Machine learning from quantitative coronary computed tomography angiography predicts ischemia and impaired myocardial blood flow

Background

Atherosclerotic plaque characteristics influence the hemodynamic consequences of coronary lesions. This study sought to assess the performance of a machine learning (ML) score integrating coronary computed tomography angiography (CCTA)-based quantitative plaque features for the prediction of ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by [15O]H2O positron emission tomography (PET).

Methods

This post-hoc analysis of the PACIFIC (Prospective Comparison of Cardiac PET/CT, SPECT/CT Perfusion Imaging and CT Coronary Angiography With Invasive Coronary Angiography) trial included 208 patients with suspected coronary artery disease who underwent CCTA, [15O]H2O PET, and 3-vessel invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. A boosted ensemble ML algorithm (XGBoost) trained on data from the NXT (Analysis of Coronary Blood Flow using CT Angiography: Next Steps) trial was used to develop a ML score for the prediction of per-vessel ischemia (invasive FFR ≤0.80). The performance of the ML score was evaluated in 551 vessels from the PACIFIC trial for external validation. Thereafter, we assessed the discriminative ability of the ML score for per-vessel impaired hyperemic MBF (≤2.30 mL/min/g).

Results

In total, 138 (25.0%) vessels had ischemia and 195 (35.4%) vessels had impaired hyperemic MBF. CCTA-derived quantitative percent diameter stenosis and low-density noncalcified plaque (LDNCP) volume were higher in ischemic vessels compared with non-ischemic vessels (60.8% vs. 19.9%; and 42.3 mm3 vs. 9.1 mm3; both p<0.001). The ML score demonstrated a significantly higher area under the receiver-operating characteristic curve (AUC) for predicting ischemia (0.92, 95% confidence interval [CI] 0.89–0.94) compared with visual stenosis grade (0.84, 95% CI 0.80–0.87; p<0.001). Overall, quantitative percent diameter stenosis and LDNCP volume had greatest feature importance for ML, followed by percent area stenosis, minimum luminal diameter, and contrast density drop (Figure 1). An individualized explanation of ML ischemia prediction is shown in Figure 2. When applied for impaired MBF discrimination, the ML score exhibited an AUC of 0.82 (95% CI 0.78–0.85) and was superior to visual stenosis grade (AUC 0.76, 95% CI 0.72–0.80; p=0.03).

Conclusions

An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and abnormal MBF by PET, outperforming standard visual CCTA interpretation.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute, United States Performance of the ML scoreIndividual explanation of ML prediction

Pericoronary adipose tissue attenuation, low-attenuation plaque burden and 5-year risk of myocardial infarction

Introduction

Pericoronary adipose tissue (PCAT) attenuation has emerged as a surrogate marker of pericoronary inflammation. To date, no studies have compared the impact of pericoronary adipose tissue (PCAT) attenuation and quantitative plaque burden on cardiac outcomes.

Purpose

We aimed to establish the relative merits of these approaches to risk prediction and hypothesised that the combination of PCAT attenuation and quantitative plaque burden measures could provide additive and improved prediction of myocardial infarction in patients with stable chest pain.

Methods

In a post-hoc analysis of a randomized controlled trial, we investigated the association between the future risk of fatal or non-fatal myocardial infarction and PCAT attenuation measured from CT coronary angiography using multivariable Cox regression models including plaque burden, obstructive coronary disease and cardiac risk score (incorporating age, sex, diabetes, smoking, hypertension, hyperlipidaemia and family history of cardiovascular disease).

Results

In 1697 evaluable participants (mean age 58±10 years), there were 37 myocardial infarctions after a median follow-up of 4.7 [interquartile interval, 4.0–5.7] years. Median low-attenuation plaque burden was 4.20 [0–6.86] % and mean PCAT −76±8 Hounsfield units (HU).

