Diagnostic Performance of QFR for the Evaluation of Intermediate Coronary Artery Stenosis Confirmed by Fractional Flow Reserve

Accuracy of deep learning in the differential diagnosis of coronary artery stenosis: a systematic review and meta-analysis

BACKGROUND

In recent years, as deep learning has received widespread attention in the field of heart disease, some studies have explored the potential of deep learning based on coronary angiography (CAG) or coronary CT angiography (CCTA) images in detecting the extent of coronary artery stenosis. However, there is still a lack of a systematic understanding of its diagnostic accuracy, impeding the advancement of intelligent diagnosis of coronary artery stenosis. Therefore, we conducted this study to review the accuracy of image-based deep learning in detecting coronary artery stenosis.

METHODS

We retrieved PubMed, Cochrane, Embase, and Web of Science until April 11, 2023. The risk of bias in the included studies was appraised using the QUADAS-2 tool. We extracted the accuracy of deep learning in the test set and performed subgroup analyses by binary and multiclass classification scenarios. We performed a subgroup analysis based on different degrees of stenosis and applied a double arcsine transformation to process the data. The analysis was done by using R.

RESULTS

Our systematic review finally included 18 studies, involving 3568 patients and 13,362 images. In the included studies, deep learning models were constructed based on CAG and CCTA. In binary classification tasks, the accuracy for detecting > 25%, > 50% and > 70% degrees of stenosis at the vessel level were 0.81 (95% CI: 0.71-0.85), 0.73 (95% CI: 0.58-0.88) and 0.61 (95% CI: 0.56-0.65), respectively. In multiclass classification tasks, the accuracy for detecting 0-25%, 25-50%, 50-70%, and 70-100% degrees of stenosis at the vessel level were 0.78 (95% CI: 0.73-0.84), 0.86 (95% CI: 0.78-0.93), 0.83 (95% CI: 0.70-0.97), and 0.70 (95% CI: 0.42-0.98), respectively.

CONCLUSIONS

Our study shows that deep learning models based on CAG and CCTA appear to be relatively accurate in diagnosing different degrees of coronary artery stenosis. However, for various degrees of stenosis, their accuracy still needs to be further improved.

Quantitative flow ratio or angiography for the assessment of non-culprit lesions in acute coronary syndromes, a randomized trial

BACKGROUND

Patients undergoing percutaneous coronary intervention for acute coronary syndromes often have multivessel disease (MVD). Quantitative flow ratio (QFR) is an angiography-based technology that may help quantify the functional significance of non-culprit lesions, with the advantage that measurements are possible also once the patient is discharged from the catheterization laboratory.

AIM

Our two-center, randomized superiority trial aimed to test whether QFR, as compared to angiography, modifies the rate of non-culprit lesion interventions (primary functional endpoint) and improves the outcomes of patients with acute coronary syndromes and MVD (primary clinical endpoint).

METHODS

In total, 202 consecutive patients (64 [56-71] years of age, 160 men) with STEMI (n = 69 (34%)), NSTEMI (n = 94 (47%)), or unstable angina (n = 39 (19%)) and MVD who had undergone successful treatment of all culprit lesions were randomized 1:1 to angiography- vs. QFR-guided delayed revascularization of 246 non-culprit stenoses (1.2/patient).

RESULTS

The proportion of patients assigned to percutaneous intervention was not different between groups (angiography group: 45 (45%) vs. QFR: 56 (55%), P = 0.125; relative risk = 0.80 (0.60-1.06)). At 12 months, a primary clinical endpoint event (composite of death, nonfatal myocardial infarction, revascularization, and significant angina) occurred in 24 patients (angiography-guided) and 23 patients (QFR-guided; P = 0.637, HR = 1.16 [0.63-2.15]). None of its components was different between groups.

DISCUSSION

QFR guidance based on analysis of images from the primary intervention was not associated with a difference in the rate of non-culprit lesion staged revascularization nor in the 12-month incidence of clinical events in patients with acute coronary syndromes and multivessel disease.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov Registry (NCT04808310).

Validity and Diagnostic Performance of Computing Fractional Flow Reserve From 2-Dimensional Coronary Angiography Images

BACKGROUND

Measurement of fractional flow reserve (FFR) is the gold standard for determining the physiologic significance of coronary artery stenosis, but newer software programs can calculate the FFR from 2-dimensional angiography images.

METHODS

A retrospective analysis was conducted using the records of patients with intermediate coronary stenoses who had undergone adenosine FFR (aFFR). To calculate the computed FFR, a software program used simulated coronary blood flow using computational geometry constructed using at least 2 patient-specific angiographic images. Two cardiologists reviewed the angiograms and determined the computational FFR independently. Intraobserver variability was measured using κ analysis and the intraclass correlation coefficient. The correlation coefficient and Bland-Altman plots were used to assess the agreement between the calculated FFR and the aFFR.

RESULTS

A total of 146 patients were included, with 95 men and 51 women, with a mean (SD) age of 61.1 (9.5) y. The mean (SD) aFFR was 0.847 (0.072), and 41 patients (27.0%) had an aFFR of 0.80 or less. There was a strong intraobserver correlation between the computational FFRs (r = 0.808; P < .001; κ = 0.806; P < .001). There was also a strong correlation between aFFR and computational FFR (r = 0.820; P < .001) and good agreement on the Bland-Altman plot. The computational FFR had a high sensitivity (95.1%) and specificity (90.1%) for detecting an aFFR of 0.80 or less.

