identification of patients with acute myocardial infarction based on coronary CT angiography: the value of pericoronary adipose tissue radiomics.

To determine whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate acute myocardial infarction (MI) from unstable angina (UA). In a single-center retrospective case-control study, patients with acute MI (n = 105) were matched to patients with UA (n = 105) and all patients were randomly divided into training and validation cohorts with a ratio of 7:3. Fat attenuation index (FAI) and PCAT radiomics features selected by Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) around the proximal three major epicardial coronary vessels (LAD [left anterior descending artery], LCx [left circumflex artery], and RCA [right coronary artery]) were used to build logistic regression models. Finally, a FAI model, three radiomics models of PCAT (LAD, LCx, and RCA), and a combined model that used the scores of these independent models were constructed. The performance of the models was evaluated by identification, calibration, and clinical application. In training and validation cohorts, compared with the FAI model (AUC = 0.53, 0.50), the combined model achieved superior performance (AUC = 0.97, 0.95) while there was a significant difference of AUC between two models (p < 0.05). The calibration curves of the combined model demonstrated the smallest Brier score loss. Decision curve analysis suggested that the combined model provided higher clinical benefit than the FAI model. The CCTA-based radiomics phenotype of PCAT outperforms the FAI model in discriminating acute MI from UA. The combination of PCAT radiomics and FAI could further enhance the performance of acute MI identification. • Fat attenuation index based on CCTA can detect inflammation-induced changes in the ratio of lipid to aqueous phase in pericoronary adipose tissue. • Fat attenuation index cannot distinguish acute MI patients from UA patients, suggesting that the two groups have the same degree of ratio of lipid to aqueous phase in pericoronary adipose tissue. • Radiomics features of PCAT have the potential to distinguish acute MI patients from UA patients.

Si N ，Shi K ，Li N ，Dong X ，Zhu C ，Guo Y ，Hu J ，Cui J ，Yang F ，Zhang T ... -  《-》