Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index

Assessment of perioperative cardiac risk using preoperative quantitative flow ratio in patients with coronary artery disease undergoing noncardiac surgery: a retrospective cohort study

Background

Quantitative flow ratio (QFR) is a novel diagnostic modality for the functional testing of coronary artery stenosis, but evidence concerning the postoperative prognostic implication of QFR in noncardiac surgery (NCS) of patients with coronary artery disease (CAD) is limited. The purpose of this study was to examine the role of QFR in perioperative risk prediction in patients with coronary heart disease.

Methods

This retrospective cohort study was conducted in The First Affiliated Hospital of Wenzhou Medical University between 2013 and 2022, and consecutively included patients with CAD who had undergone NCS <1 year after coronary angiography. The primary endpoint was major adverse cardiovascular events (MACEs), which were defined as a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, cardiopulmonary arrest, malignant ventricular arrhythmia (MVA), congestive heart failure, and revascularization. Univariate and multifactorial Cox regression was used to identify the independent risk factors for perioperative cardiovascular events and to construct new models. The area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare the newly constructed model with existing traditional models.

Results

Among the 929 participants enrolled (median age 68 years; 72.0% male), the primary endpoint was met in 67 (7.2%) patients within 30 days of follow-up. There was no significant difference in the incidence of the primary endpoint between patients with QFR <0.75 and those with "gray zone" lesions (0.75≤ QFR ≤0.8) (log-rank P=0.325). Patients with QFR <0.75 and those with "gray zone" lesions (0.75≤ QFR ≤0.8) had a higher incidence of primary endpoint events compared to patients with QFR >0.8. [QFR <0.75 vs. QFR >0.8: adjusted hazard ratio (HR) =20.70, P<0.001; 0.75≤ QFR ≤0.8 vs. QFR >0.8: HR =15.99, P<0.001]. The independent predictors of MACEs events within 30 days after NCS were albumin level [HR =0.92, 95% confidence interval (CI): 0.87-0.98; P=0.008], emergency surgery (HR =4.12, 95% CI: 1.66-10.23; P=0.002), and QFR ≤0.8 (HR =15.92, 95% CI: 5.96-42.51; P<0.001). In addition, adjusting the original Revised Cardiac Risk Index (RCRI) with QFR ≤0.8 as a risk factor significantly improved the risk stratification of postoperative adverse events, with the adjusted AUC rising from 0.574 to 0.740 (P<0.001).

Conclusions

QFR ≤0.8 could independently predict perioperative cardiovascular adverse events in patients with CAD undergoing NCS and improve the predictive value of original predictive index. Gray-zone lesions (0.75≤ QFR ≤0.8) should be actively treated.

Prognostic value of serial coronary computed tomography angiography-derived perivascular fat-attenuation index and plaque volume in patients with suspected coronary artery disease

AIMS

To investigate the prognostic value of serial coronary computed tomography angiography (CCTA) derived plaque information, fractional flow reserve (CT-FFR), and perivascular fat-attenuation index (FAI) on major adverse cardiac events (MACE) in patients with suspected coronary artery disease.

MATERIALS AND METHODS

A total of 252 patients who underwent serial CCTA between January 2018 and December 2021 and were followed until June 2022. MACE were recorded. The analysis indexes included percent diameter stenosis (%DS), lesion length, plaque volume, CT-FFR, and FAI, with an emphasis on their changes between the baseline and follow-up CCTAs. Multivariate regression analysis were employed to identify independent risk factors for MACE.

RESULTS

After a median follow-up of 48-month, MACE occurred in 32 patients (12.7%). Patients with MACE displayed more severe stenosis, longer lesions, and larger plaque volumes in both baseline and follow-up CCTAs compared with no-MACE patients (all P<0.05). Patients with MACE displayed more severe stenosis, longer lesion, and larger plaque volume in both baseline and follow-up CCTAs compared with no-MACE patients. In addition, MACE patients also showed lower CT-FFR and higher △CT-FFR. Although FAI was significantly higher in MACE patients at baseline CCTA, FAI was notably increased in MACE patients, and decreased in the no-MACE patients (all P<0.05). Logistic regression analysis showed that ΔFAI, %DS, and plaque volume were independent predictors of MACE, with ΔFAI being the most significant (OR: 16.725, P<0.000). A multivariable model showed a significantly improved C-index of 0.903 (95% confidence interval: 0.836-0.970) for MACE prediction, when compared with single index alone.

