Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging.

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

Diagnostic cutoff for pressure drop coefficient in relation to fractional flow reserve and coronary flow reserve: A patient-level analysis.

OBJECTIVES AND BACKGROUND: Functional assessment of intermediate coronary stenosis during cardiac catheterization is conducted using diagnostic parameters like fractional flow reserve (FFR), coronary flow reserve (CFR), hyperemic stenosis resistance index (HSR), and hyperemic microvascular resistance (HMR). CDP (ratio of pressure drop across a stenosis to distal dynamic pressure), a nondimensional index derived from fundamental fluid dynamic principles, based on a combination of intracoronary pressure, and flow measurements may improve the functional assessment of coronary lesion severity. METHODS: Patient-level data pertaining to 350 intracoronary pressure and flow measurements across coronary stenoses was assessed to evaluate CFR, FFR, HSR, HMR, and CDP. CDP was calculated as (ΔP)/(0.5 × ρ × APV(2)). The density of blood (ρ) was assumed to be 1.05 g/cm(3). The correlation of current diagnostic parameters (CFR, FFR, HSR, and HMR) with CDP was evaluated. The receiver operating characteristic (ROC) curve was used to identify the optimal cut-off point of CDP, corresponding to the clinically used cut-off values (FFR = 0.80 and CFR = 2.0). RESULTS: CDP correlated significantly with FFR (r = 0.81, P < 0.05) and had significant diagnostic efficiency (ROC-area under curve of 86%), specificity (72%) and sensitivity (85%) at FFR < 0.8. The corresponding cut-off value for CDP to detect FFR < 0.8 was at CDP>25.4. CDP also correlated significantly (r = 0.98, P < 0.05) with epicardial-specific parameter, HSR. CONCLUSIONS: CDP, a functional parameter based on both intracoronary pressure and flow measurements, has close agreement (area under ROC curve = 86%) with FFR, the frequently used method of evaluating stenosis severity.

Functional diagnosis of coronary stenoses using pressure drop coefficient: a pilot study in humans.

OBJECTIVES AND BACKGROUND: Myocardial fractional flow reserve (FFR) in conjunction with coronary flow reserve (CFR) is used to evaluate the hemodynamic severity of coronary lesions. However, discordant results between FFR and CFR have been observed in intermediate coronary lesions. A functional parameter, pressure drop coefficient (CDP; ratio of pressure drop to distal dynamic pressure), was assessed using intracoronary pressure drop (dp) and average peak velocity (APV). The CDP is a nondimensional ratio, derived from fundamental fluid dynamic principles. We sought to evaluate the correlation of CDP with FFR, CFR, and hyperemic stenosis resistance (HSR: ratio of pressure drop to APV) in human subjects. METHODS: Twenty-seven patients with reversible perfusion defects based on SPECT were consented for the study before cardiac catheterization. Distal coronary pressure and APV were measured simultaneously for each coronary lesion using a Combowire(©) during cardiac catheterization. Reference diameter, minimal lumen diameter, and %AS were obtained by quantitative coronary angiography. Maximum hyperemia was induced by IV adenosine (140 µg/kg/min). CDP was calculated as, (Δp)/(0.5 × ρ × APV(2) ). The density of blood (ρ) was assumed to be 1.05 gm/cm(3) . RESULTS: The functional index, CDP, when correlated simultaneously with FFR and CFR, was found to have a significant correlation (r = 0.61; P < 0.05). Similarly a significant correlation was achieved when CDP was correlated with HSR (r = 0.91; P < 0.001). This is consistent with the definition of CDP, which is a functional parameter that includes both pressure and flow information. CONCLUSIONS: CDP, a nondimensional parameter combining simultaneous measurements of pressure drop and velocity data, can accurately define the severity of coronary stenoses and could prove advantageous clinically.

Effect of myocardial contractility on hemodynamic end points under concomitant microvascular disease in a porcine model.

In this study, coronary diagnostic parameters, pressure drop coefficient (CDP: ratio of trans-stenotic pressure drop to distal dynamic pressure), and lesion flow coefficient (LFC: ratio of % area stenosis (%AS) to the CDP at throat region), were evaluated to distinguish levels of %AS under varying contractility conditions, in the presence of microvascular disease (MVD). In 10 pigs, %AS and MVD were created using angioplasty balloons and 90-µm microspheres, respectively. Simultaneous measurements of pressure drop, left ventricular pressure (p), and velocity were obtained. Contractility was calculated as (dp/dt)max, categorized into low contractility <900 mmHg/s and high contractility >900 mmHg/s, and in each group, compared between %AS <50 and >50 using analysis of variance. In the presence of MVD, between the %AS <50 and >50 groups, values of CDP (71 ± 1.4 and 121 ± 1.3) and LFC (0.10 ± 0.04 and 0.19 ± 0.04) were significantly different (P < 0.05), under low-contractility conditions. A similar %AS trend was observed under high-contractility conditions (CDP: 18 ± 1.4 and 91 ± 1.4; LFC: 0.08 ± 0.04 and 0.25 ± 0.04). Under MVD conditions, similar to fractional flow reserve, CDP and LFC were not influenced by contractility.

