Cardiovascular risk factors and illicit drug use may have a more profound effect on coronary atherosclerosis progression in people living with HIV

Cardiovascular Risk in People Living with Human Immunodeficiency (HIV) Viremia Suppression in a Young, Mid-Eastern European Population - Preliminary Study

Purpose

People living with HIV are twice as likely to develop cardiovascular diseases (CVDs) and myocardial infarction related to atherosclerosis than the uninfected population. This study aimed to evaluate the prevalence of subclinical atherosclerosis in a young, mid-eastern European population of PLWH receiving ART for undetectable viremia.

Patients and Methods

This was a single-centre study. We included 34 patients below 50 years old, treated in Szczecin, Poland, with confirmed HIV-1 infection, treated with antiretroviral therapy (ART), and undetectable viremia. All patients underwent coronary artery computed tomography (CACT), carotid artery intima-media thickness (IMT) evaluation, and echocardiography.

Results

In the primary assessment, only two (5.8%) patients had an increased CVD risk calculated using the Framingham Risk Score (FRS), but we identified coronary or carotid plaques in 26.5% of the patients. Neither traditional risk factors nor those associated with HIV significantly influenced the presence of the plaque. IMT was significantly positively correlated with age and the FRS (R=0.38, p=0.04). Relative wall thickness assessed in echocardiography was higher in those with plaque (0.49 vs 0.44, p=0.04) and significantly correlated with IMT (R=0.38, p=0.04).

Conclusion

In our population, more than a quarter of PLWH with undetectable viremia had subclinical atherosclerosis in either the coronary or carotid arteries. The FRS underpredicted atherosclerosis in this population. The role of RWT as a possible early marker of atherosclerosis needs further studies.

Temporal assessment of lesion morphology on radiological images beyond lesion volumes-a proof-of-principle study

OBJECTIVES

To develop a general framework to assess temporal changes in lesion morphology on radiological images beyond volumetric changes and to test whether cocaine abstinence changes coronary plaque structure on serial coronary CT angiography (CTA).

METHODS

Chronic cocaine users with human immunodeficiency virus (HIV) infection were prospectively enrolled to undergo cash-based contingency management to achieve cocaine abstinence. Participants underwent coronary CTA at baseline and 6 and 12 months following recruitment. We segmented all coronary plaques and extracted 1103 radiomic features. We implemented weighted correlation network analysis to derive consensus eigen radiomic features (named as different colors) and used linear mixed models and mediation analysis to assess whether cocaine abstinence affects plaque morphology correcting for clinical variables and plaque volumes and whether serum biomarkers causally mediate these changes. Furthermore, we used Bayesian hidden Markov network changepoint analysis to assess the potential rewiring of the radiomic network.

RESULTS

Sixty-nine PLWH (median age 55 IQR: 52-59 years, 19% female) completed the study, of whom 26 achieved total abstinence. Twenty consensus eigen radiomic features were derived. Cocaine abstinence significantly affected the pink and cyan eigen features (-0.04 CI: [-0.06; -0.02], p = 0.0009; 0.03 CI: [0.001; 0.04], p = 0.0017, respectively). These effects were mediated through changes in endothelin-1 levels. In abstinent individuals, we observed significant rewiring of the latent radiomic signature network.

CONCLUSIONS

Using our proposed framework, we found 1 year of cocaine abstinence to significantly change specific latent coronary plaque morphological features and rewire the latent morphologic network above and beyond changes in plaque volumes and clinical characteristics.

KEY POINTS

• We propose a general methodology to decompose the latent morphology of lesions on radiological images using a radiomics-based systems biology approach. • As a proof-of-principle, we show that 1 year of cocaine abstinence results in significant changes in specific latent coronary plaque morphologic features and rewiring of the latent morphologic network above and beyond changes in plaque volumes and clinical characteristics. • We found endothelin-1 levels to mediate these structural changes providing potential pathological pathways warranting further investigation.

Deep learning-based atherosclerotic coronary plaque segmentation on coronary CT angiography

OBJECTIVES

Volumetric evaluation of coronary artery disease (CAD) allows better prediction of cardiac events. However, CAD segmentation is labor intensive. Our objective was to create an open-source deep learning (DL) model to segment coronary plaques on coronary CT angiography (CCTA).