CONCLUSIONS

Serial CCTA-derived ΔFAI, %DS, and plaque volume are crucial independent predictors of MACE in patients with suspected coronary artery disease, highlighting the importance of CCTA in patient risk stratification and prognostic assessment.

A cloud-based medical device for predicting cardiac risk in suspected coronary artery disease: a rapid review and conceptual economic model

Background

The CaRi-Heart® device estimates risk of 8-year cardiac death, using a prognostic model, which includes perivascular fat attenuation index, atherosclerotic plaque burden and clinical risk factors.

Objectives

To provide an Early Value Assessment of the potential of CaRi-Heart Risk to be an effective and cost-effective adjunctive investigation for assessment of cardiac risk, in people with stable chest pain/suspected coronary artery disease, undergoing computed tomography coronary angiography. This assessment includes conceptual modelling which explores the structure and evidence about parameters required for model development, but not development of a full executable cost-effectiveness model.

Data sources

Twenty-four databases, including MEDLINE, MEDLINE In-Process and EMBASE, were searched from inception to October 2022.

Methods

Review methods followed published guidelines. Study quality was assessed using Prediction model Risk Of Bias ASsessment Tool. Results were summarised by research question: prognostic performance; prevalence of risk categories; clinical effects; costs of CaRi-Heart. Exploratory searches were conducted to inform conceptual cost-effectiveness modelling.

Results

The only included study indicated that CaRi-Heart Risk may be predictive of 8 years cardiac death. The hazard ratio, per unit increase in CaRi-Heart Risk, adjusted for smoking, hypercholesterolaemia, hypertension, diabetes mellitus, Duke index, presence of high-risk plaque features and epicardial adipose tissue volume, was 1.04 (95% confidence interval 1.03 to 1.06) in the model validation cohort. Based on Prediction model Risk Of Bias ASsessment Tool, this study was rated as having high risk of bias and high concerns regarding its applicability to the decision problem specified for this Early Value Assessment. We did not identify any studies that reported information about the clinical effects or costs of using CaRi-Heart to assess cardiac risk. Exploratory searches, conducted to inform the conceptual cost-effectiveness modelling, indicated that there is a deficiency with respect to evidence about the effects of changing existing treatments or introducing new treatments, based on assessment of cardiac risk (by any method), or on measures of vascular inflammation (e.g. fat attenuation index). A de novo conceptual decision-analytic model that could be used to inform an early assessment of the cost effectiveness of CaRi-Heart is described. A combination of a short-term diagnostic model component and a long-term model component that evaluates the downstream consequences is anticipated to capture the diagnosis and the progression of coronary artery disease.

Limitations

The rapid review methods and pragmatic additional searches used to inform this Early Value Assessment mean that, although areas of potential uncertainty have been described, we cannot definitively state where there are evidence gaps.

Conclusions

The evidence about the clinical utility of CaRi-Heart Risk is underdeveloped and has considerable limitations, both in terms of risk of bias and applicability to United Kingdom clinical practice. There is some evidence that CaRi-Heart Risk may be predictive of 8-year risk of cardiac death, for patients undergoing computed tomography coronary angiography for suspected coronary artery disease. However, whether and to what extent CaRi-Heart represents an improvement relative to current standard of care remains uncertain. The evaluation of the CaRi-Heart device is ongoing and currently available data are insufficient to fully inform the cost-effectiveness modelling.

Future work

A large (n = 15,000) ongoing study, NCT05169333, the Oxford risk factors and non-invasive imaging study, with an estimated completion date of February 2030, may address some of the uncertainties identified in this Early Value Assessment.

Study registration

This study is registered as PROSPERO CRD42022366496.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135672) and is published in full in Health Technology Assessment; Vol. 28, No. 31. See the NIHR Funding and Awards website for further award information.

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease

OBJECTIVE

To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD).

MATERIALS AND METHODS

The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic.

RESULTS

Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model.

