Quantitative CT assessment identifies more heart transplanted patients with progressive coronary wall thickening than standard clinical read

Computed tomography pericoronary adipose tissue density predicts coronary allograft vasculopathy and adverse clinical outcomes after cardiac transplantation

AIMS

To assess pericoronary adipose tissue (PCAT) density on coronary computed tomography angiography (CCTA) as a marker of inflammatory disease activity in coronary allograft vasculopathy (CAV).

METHODS AND RESULTS

PCAT density, lesion volumes, and total vessel volume-to-myocardial mass ratio (V/M) were retrospectively measured in 126 CCTAs from 94 heart transplant patients [mean age 49 (SD 14.5) years, 40% female] who underwent imaging between 2010 and 2021; age- and sex-matched controls; and patients with atherosclerosis. PCAT density was higher in transplant patients with CAV [n = 40; -73.0 HU (SD 9.3)] than without CAV [n = 86; -77.9 HU (SD 8.2)], and controls [n = 12; -86.2 HU (SD 5.4)], P < 0.01 for both. Unlike patients with atherosclerotic coronary artery disease (n = 32), CAV lesions were predominantly non-calcified and comprised of mostly fibrous or fibrofatty tissue. V/M was lower in patients with CAV than without [32.4 mm3/g (SD 9.7) vs. 41.4 mm3/g (SD 12.3), P < 0.0001]. PCAT density and V/M improved the ability to predict CAV from area under the receiver operating characteristic curve (AUC) 0.75-0.85 when added to donor age and donor hypertension status (P < 0.0001). PCAT density above -66 HU was associated with a greater incidence of all-cause mortality {odds ratio [OR] 18.0 [95% confidence interval (CI) 3.25-99.6], P < 0.01} and the composite endpoint of death, CAV progression, acute rejection, and coronary revascularization [OR 7.47 (95% CI 1.8-31.6), P = 0.01] over 5.3 (SD 2.1) years.

CONCLUSION

Heart transplant patients with CAV have higher PCAT density and lower V/M than those without. Increased PCAT density is associated with adverse clinical outcomes. These CCTA metrics could be useful for the diagnosis and monitoring of CAV severity.

New Non-Invasive Imaging Technologies in Cardiac Transplant Follow-Up: Acquired Evidence and Future Options

Heart transplantation (HT) is the established treatment for end-stage heart failure, significantly enhancing patients' survival and quality of life. To ensure optimal outcomes, the routine monitoring of HT recipients is paramount. While existing guidelines offer guidance on a blend of invasive and non-invasive imaging techniques, certain aspects such as the timing of echocardiographic assessments and the role of echocardiography or cardiac magnetic resonance (CMR) as alternatives to serial endomyocardial biopsies (EMBs) for rejection monitoring are not specifically outlined in the guidelines. Furthermore, invasive coronary angiography (ICA) is still recommended as the gold-standard procedure, usually performed one year after surgery and every two years thereafter. This review focuses on recent advancements in non-invasive and contrast-saving imaging techniques that have been investigated for HT patients. The aim of the manuscript is to identify imaging modalities that may potentially replace or reduce the need for invasive procedures such as ICA and EMB, considering their respective advantages and disadvantages. We emphasize the transformative potential of non-invasive techniques in elevating patient care. Advanced echocardiography techniques, including strain imaging and tissue Doppler imaging, offer enhanced insights into cardiac function, while CMR, through its multi-parametric mapping techniques, such as T1 and T2 mapping, allows for the non-invasive assessment of inflammation and tissue characterization. Cardiac computed tomography (CCT), particularly with its ability to evaluate coronary artery disease and assess graft vasculopathy, emerges as an integral tool in the follow-up of HT patients. Recent studies have highlighted the potential of nuclear myocardial perfusion imaging, including myocardial blood flow quantification, as a non-invasive method for diagnosing and prognosticating CAV. These advanced imaging approaches hold promise in mitigating the need for invasive procedures like ICA and EMB when evaluating the benefits and limitations of each modality.

Anatomical attention-based prediction of postoperative pulmonary venous obstruction via CTA images

Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart disease, with which some patients suffer from postoperative pulmonary venous obstruction (PPVO), requiring particular follow-up strategies and treatments. PPVO prediction has important clinical significance, while building a PPVO prediction model is challenging due to limited data and class imbalance distribution. Inspired by the anatomical evidence of PPVO, which is related to the structure of the left atrium (LA) and pulmonary vein (PV), we design an effective multi-task network for PPVO classification. The proposed method incorporates clinical priors and merits of the segmentation-based network into the classification task. The features learned from segmenting LA and PV are concatenated into the PPVO classification branch to constrain the learning of discriminative features. Anatomical-guided attention is applied in the aggregation of these features to restrict them focusing on TAPVC-related regions. To deal with the imbalance classification problem of PPVO, a novel classification loss derived by masked class activation map (MCAM) is designed to improve the classification performance. Computed tomography angiography (CTA) images of 146 patients diagnosed with supracardiac TAPVC in Shanghai Children's Medical Center and Guangdong Provincial People's Hospital were enrolled in this work. The comprehensive experiments demonstrate the effectiveness and generalization of our proposed method. The automatic PPVO prediction model shows the potential application in helping clinicians develop follow-up strategies, thereby improving the survival rate of TAPVC patients.

Building A Risk Prediction Model for Postoperative Pulmonary Vein Obstruction via Quantitative Analysis of CTA Images

Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufcient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are rst reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identied from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantied. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0.88 and 0.87 respectively, demonstrating its effectiveness in risk prediction.