Association Between Intracoronary Imaging During PCI and Clinical Outcomes in a Real-World US Medicare Population.

Background: Use of intravascular ultrasound (IVUS) or optical coherence tomography (OCT) during percutaneous coronary intervention (PCI) is endorsed by society guidelines, but US data on real-world outcomes are lacking. Methods: Medicare claims data were identified for inpatient PCIs performed October 2015 to March 2020, with IVUS/OCT captured by ICD-10-PCS codes. Three-way propensity score matching (angio vs IVUS vs OCT) on baseline and procedural characteristics was performed. Major adverse cardiovascular events (MACE), a composite of death, myocardial infarction (MI), or repeat revascularization, was evaluated through 3 years, with a 30-day blanking window after index PCI to exclude staged procedures. Results: Of the 502,821 PCI procedures, 463,201 (92%) were guided by angiography alone, with IVUS or OCT used in 37,908 (7.5%) and 1712 (0.3%), respectively. After propensity matching, compared with angiography, the risk of major adverse cardiovascular event was similar for IVUS (hazard ratio [HR], 0.97; 95% CI, 0.91-1.03; P = .285) but lower for OCT (HR, 0.85; 95% CI, 0.77-0.94; P = .001). A similar trend was observed in clinically relevant subgroups. Compared with angiography alone, the risk of MI or repeat revascularization was lower with OCT (HR, 0.86; 95% CI, 0.76-0.97; P = .015), and the risk of MI alone was lower with IVUS (HR, 0.90; 95% CI, 0.82-0.99; P = .038). Conclusions: In a real-world US cohort, IVUS and OCT were used infrequently during PCI. Compared with angiography alone, use of intracoronary imaging during index PCI was associated with lower rates of clinical events through 3 years.

Improved Functional Assessment of Ischemic Severity Using 3D Printed Models.

Objective: To develop a novel in vitro method for evaluating coronary artery ischemia using a combination of non-invasive coronary CT angiograms (CCTA) and 3D printing (FFR3D). Methods: Twenty eight patients with varying degrees of coronary artery disease who underwent non-invasive CCTA scans and invasive fractional flow reserve (FFR) of their epicardial coronary arteries were included in this study. Coronary arteries were segmented and reconstructed from CCTA scans using Mimics (Materialize). The segmented models were then 3D printed using a Carbon M1 3D printer with urethane methacrylate (UMA) family of rigid resins. Physiological coronary circulation was modeled in vitro as flow-dependent stenosis resistance in series with variable downstream resistance. A range of physiological flow rates (Q) were applied using a peristaltic steady flow pump and titrated with a flow sensor. The pressure drop (ΔP) and the pressure ratio (Pd/Pa) were assessed for patient-specific aortic pressure (Pa) and differing flow rates (Q) to evaluate FFR3D using the 3D printed model. Results: There was a good positive correlation (r = 0.87, p < 0.0001) between FFR3D and invasive FFR. Bland-Altman analysis revealed a good concordance between the FFR3D and invasive FFR values with a mean bias of 0.02 (limits of agreement: -0.14 to 0.18; p = 0.2). Conclusions: 3D printed patient-specific models can be used in a non-invasive in vitro environment to quantify coronary artery ischemia with good correlation and concordance to that of invasive FFR.

Influence of heart rate on fractional flow reserve, pressure drop coefficient, and lesion flow coefficient for epicardial coronary stenosis in a porcine model.