CONCLUSION

Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Coronary Artery Tree Description and Lesion Evaluation (CatLet) score for functional evaluation of coronary stenosis: a comparison study with pressure wire fractional flow reserve

Background

Pressure wire fractional flow reserve (FFR) and its derivatives, such as quantitative flow ratio (QFR), computational pressure flow-derived FFR (caFFR), coronary angiography-derived FFR (FFRangio), and computed tomography-derived FFR (FFRCT), have been validated for identifying functionally significant stenosis and guiding revascularization strategy. The limitations of using these methods include the side effects of hyperemia-induced agents, additional costs, and vulnerability to microvascular resistance. FFR is related both to the degree of a stenotic coronary artery and to its subtended myocardial territory. Coronary Artery Tree Description and Lesion Evaluation (CatLet) score (also known as Hexu) is a product of the degree of a stenosis and the weighting of the affected coronary artery (myocardial territory). Hence, we hypothesized that the CatLet score could predict hemodynamically significant coronary stenosis.

Methods

We retrospectively enrolled consecutive patients with stable coronary artery disease. They attended Sichuan Science City Hospital with at least one lesion of 50-90% diameter stenosis in a coronary artery of 2 mm or larger. FFR measurement was obtained during invasive coronary angiography. The CatLet score was obtained by multiplying a fixed value of 2.0 (for non-occlusive lesions) and the weight of the affected coronary artery. The primary endpoint was the CatLet score's diagnostic accuracy in identifying hemodynamically significant coronary stenosis, with a pressure wire FFR of ≤0.80 being used as reference.

Results

We analyzed the FFR and CatLet scores from 206 vessels in 175 patients with stable coronary disease and intermediate coronary lesions. The per-vessel analysis revealed an overall good correlation between the CatLet score and the FFR [r=-0.61; 95% confidence interval (95% CI): -0.69 to -0.52; P<0.01]. We also noted a significant CatLet score-FFR correlation for each of the left anterior descending artery (LAD), left circumflex (LCX), and right coronary artery (RCA). With a CatLet score ≥10 as a predictor of FFR ≤0.80, the overall diagnostic accuracy included a sensitivity of 78.80% (95% CI: 67.00-87.90%), a specificity of 85.00% (95% CI: 78.00-90.50%), a positive likelihood ratio of 5.25, a negative likelihood ratio of 0.25, and an area under the curve of 0.90 (95% CI: 0.85-0.94). Equivalent vessel-specific results were also achieved for each of the LAD, LCX, and RCA.

Conclusions

The CatLet score, solely based on visual estimation of the results of coronary angiography, demonstrated good diagnostic performance with respect to myocardial ischemia. Its clinical values in guiding revascularization warrant further investigation.

Relationship between peri-coronary inflammation and coronary vascular function in patients with suspected coronary artery disease

Background

In this study, we aim to investigate the relationship between the attenuation of peri-coronary adipose tissue (PCAT) in patients with suspected coronary artery disease (CAD) and the assessment of coronary vascular functions using coronary flow reserve (CFR).

Methods

We included 364 patients who underwent 13N-NH3 positron emission tomography/computed tomography and coronary computed tomography angiography (CCTA). We determined the relationship between fat attenuation index (FAI), PCAT volume, and other qualitative CT-derived anatomic parameters with CFR.

Results

We detected a decrease in CFR (<2.5) in 206 (57%) patients. At the patient level, those with reduced CFR showed a significantly higher prevalence of diffused atherosclerosis (41% vs. 23%; P < 0.001) and higher FAI (-75.5 HU vs. -77.1 HU; P = 0.014). In patients without obstructive CAD, FAI was significantly higher in those with reduced CFR (-75.5 HU vs. -77.7 HU, P = 0.026). On the vessel level, 1,092 vessels were analyzed, and 642 (59%) exhibited reduced CFR. The vessels with reduced CFR presented a significantly higher prevalence of obstructive CAD (37% vs. 26%; P < 0.001), diffused atherosclerosis (22% vs. 11%; P < 0.001), low-attenuation plaque (6% vs. 3%; P = 0.030), and positive remodeling (7% vs. 2%; P = 0.001). FAI was higher in vessels with reduced CFR (-80.8 HU vs. -81.8 HU; P = 0.045) than in normal CFR. In the patient-level analysis, obstructive CAD, diffused atherosclerosis, and FAI were independently linked with CFR. FAI was still associated with global CFR after adjusting for traditional risk factors (age, hypertension, diabetes, hyperlipidemia, and smoking). FAI remained independently associated with reduced CFR in patients without obstructive CAD.