PCAT attenuation of the right coronary artery (RCA) was predictive of myocardial infarction (hazard ratio [HR] 1.55, 95% CI 1.08–2.22; p=0.017, per 1 standard deviation increment) with an optimum threshold of −70.5 HU [Hazards ratio (HR) 2.45, 95% CI 1.2–4.9; p=0.01]. Univariable analysis also identified the burden of non-calcified, low-attenuation and calcified plaque as well as Agatston coronary calcium score, presence of obstructive coronary artery disease and cardiovascular risk score were predictors of myocardial infarction (Figure 1). In multivariable analysis, only the low-attenuation plaque burden (HR 1.80, 95% CI 1.16 to 2.81, p=0.011, per doubling) and PCAT-RCA (HR 1.47 95%1.02 to 2.13, p=0.040, per standard deviation increment) remained predictors of myocardial infarction (Figure 1).

In multivariable analysis, adding PCAT-RCA ≥-70.5 HU to low-attenuation plaque burden >4% (optimum threshold for future myocardial infarction; HR = 4.87, 95% CI 2.03–11.78; p<0.0001) led to improved prediction of future myocardial infarction (HR 11.7, 95% CI 3.3–40.9; p<0.0001); Figure 2. In ROC analysis, integration of PCAT-RCA attenuation and LAP burden, increased the prediction for myocardial infarction compared to LAP alone (ΔAUC=0.04; p=0.01).

Conclusion

CT coronary angiography defined PCAT attenuation and low-attenuation plaque have marked and additive predictive value for the risk of fatal or non-fatal myocardial infarction.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The Chief Scientist Office of the Scottish Government Health and Social Care Directorates, British Heart Foundation, National Institute of Health/National Heart, Lung, and Blood Institute grant

Deep learning-based plaque quantification from coronary computed tomography angiography: external validation and comparison with intravascular ultrasound

Background

Atherosclerotic plaque quantification from coronary computed tomography angiography (CTA) enables accurate assessment of coronary artery disease burden, progression, and prognosis. However, quantitative plaque analysis is time-consuming and requires high expertise. We sought to develop and externally validate an artificial intelligence (AI)-based deep learning (DL) approach for CTA-derived measures of plaque volume and stenosis severity. We compared the performance of DL to expert readers and the gold standard of intravascular ultrasound (IVUS).

Methods

This was a multicenter study of patients undergoing coronary CTA at 11 sites, with software-based quantitative plaque measurements performed at a per-lesion level by expert readers. AI-based plaque analysis was performed by a DL novel convolutional neural network which automatically segmented the coronary artery wall, lumen, and plaque for the computation of plaque volume and stenosis severity. Using expert measurements as ground truth, the DL algorithm was trained on 887 patients (4,686 lesions). Thereafter, the algorithm was applied to an independent test set of 221 patients (1,234 lesions), which included an external validation cohort of 171 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) trial as well as 50 patients who underwent IVUS within one month of CTA. We report the performance of AI-based plaque analysis in the independent test set.

Results

Within the external validation cohort, there was excellent agreement between DL and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0.876), noncalcified plaque volume (ICC 0.869), and percent diameter stenosis (ICC 0.850; all p<0.001). When compared with IVUS, there was excellent agreement for DL total plaque volume (ICC 0.945), total plaque burden (ICC 0.853), minimal luminal area (ICC 0.864), and percent area stenosis (ICC 0.805; all p<0.001); with strong correlation between DL and IVUS for total plaque volume (r=0.915; p<0.001; Figure). The average DL plaque analysis time was 20 seconds per patient, compared with 25–30 minutes taken by experts.

Conclusions

AI-based plaque quantification from coronary CTA using an externally validated DL approach enables rapid measurements of plaque volume and stenosis severity in close agreement with expert readers and IVUS.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart, Lung, and Blood Institute, United States

P6151Fully automated epicardial adipose tissue volume and density measured from non-contrast CT predict major adverse cardiovascular events in asymptomatic subjects

Background

Epicardial adipose tissue (EAT) volume and density has shown to correlate with standard markers of coronary artery disease (CAD) and may predict major adverse cardiovascular events (MACE).