METHODS

Three hundred eight individuals' 894 CCTA scans with 3035 manually segmented plaques by an expert reader (considered as ground truth) were used to train (186/308, 60%), validate (tune, 61/308, 20%), and test (61/308, 20%) a 3D U-net model. We also evaluated the model on an external test set of 50 individuals with vulnerable plaques acquired at a different site. Furthermore, we applied transfer learning on 77 individuals' data and re-evaluated the model's performance using intra-class correlation coefficient (ICC).

RESULTS

On the test set, DL outperformed the currently used minimum cost approach method to quantify total: ICC: 0.88 [CI: 0.85-0.91] vs. 0.63 [CI: 0.42-0.76], noncalcified: 0.84 [CI: 0.80-0.88] vs. 0.45 [CI: 0.26-0.59], calcified: 0.99 [CI: 0.98-0.99] vs. 0.96 [CI: 0.94-0.97], and low attenuation noncalcified: 0.25 [CI: 0.13-0.37] vs. -0.01 [CI: -0.13 to 0.11] plaque volumes. On the external dataset, substantial improvement was observed in DL model performance after transfer learning, total: 0.62 [CI: 0.01-0.84] vs. 0.94 [CI: 0.87-0.97], noncalcified: 0.54 [CI: -0.04 to 0.80] vs. 0.93 [CI: 0.86-0.96], calcified: 0.91 [CI:0.85-0.95] vs. 0.95 [CI: 0.91-0.97], and low attenuation noncalcified 0.48 [CI: 0.18-0.69] vs. 0.86 [CI: 0.76-0.92].

CONCLUSIONS

Our open-source DL algorithm achieved excellent agreement with expert CAD segmentations. However, transfer learning may be required to achieve accurate segmentations in the case of different plaque characteristics or machinery.

KEY POINTS

• Deep learning 3D U-net model for coronary segmentation achieves comparable results with expert readers' volumetric plaque quantification. • Transfer learning may be needed to achieve similar results for other scanner and plaque characteristics. • The developed deep learning algorithm is open-source and may be implemented in any CT analysis software.

CAD-RADS may underestimate coronary plaque progression as detected by serial CT angiography

Aims

We wished to assess whether different clinical definitions of coronary artery disease (CAD) [segment stenosis and involvement score (SSS, SIS), Coronary Artery Disease—Reporting and Data System (CAD-RADS)] affect which patients are considered to progress and which risk factors affect progression.

Methods and results

We enrolled 115 subsequent patients (60.1 ± 9.6 years, 27% female) who underwent serial coronary computed tomography angiography (CTA) imaging with >1year between the two examinations. CAD was described using SSS, SIS, and CAD-RADS. Linear mixed models were used to investigate the effects of risk factors on the overall amount of CAD and the effect on annual progression rate of different definitions. Coronary plaque burdens were SSS 4.63 ± 4.06 vs. 5.67 ± 5.10, P < 0.001; SIS 3.43 ± 2.53 vs. 3.89 ± 2.65, P < 0.001; CAD-RADS 0:8.7% vs. 0.0% 1:44.3% vs. 40.9%, 2:34.8% vs. 40.9%, 3:7.0% vs. 9.6% 4:3.5% vs. 6.1% 5:1.7% vs. 2.6%, P < 0.001, at baseline and follow-up, respectively. Overall, 53.0%, 29.6%, and 28.7% of patients progressed over time based on SSS, SIS, and CAD-RADS, respectively. Of the patients who progressed based on SSS, only 54% showed changes in CAD-RADS. Smoking and diabetes increased the annual progression rate of SSS by 0.37/year and 0.38/year, respectively (both P < 0.05). Furthermore, each year increase in age raised SSS by 0.12 [confidence interval (CI) 0.05–0.20, P = 0.001] and SIS 0.10 (CI 0.06–0.15, P < 0.001), while female sex was associated with 2.86 lower SSS (CI −4.52 to −1.20, P < 0.001) and 1.68 SIS values (CI −2.65 to −0.77, P = 0.001).

Conclusion

CAD-RADS could not capture the progression of CAD in almost half of patients with serial CTA. Differences in CAD definitions may lead to significant differences in patients who are considered to progress, and which risk factors are considered to influence progression.