Conclusions

Coronary perivascular inflammation evaluated by CCTA was independently associated with coronary vascular function. In patients without obstructive CAD, FAI was higher in the presence of reduced CFR. Altogether, FAI can help reveal microcirculatory damage in patients who do not exhibit epicardial artery stenosis.

Relationships between the coronary fat attenuation index for patients with heart-related disease measured automatically on coronary computed tomography angiography and coronary adverse events and degree of coronary stenosis

Background

Pericoronary artery coronary tissue (PACT) is a type of epicardial fat that can reflect the state of the coronary artery (inflammation, etc.). However, it cannot be reasonably and efficiently utilized in routine computed tomography (CT) examination. The aim of this study was to use artificial intelligence (AI) software to analyze coronary computed tomography angiography (CCTA) and measure the coronary perivascular fat attenuation index (FAI) of patients. The relationship between FAI and the occurrence of coronary adverse events and the degree of coronary stenosis were further analyzed.

Methods

This study involved patients who experienced CCTA in West China Hospital, Sichuan University, from January 2012 to December 2012. These patients were followed up to 2020 and classified according to the occurrence of coronary adverse events and the degree of stenosis of the lumen. For all patients, AI software was used to analyze the CCTA images of patients, and the FAI of 3 coronary arteries, the left anterior descending artery (LAD), the left circumflex artery (LCX), and the right coronary artery (RCA), was measured. Moreover, the relationship between FAI and patients with different degrees of coronary stenosis and adverse coronary events was determined.

Results

Comparisons between any 2 groups showed that the differences in the FAI among the 4 groups for the LAD were significant (all P values <0.05). There were no significant differences between the group with less-than-moderate stenosis (Mb) without adverse events and the group with moderate-or-above stenosis (M) with no adverse events for the LCX (P>0.05). For the remaining groups, FAI values exhibited statistically significant differences (P<0.05). According to the degree of lumen stenosis, the patients were divided into groups according to LAD, LCX, and RCA and the sum of the 3 vessels. There were significant differences in coronary FAI among the groups with different degrees of lumen stenosis for the sum of the 3 vessels, the LAD, and the LCX (P<0.05).

Conclusions

FAI can reflect the state of the coronary artery, which is related to inflammation of the coronary lumen. Moreover, there is a relationship between FAI and the degree of stenosis in the coronary lumen: the narrower the coronary lumen is, the higher the FAI around the lumen.

Perivascular adipose tissue as a source of therapeutic targets and clinical biomarkers

Obesity is a modifiable cardiovascular risk factor, but adipose tissue (AT) depots in humans are anatomically, histologically, and functionally heterogeneous. For example, visceral AT is a pro-atherogenic secretory AT depot, while subcutaneous AT represents a more classical energy storage depot. Perivascular adipose tissue (PVAT) regulates vascular biology via paracrine cross-talk signals. In this position paper, the state-of-the-art knowledge of various AT depots is reviewed providing a consensus definition of PVAT around the coronary arteries, as the AT surrounding the artery up to a distance from its outer wall equal to the luminal diameter of the artery. Special focus is given to the interactions between PVAT and the vascular wall that render PVAT a potential therapeutic target in cardiovascular diseases. This Clinical Consensus Statement also discusses the role of PVAT as a clinically relevant source of diagnostic and prognostic biomarkers of vascular function, which may guide precision medicine in atherosclerosis, hypertension, heart failure, and other cardiovascular diseases. In this article, its role as a 'biosensor' of vascular inflammation is highlighted with description of recent imaging technologies that visualize PVAT in clinical practice, allowing non-invasive quantification of coronary inflammation and the related residual cardiovascular inflammatory risk, guiding deployment of therapeutic interventions. Finally, the current and future clinical applicability of artificial intelligence and machine learning technologies is reviewed that integrate PVAT information into prognostic models to provide clinically meaningful information in primary and secondary prevention.

Identification of ischemia-causing lesions using coronary plaque quantification and changes in fractional flow reserve derived from computed tomography across the lesion

Background

This study sought to evaluate the association between coronary plaque characteristics, changes in the fractional flow reserve (FFR) derived from computed tomography across the lesion (ΔFFRCT), and lesion-specific ischemia using the FFR in patients with suspected or known coronary artery disease.