A limitation in the use of invasive coronary diagnostic indexes is that fluctuations in hemodynamic factors such as heart rate (HR), blood pressure, and contractility may alter resting or hyperemic flow measurements and may introduce uncertainties in the interpretation of these indexes. In this study, we focused on the effect of fluctuations in HR and area stenosis (AS) on diagnostic indexes. We hypothesized that the pressure drop coefficient (CDP(e), ratio of transstenotic pressure drop and distal dynamic pressure), lesion flow coefficient (LFC, square root of ratio of limiting value CDP and CDP at site of stenosis) derived from fluid dynamics principles, and fractional flow reserve (FFR, ratio of average distal and proximal pressures) are independent of HR and can significantly differentiate between the severity of stenosis. Cardiac catheterization was performed on 11 Yorkshire pigs. Simultaneous measurements of distal coronary arterial pressure and flow were performed using a dual sensor-tipped guidewire for HR < 120 and HR > 120 beats/min, in the presence of epicardial coronary lesions of <50% AS and >50% AS. The mean values of FFR, CDP(e), and LFC were significantly different (P < 0.05) for lesions of <50% AS and >50% AS (0.88 ± 0.04, 0.76 ± 0.04; 62 ± 30, 151 ± 35, and 0.10 ± 0.02 and 0.16 ± 0.01, respectively). The mean values of FFR and CDP(e) were not significantly different (P > 0.05) for variable HR conditions of HR < 120 and HR > 120 beats/min (FFR, 0.81 ± 0.04 and 0.82 ± 0.04; and CDP(e), 95 ± 33 and 118 ± 36). The mean values of LFC do somewhat vary with HR (0.14 ± 0.01 and 0.12 ± 0.02). In conclusion, fluctuations in HR have no significant influence on the measured values of CDP(e) and FFR but have a marginal influence on the measured values of LFC. However, all three parameters can significantly differentiate between stenosis severities. These results suggest that the diagnostic parameters can be potentially used in a better assessment of coronary stenosis severity under a clinical setting.

Effect of guidewire on contribution of loss due to momentum change and viscous loss to the translesional pressure drop across coronary artery stenosis: an analytical approach.

BACKGROUND: Guidewire (GW) size and stenosis dimensions are the two major factors affecting the translesional pressure drop. Studying the combined effect of these parameters on the mean pressure drop (Δp) across the stenosis is of high practical importance. METHODS: In this study, time averaged mass and momentum conservation equations are solved analytically to obtain pressure drop-flow, Δp-Q, curves for three different percentage area blockages corresponding to moderate (64%), intermediate (80%), and severe (90%) stenoses. Stenosis is considered to be axisymmetric consisting of three different sections namely converging, throat, and diverging regions. Analytical expressions for pressure drop are obtained for each of these regions separately. Using this approach, effects of lesion length and GW insertion on the mean translesional pressure drop and its component (loss due to momentum change and viscous loss) are analyzed. RESULTS AND CONCLUSION: It is observed that for a given percent area stenosis (AS), increase in the throat length only increases the viscous loss. However, increase in the severity of stenosis and GW insertion increase both loss due to momentum change and viscous loss. GW insertion has greater contribution to the rise in viscous loss (increase by 2.14 and 2.72 times for 64% and 90% AS, respectively) than loss due to momentum change (1.34% increase for 64% AS and 25% decrease for 90% AS). It also alters the hyperemic pressure drop in moderate (48% increase) to intermediate (30% increase) stenoses significantly. However, in severe stenoses GW insertion has a negligible effect (0.5% increase) on hyperemic translesional pressure drop. It is also observed that pressure drop in a severe stenosis is less sensitive to lesion length variation (4% and 14% increase in Δp for without and with GW, respectively) as compared to intermediate (10% and 30% increase in Δp for without and with GW, respectively) and moderate stenoses (22% and 48% increase in Δp for without and with GW, respectively). Based on the contribution of pressure drop components to the total translesional pressure drop, it is found that viscous losses are dominant in moderate stenoses, while in severe stenoses losses due to momentum changes are significant. It is also shown that this simple analytical solution can provide valuable information regarding interpretation of coronary diagnostic parameters such as fractional flow reserve (FFR).

Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches.

BACKGROUND: Machine learning (ML) is a computer algorithm used to identify patterns for prediction in various tasks, and ML methods have been beneficial for developing prediction models when applied to heterogeneous and large datasets. We aim to examine the prognostic ability of a ML-based prediction algorithm utilizing routine health checkup data to predict all-cause mortality (ACM) compared to established risk prediction approaches. METHODS: A total 86155 patients with seventy available parameters (35 clinical, 32 laboratory, and 3 coronary artery calcium score [CACS] parameters) were analyzed. ML involved feature selection, splitting data randomly into a training (70%) and test set (30%), and model building with a boosted ensemble algorithm. The developed ML model was validated in a separate cohort of 4915 patients. The performance of ML for predicting ACM was compared with the following models: (i) the Framingham risk score (FRS) + CACS, (ii) atherosclerotic cardiovascular disease (ASCVD) + CACS, with (iii) logistic regression (LR) model. RESULTS: In the derivation dataset, 690 patients died during the median 4.6-year follow-up (interquartile range, 3.0-6.6 years). The AUC value in the ML model was significantly higher than the other models in test set (ML: 0.82, FRS + CACS: 0.70, ASCVD + CACS: 0.74; LR model: 0.79, p < 0.05 for all), but not statistically significantly higher in validation set (ML: 0.78, FRS + CACS: 0.62, ASCVD + CACS: 0.72; LR model: 0.74, p: 0.572 and 0.625 for ASCVD + CACS and LR model, respectively). The ML model improved reclassification over the other models in low to intermediate risk patients (p < 0.001 for all). CONCLUSION: The prediction algorithm derived by ML methods showed a robust ability to predict ACM and improved reclassification over established conventional risk prediction approaches in asymptomatic population undergoing a health checkup.