CONCLUSION

A novel software program provides a feasible method of calculating FFR from coronary angiography images without resorting to pharmacologically induced hyperemia.

Diagnostic comparison of automatic and manual TIMI frame-counting-generated quantitative flow ratio (QFR) values

Quantitative flow ratio (QFR) is a computational measurement of FFR (fractional flow reserve), calculated from coronary angiography. Latest QFR software automates TIMI frame counting (TFC), which occurs during the flow step of QFR analyses, making the analysis faster and more reproducible. The objective is to determine the diagnostic performance of QFR values obtained from analyses using automatic TFC compared to those obtained from analyses using manual TFC. This was a single-arm clinical trial that used the prospective analysis of the coronary angiographic image series of 97 patients who underwent evaluation of stable coronary artery disease with FFR/iFR at MedStar Washington Hospital Center in Washington, DC, USA. Automatic and manual TFC QFR values were obtained from the analyses of each of the 97 patients' image series, with manual TFC QFR values as the current gold standard for comparison. The diagnostic performance of automatic TFC QFR values was measured as follows: sensitivity was 0.87 (95% CI 0.66-0.97) and specificity was 1.00 (95% CI 0.9514-1.00), positive predictive value (PPV) was 1.00 (95%CI 1.00-1.00), while the NPV was 0.96 (95% CI 0.96-0.99). Overall accuracy was 96.91% (95% CI 91.23%-99.36%). The agreement as illustrated by the Bland-Altman plot shows a bias of 0.0023 (SD 0.0208) and narrow limits of agreement (LOA): Upper LOA 0.0573 and Lower LOA - 0.0528. The area under curve (AUC) was 0.996. QFR values generated from automatic TFC are comparable to those generated from manual TFC in diagnostic capability. The most recent software update produces values equivalent to those of the previous manual option, and can therefore be used interchangeably.

Quantitative Flow Ratio or Angiography for the Assessment of Non-culprit Lesions in Acute Coronary Syndromes: Protocol of the Randomized Trial QUOMODO

Background

Approximately 50% of the patients undergoing percutaneous coronary intervention (PCI) for acute coronary syndromes (ACS) have additional stenotic lesions in non-infarct-related coronary arteries. The decision whether these stenoses require further treatment is routinely based on angiography alone. The quantitative flow ratio (QFR) is a simple non-invasive method that may help quantify the functional significance of these intermediate coronary artery lesions. The aim of our single-center, randomized superiority trial is to test the impact and efficacy of QFR, as compared to angiography, in the treatment of patients with ACS with multivessel coronary artery disease. Primary goal of the study is to investigate 1. The impact of QFR on the proportion of patients receiving PCI vs. conservative therapy and 2. whether QFR improves angina pectoris and overall cardiovascular outcomes.

Methods and Analysis

After treatment of the culprit lesion(s), a total of 200 consecutive ACS patients will be randomized 1:1 to angiography- vs. QFR-guided revascularization of non-culprit stenoses. Patients and clinicians responsible are blinded to the randomization group. The primary functional endpoint is defined as the proportion of patients assigned to medical treatment in the two groups. The primary clinical endpoint is a composite of death, non-fatal myocardial infarction, revascularization and significant angina at 12 months. Secondary endpoints include changes in the SAQ subgroups, and clinical events at 3- and 12-month follow-up.

Discussion

This study is designed to investigate whether QFR-based decision-making is associated with a decrease in angina and an improved prognosis in patients with multivessel disease.

Trial Registration Number

ClinicalTrials.gov Registry (NCT04808310).

Fractional flow reserve, quantitative flow ratio, and instantaneous wave-free ratio: a comparison of the procedure-related dose of ionising radiation

INTRODUCTION

The development of interventional cardiology increases the number of invasive procedures which are inevitably associated with increased exposure to ionizing radiation and associated risks. A percutaneous coronary intervention (PCI) substantiated by evaluation of the coronary artery lesion's functional significance is recommended by both European and American cardiologists. Nevertheless, the prevalence of physiology-guided PCIs does not exceed 10% all over the globe.

AIM

To identify the physiology evaluation method which is associated with the lowest exposure to ionising radiation.

MATERIAL AND METHODS

Anonymised data of 421 patients with stable angina pectoris for whom elective coronary artery angiography followed by physiological assessment of intermediate coronary artery stenosis was performed were prospectively included in this study. Only diagnostic-procedure-related data of dose of ionizing radiation were analysed. Physiological assessment of coronary artery lesions was performed by fractional flow reserve (FFR), quantitative flow ratio (QFR), or instantaneous wave-free ratio (iFR).

RESULTS

Compared to FFR as a reference, fluoroscopy time (FT) was almost half in QFR and almost double in iFR, p < 0.001. QFR was associated with more than 3 times shorter FT compared to iFR. The dose area product was 663.87 ±260.51 cGy/cm2 (p = 0.03) lower in QFR compared to iFR.

CONCLUSIONS

QFR is associated with significantly reduced exposure to ionising radiation compared to both FFR and iFR. Therefore, wider QFR application in clinical practice could eliminate any additional exposure to ionising radiation and increase the prevalence of physiology-guided coronary artery revascularization.

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information.