Methods

The study assessed coronary computed tomography (CT) angiography stenosis, plaque characteristics, ΔFFRCT, and FFR in 164 vessels of 144 patients. Obstructive stenosis was defined as stenosis ≥50%. An area under the receiver -operating characteristics curve (AUC) analysis was conducted to define the optimal thresholds for ΔFFRCT and the plaque variables. Ischemia was defined as a FFR of ≤0.80.

Results

The optimal cut-off value of ΔFFRCT was 0.14. Low-attenuation plaque (LAP) ≥76.23 mm3 and a percentage aggregate plaque volume (%APV) ≥28.91% can be used to predict ischemia independent of other plaque characteristics. The addition of LAP ≥76.23 mm3 and %APV ≥28.91% improved the discrimination (AUC, 0.742 vs. 0.649, P=0.001) and reclassification abilities [category-free net reclassification index (NRI), 0.339, P=0.027; relative integrated discrimination improvement (IDI) index, 0.093, P<0.001] of the assessments compared to the stenosis evaluation alone, and the addition of information about ΔFFRCT ≥0.14 further increased the discrimination (AUC, 0.828 vs. 0.742, P=0.004) and reclassification abilities (NRI, 1.029, P<0.001; relative IDI, 0.140, P<0.001) of the assessments.

Conclusions

The addition of the plaque assessment and ΔFFRCT to the stenosis assessments improved the identification of ischemia compared to the stenosis assessment alone.

Machine-learning-derived radiomics signature of pericoronary tissue in coronary CT angiography associates with functional ischemia

Objectives: To determine the association between radiomics signature (Rad-signature) of pericoronary tissue (PCT) in coronary computed tomography angiography (CCTA) and CT-derived fractional flow reserve (CT-FFR), and explore the influential factors of functional ischemia.

Methods: We retrospectively included 350 patients who underwent CCTA from 2 centers, consisting of the training (n = 134), validation (n = 66), and testing (with CCTA and invasive coronary angiography, n = 150) groups. After evaluating coronary stenosis level in CCTA (anatomical CT), pericoronary fat attenuation index (FAI), and CT-FFR, we extracted 1,691 radiomic features from PCT. By accumulating and weighting the most contributive features to functional ischemia (CT-FFR ≤ 0.8) the Rad-signature was established using Boruta integrating with a random forest algorithm. Another 45 patients who underwent CCTA and invasive FFR were included to assure the performance of Rad-signature.

Results: A total of 1046 vessels in 350 patients were analyzed, and functional ischemia was identified in 241/1046 (23.0%) vessels and 179/350 (51.1%) patients. From the 47 features highly relevant to functional ischemia, the top-8 contributive features were selected to establish Rad-signature. At the vessel level, the area under the curve (AUC) of Rad-signature to discriminate functional ischemia was 0.83, 0.82, and 0.82 in the training, validation, and testing groups, higher than 0.55, 0.55, and 0.52 of FAI (p &lt; 0.001), respectively, and was higher than 0.72 of anatomical CT in the testing group (p = 0.017). The AUC of the combined model (Rad-signature + anatomical CT) was 0.86, 0.85, and 0.83, respectively, significantly higher than that of anatomical CT and FAI (p &lt; 0.05). In the CCTA-invasive FFR group, using invasive FFR as the standard, the mean AUC of Rad-signature was 0.83 ± 0.02. At the patient level, multivariate logistic regression analysis showed that Rad-signature of left anterior descending (LAD) [odds ratio (OR) = 1.72; p = 0.012] and anatomical CT (OR = 3.53; p &lt; 0.001) were independent influential factors of functional ischemia (p &lt; 0.05). In the subgroup of nonobstructive (stenosis &lt;50% in invasive coronary angiography) and obstructive (≥50%) cases of the testing group, the independent factor of functional ischemia was FAI of LAD (OR = 1.10; p = 0.041) and Rad-signature of LAD (OR = 2.45; p = 0.042), respectively.

Conclusion: The machine-learning-derived Rad-signature of PCT in CCTA demonstrates significant association with functional ischemia.