Evaluation of cardiac allograft vasculopathy by positron emission tomography

Cardiac allograft vasculopathy (CAV) remains one of the most important late occurring complications in heart transplant (HT) recipients significantly effecting graft survival. Recently, there has been tremendous focus on the development of effective and safe non-invasive diagnostic strategies for the diagnosis of CAV employing a wide range of imaging technologies. During the past decade multiple studies have been published using positron emission tomography (PET) myocardial perfusion imaging, establishing the value of PET myocardial blood flow quantification for the evaluation of CAV. These independent investigations demonstrate that PET can be successfully used to establish the diagnosis of CAV, can be utilized for prognostication and may be used for serial monitoring of HT recipients. In addition, molecular imaging techniques have started to emerge as new tools to enhance our knowledge to better understand the pathophysiology of CAV.

Clinical implementation of coronary computed tomography angiography for routine detection of cardiac allograft vasculopathy in heart transplant patients

Cardiac allograft vasculopathy (CAV) is an accelerated form of coronary artery disease that affects long‐term outcomes in heart transplant (HTx) patients. We prospectively evaluated the feasibility of coronary computed tomography angiography (CCTA) for the detection of CAV during clinical implementation at our center. All consecutive HTx patients >4 years post‐transplant were actively converted from myocardial perfusion imaging to CCTA for the annual assessment of CAV. Between February 2018 and May 2019, 129/172 (75%) HTx patients underwent a CCTA. Renal impairment (n = 21/43) was the most frequent reason for patients could not undergo CCTA. CCTA image quality was good–excellent in 118/129 (92%) patients, and the radiation dose was 2.1 (1.6–2.8) mSv. CCTA showed obstructive CAV in 19/129 (15%) patients. Thirteen (10%) patients underwent additional tests, of which 8 patients underwent coronary revascularization within 90 days of CCTA. After 1 year, 3 additional coronary angiograms were performed, resulting in one revascularization in a patient with known severe CAV who developed ventricular tachycardia. One myocardial infarction after coronary stenting and 2 non‐cardiac deaths were observed. CCTA can be successfully implemented for routine detection of CAV with good image quality and low radiation dose. CCTA allows CAV evaluation with the limited need for additional invasive testing.

Coronary computed tomography-angiography quantitative plaque analysis improves detection of early cardiac allograft vasculopathy: A pilot study

Cardiac allograft vasculopathy (CAV) is an increasingly important complication after cardiac transplant. We assessed the additive diagnostic benefit of quantitative plaque analysis in patients undergoing coronary computed tomography-angiography (CCTA). Consecutive patients undergoing CCTA for CAV surveillance were identified. Scans were visually interpreted for coronary stenosis. Semiautomated software was used to quantify noncalcified plaque (NCP), as well as its components. Optimal diagnostic cut-offs for CAV, with coronary angiography as gold standard, were defined using receiver operating characteristic curves. In total, 36 scans were identified in 17 patients. CAV was present in 17 (46.0%) reference coronary angiograms, at a median of 1.9 years before CCTA. Median NCP (147 vs 58, P < .001), low-density NCP (median 4.5 vs 0.9, P = .003), fibrous plaque (median 76.1 vs 31.1, P = .003), and fibrofatty plaque (median 63.6 vs 27.6, P < .001) volumes were higher in patients with CAV, whereas calcified plaque was not (median 0.0 vs 0.0, P = .510). Visual assessment of CCTA alone was 70.6% sensitive and 100% specific for CAV. The addition of total NCP volume increased sensitivity to 82.4% while maintaining 100% specificity. NCP volume is significantly higher in patients with CAV. The addition of quantitative analysis to visual interpretation improves the sensitivity for detecting CAV without reducing specificity.

Quantitative coronary computed tomography angiography for the detection of cardiac allograft vasculopathy

Objectives

To associate coronary wall volume and composition, derived from coronary computed tomography angiography (CTA), with cardiac allograft vasculopathy (CAV) detected on invasive coronary angiography (ICA) in heart-transplanted (HTX) patients.

Methods

We included consecutive adults who received ICA and coronary CTA for evaluation of CAV ≥ 10 months after HTX. In all coronary segments, we assessed lumen and wall volumes and segmental length, calculated volume-length ratio (VLR) (volumes indexed by segmental length; mm³/mm), wall burden (WB) (wall/wall + lumen volumes; %), and assessed proportions of calcified, fibrotic, fibro-fatty, and low-attenuation tissue (%) in coronary wall. We rendered independent CTA measures associated with CAV by ICA, tested their discriminatory capacity, and assessed concordance between CTA and ICA.

Results

Among 50 patients (84% men; 53.6 ± 11.9 years), we analyzed 632 coronary segments. Mean interval between HTX and CTA was 6.7 ± 4.7 years and between ICA and CTA 1 (0–1) day. Segmental VLR, WB, and proportion of fibrotic tissue were independently associated with CAV (OR = 1.06–1.27; p ≤ 0.002), reaching a high discriminatory capacity (combination of all three: AUC = 0.84; 95%CI, 0.75–0.90). Concordance between CTA and ICA was higher in advanced CAV (88%) compared with that in none (37%) and mild (19%) CAV. Discordance was primarily driven by a large number of segments with coronary wall changes on CTA but without luminal stenoses on ICA (177/591; 25%).

Conclusion

CTA-derived coronary wall VLR, WB, and the proportion of fibrotic tissue are independent markers of CAV. Combination of these three parameters may aid the detection of early CAV not detected by ICA, the current standard of care.

Key Points

• Coronary CTA detects CAV in HTX patients.

• Coronary wall volume-length ratio, wall burden, and proportion of fibrotic tissue are independently associated with CAV.

• In contrast to ICA, coronary CTA may identify the early stages of CAV.

Electronic supplementary material

The online version of this article (10.1007/s00330-019-06653-3) contains supplementary material, which is available to authorized users.