Fractional flow reserve as the standard of reference: All that glistens is not gold

Updated Strategies in Non-Culprit Stenosis Management of Multivessel Coronary Disease-A Contemporary Review

The prevalence of multivessel coronary artery disease (CAD) in acute coronary syndrome (ACS) patients underscores the need for optimal revascularization strategies. The ongoing debate surrounding percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), hybrid interventions, or medical-only management adds complexity to decision-making, particularly in specific angiographic scenarios. The article critically reviews existing literature, providing evidence-based perspectives on non-culprit lesion revascularization in ACS. Emphasis is placed on nuances such as the selection of revascularization methods, optimal timing for interventions, and the importance of achieving completeness in revascularization. The debate between culprit-only revascularization and complete revascularization is explored in detail, focusing on ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI), including patients with cardiogenic shock. Myocardial revascularization guidelines and recent clinical trials support complete revascularization strategies, either during the index primary PCI or within a short timeframe following the culprit lesion PCI (in both STEMI and NSTEMI). The article also addresses the complexities of decision-making in NSTEMI patients with multivessel CAD, advocating for immediate multivessel PCI unless complex coronary lesions require a staged revascularization approach. Finally, the article provided contemporary data on chronic total occlusion revascularization in ACS patients, highlighting the prognostic impact. In conclusion, the article addresses the evolving challenges of managing multivessel CAD in ACS patients, enhancing thoughtful integration into the clinical practice of recent data. We provided evidence-based, individualized approaches to optimize short- and long-term outcomes. The ongoing refinement of clinical and interventional strategies for non-culprit lesion management remains dynamic, necessitating careful consideration of patient characteristics, coronary stenosis complexity, and clinical context.

Machine learning to predict hemodynamically significant CAD based on traditional risk factors, coronary artery calcium and epicardial fat volume

We sought to establish an explainable machine learning (ML) model to screen for hemodynamically significant coronary artery disease (CAD) based on traditional risk factors, coronary artery calcium (CAC) and epicardial fat volume (EFV) measured from non-contrast CT scans. 184 symptomatic inpatients who underwent Single Photon Emission Computed Tomography/Myocardial Perfusion Imaging (SPECT/MPI) and Invasive Coronary Angiography (ICA) were enrolled. Clinical and imaging features (CAC and EFV) were collected. Hemodynamically significant CAD was defined when coronary stenosis severity ≥ 50% with a matched reversible perfusion defect in SPECT/MPI. Data was randomly split into a training cohort (70%) on which five-fold cross-validation was done and a test cohort (30%). The normalized training phase was preceded by the selection of features using recursive feature elimination (RFE). Three ML classifiers (LR, SVM, and XGBoost) were used to construct and choose the best predictive model for hemodynamically significant CAD. An explainable approach based on ML and the SHapley Additive exPlanations (SHAP) method was deployed to generate individual explanation of the model's decision. In the training cohort, hemodynamically significant CAD patients had significantly higher age, BMI and EFV, higher proportions of hypertension and CAC comparing with controls (P all < .05). In the test cohorts, hemodynamically significant CAD had significantly higher EFV and higher proportion of CAC. EFV, CAC, diabetes mellitus (DM), hypertension, and hyperlipidemia were the highest ranking features by RFE. XGBoost produced better performance (AUC of 0.88) compared with traditional LR model (AUC of 0.82) and SVM (AUC of 0.82) in the training cohort. Decision Curve Analysis (DCA) demonstrated that XGBoost model had the highest Net Benefit index. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 0.89, 68.0%, 96.8%, 94.4%, 79.0% and 83.9% in the XGBoost model. A XGBoost model based on EFV, CAC, hypertension, DM and hyperlipidemia to assess hemodynamically significant CAD was constructed and validated, which showed favorable predictive value. ML combined with SHAP can offer a transparent explanation of personalized risk prediction, enabling physicians to gain an intuitive understanding of the impact of key features in the model.

Angiographic Lesion Morphology Provides Incremental Value to Generalize Quantitative Flow Ratio for Predicting Myocardial Ischemia

Aim

The quantitative flow ratio (QFR) is favorable for functional assessment of coronary artery stenosis without pressure wires and induction of hyperemia. The aim of this study was to explore whether angiographic lesion morphology provides incremental value to generalize QFR for predicting myocardial ischemia in unselected patients.