Purpose

We aimed to evaluate the prognostic value of EAT volume and density measured by fully automated deep-learning software from non-contrast cardiac computed tomography (CT).

Methods

We assessed 2071 consecutive asymptomatic subjects (age 56±9 years, 59% male) from the EISNER (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) trial with long-term follow-up after coronary artery calcium (CAC) measurement. EAT volume and mean density were quantified using automated deep-learning software from non-contrast cardiac CT. MACE was defined as myocardial infarction (MI), cardiac death, late (>90 days) revascularization and acute coronary syndrome (ACS). EAT volume and density were systematically compared to CAC score and atherosclerotic cardiovascular disease (ASCVD) risk score using Cox proportional hazards regression for MACE prediction.

Results

At 14±3 years, 217 subjects suffered MACE. In age-and-gender-adjusted multivariate analysis, ASCVD risk score, CAC (two-fold increase) and EAT volume (two-fold increase) were associated with increased risk of suffering MACE [Hazard Ratio (HR) (95% CI): 1.03 (1.01–1.04); 1.25 (1.19–1.30); and 1.36 (1.08–1.70) respectively, p<0.01 for all] (Figure); the corresponding Harrell's C-statistic was 0.76. The area-under-the curve from receiver-operator characteristic analysis for MACE prediction increased significantly from 0.69 to 0.77 (p<0.0001) when EAT volume and CAC were added to the current clinical standard (ASCVD, family history and obesity measures BMI and BSA). Both in men and women, increase in EAT volume was associated with increased risk of MACE, with HR 1.14 (1.06–1.22), p<0.001 in men vs. 1.15 (1.01–1.31), p=0.03 in women, for each 20 cubic centimeter increase in volume. EAT density (HU) was independently inversely associated with MACE [HR: 0.96 (0.93–0.99), p=0.01].

MACE Prediction

Conclusions

EAT volume and density measurements improve prediction of MACE in asymptomatic populations over the current clinical standard. Fully automated EAT volume and density quantification by deep-learning from non-contrast cardiac CT can provide additional prognostic value for the asymptomatic patient.

Acknowledgement/Funding

1R01HL133616, Forschungsstiftung Medizin Universitätsklinikum Erlangen, grant from Dr Miriam and Sheldon G. Adelson Medical Research Foundation

High power wideband gyrotron backward wave oscillator operating towards the terahertz region

Experimental results are presented of the first successful gyrotron backward wave oscillator (gyro-BWO) with continuous frequency tuning near the low-terahertz region. A helically corrugated interaction region was used to allow efficient interaction over a wide frequency band at the second harmonic of the electron cyclotron frequency without parasitic output. The gyro-BWO generated a maximum output power of 12 kW when driven by a 40 kV, 1.5 A, annular-shaped large-orbit electron beam and achieved a frequency tuning band of 88-102.5 GHz by adjusting the cavity magnetic field. The performance of the gyro-BWO is consistent with 3D particle-in-cell numerical simulations.

Characterization of the DGAT1 gene in the New Zealand dairy population

Recently, DGAT1 was identified as the gene that underlies the QTL for bovine milk production on chromosome 14. This study investigated the effect of the reported polymorphism in three dairy breeds in New Zealand. Statistically significant results were identified for milk fat, milk protein, and volume for Jersey and Holstein-Friesian breeds, and only milk volume for Ayrshires. The average allele substitution effects were 2 to 3 kg of protein and 120 to 130 l milk for both the Jersey and Holstein-Friesian breeds. For milk fat, the average allele substitution effect was 6 kg for Holstein-Friesians and 3 kg for Jerseys. In all breeds, where the polymorphism increased milk fat yield, it decreased milk protein yield and milk volume.

Fatal lung abscess due to Lactobacillus casei ss rhamnosus

A fatal case of community acquired pneumonia due to Lactobacillus casei ss rhamnosus is reported. Clinicians should be aware of this type of pneumonia.