Methods

This study was a substudy to the CT-FFR CHINA trial, referring 345 participants from five centers with suspected coronary artery disease on coronary CT angiography for diagnostic invasive coronary angiography (ICA). Fractional flow reserve (FFR) was measured in all vessels with 30–90% diameter stenosis. QFR was calculated in 186 lesions from 159 participants in a blinded manner. In addition, parameters to characterize lesion features were recorded or measured, including left anterior descending arteries (LADs)-involved lesions, side branch located at stenotic lesion (BL), multiple lesions (ML), minimal lumen diameter (MLD), reference lumen diameter (RLD), percent diameter stenosis (%DS), lesion length (LL), and LL/MLD4. Logistic regression was used to construct two kinds of models by combining single or two lesion parameters with the QFR. The performances of these models were compared with that of QFR on a per-vessel level.

Results

A total of 148 participants (mean age: 59.5 years; 101 men) with 175 coronary arteries were included for final analysis. In total, 81 (46%) vessels were considered hemodynamically significant. QFR correctly classified 82.29% of the vessels using FFR with a cutoff of 0.80 as reference standard. The area under the receiver operating characteristic curve (AUC) of QFR was 0.86 with a sensitivity, specificity, positive predictive value, and negative predictive value of 80.25, 84.04, 81.25, and 83.16%, respectively. The combined models (QFR + LAD + MLD, QFR + LAD + %DS, QFR + BL + MLD, and QFR + BL + %DS) outperformed QFR with higher AUCs (0.91 vs. 0.86, P = 0.02; 0.91 vs. 0.86, P = 0.02; 0.91 vs. 0.86, P = 0.02; 0.90 vs. 0.86, P = 0.03, respectively). Compared with QFR, the sensitivity of the combined models (QFR + BL and QFR + MLD) was improved (91.36 vs. 80.25%, 91.36 vs. 80.25%, respectively, both P &lt; 0.05) without compromised specificity or accuracy.

Conclusion

Combined with angiographic lesion parameters, QFR can be optimized for predicting myocardial ischemia in unselected patients.

Diagnostic Performance of Fractional Flow Reserve From CT Coronary Angiography With Analytical Method

The aim of this study was to evaluate a new analytical method for calculating non-invasive fractional flow reserve (FFR_AM) to diagnose ischemic coronary lesions. Patients with suspected or known coronary artery disease (CAD) who underwent computed tomography coronary angiography (CTCA) and invasive coronary angiography (ICA) with FFR measurements from two sites were prospectively recruited. Obstructive CAD was defined as diameter stenosis (DS) ≥50% on CTCA or ICA. FFR_AM was derived from CTCA images and anatomical features using analytical method and was compared with computational fluid dynamics (CFD)-based FFR (FFR_B) and invasive ICA-based FFR. FFR_AM, FFR_B, and invasive FFR ≤ 0.80 defined ischemia. A total of 108 participants (mean age 60, range: 30–83 years, 75% men) with 169 stenosed coronary arteries were analyzed. The per-vessel accuracy, sensitivity, specificity, and positive predictive and negative predictive values were, respectively, 81, 75, 86, 81, and 82% for FFR_AM and 87, 88, 86, 83, and 90% for FFR_B. The area under the receiver operating characteristics curve for FFR_AM (0.89 and 0.87) and FFR_B (0.90 and 0.86) were higher than both CTCA- and ICA-derived DS (all p < 0.0001) on per-vessel and per-patient bases for discriminating ischemic lesions. The computational time for FFR_AM was much shorter than FFR_B (2.2 ± 0.9 min vs. 48 ± 36 min, excluding image acquisition and segmentation). FFR_AM calculated from a novel and expeditious non-CFD approach possesses a comparable diagnostic performance to CFD-derived FFR_B, with a significantly shorter computational time.

Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2020: positron emission tomography, computed tomography, and magnetic resonance

Although the year 2020 was different from other years in many respects, the Journal of Nuclear Cardiology published excellent articles pertaining to imaging in patients with cardiovascular disease due to the dedication of the investigators in our field all over the world. In this review, we will summarize a selection of these articles to provide a concise review of the main advancements that have recently occurred in the field and provide the reader with an opportunity to review a wide selection of articles. We will focus on publications dealing with positron emission tomography, computed tomography, and magnetic resonance and hope that you will find this review helpful.