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP.

Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System in order to elucidate the determinants of in-hospital mortality in patients undergoing percutaneous coronary intervention across New York State. Methods and Results We examined 479 804 patients undergoing percutaneous coronary intervention between 2004 and 2012, utilizing traditional and advanced machine learning algorithms to determine the most significant predictors of in-hospital mortality. The entire data were randomly split into a training (80%) and a testing set (20%). Tuned hyperparameters were used to generate a trained model while the performance of the model was independently evaluated on the testing set after plotting a receiver-operator characteristic curve and using the output measure of the area under the curve ( AUC ) and the associated 95% CIs. Mean age was 65.2±11.9 years and 68.5% were women. There were 2549 in-hospital deaths within the patient population. A boosted ensemble algorithm (AdaBoost) had optimal discrimination with AUC of 0.927 (95% CI 0.923-0.929) compared with AUC of 0.913 for XGB oost (95% CI 0.906-0.919, P=0.02), AUC of 0.892 for Random Forest (95% CI 0.889-0.896, P<0.01), and AUC of 0.908 for logistic regression (95% CI 0.907-0.910, P<0.01). The 2 most significant predictors were age and ejection fraction. Conclusions A big data approach that utilizes advanced machine learning algorithms identifies new associations among risk factors and provides high accuracy for the prediction of in-hospital mortality in patients undergoing percutaneous coronary intervention.

Image-Based Computational Fluid Dynamic Analysis for Surgical Planning of Sequential Grafts in Coronary Artery Bypass Grafting.

Coronary bypass grafting (CABG) is a surgical procedure for anastomosing small grafts to the coronary vessels. The bypass graft bridges the occluded or diseased coronary artery, allowing sufficient blood flow to deliver oxygen and nutrients to the heart muscles. Patient-specific (PS) anatomy obtained from coronary computed tomography angiography (CCTA) was used to generate a 3D aorto-coronary model (pre-surgery). Additionally, three more models with idealized grafts (individual and sequential grafts), were created using Boolean operations to represent post-surgery configuration. Fractional flow reserve (FFR) and wall shear stress (WSS) were estimated from the computational fluid dynamics (CFD). The pre-surgical FFR values for all the three left coronary arteries were significant (FFR<.80). The flow was restored (FFR>0.80) distal to stenosis in all the three post- surgical idealized graft models. Peak WSS values of 468, 336 and 295 dynes/cm2 were observed at the toe of the individual end-to-side anastomosis for the three graft models. More importantly, low WSS (< 100 dynes/cm2) prevails at the heel and the walls opposite to the anastomosis in the sequential graft models. The prevailing low WSS at the heel and the wall bed opposite to anastomosis, in a sequential graft model, reduces restenosis rates and promotes a uniform hemodynamic environment for a better long-term patency of the graft. PS- CFD simulations based on CCTA can be helpful in assessing the hemodynamic parameters of graft models for optimal surgical planning.

Machine learning in cardiac CT: Basic concepts and contemporary data.

Propelled by the synergy of the groundbreaking advancements in the ability to analyze high-dimensional datasets and the increasing availability of imaging and clinical data, machine learning (ML) is poised to transform the practice of cardiovascular medicine. Owing to the growing body of literature validating both the diagnostic performance as well as the prognostic implications of anatomic and physiologic findings, coronary computed tomography angiography (CCTA) is now a well-established non-invasive modality for the assessment of cardiovascular disease. ML has been increasingly utilized to optimize performance as well as extract data from CCTA as well as non-contrast enhanced cardiac CT scans. The purpose of this review is to describe the contemporary state of ML based algorithms applied to cardiac CT, as well as to provide clinicians with an understanding of its benefits and associated limitations.

Evaluation of lesion flow coefficient for the detection of coronary artery disease in patient groups from two academic medical centers.

BACKGROUND: In this study, lesion flow coefficient (LFC: ratio of % area stenosis [%AS] to the square root of the ratio of the pressure drop across the stenosis to the dynamic pressure in the throat region), that combines both the anatomical (%AS) and functional measurements (pressure and flow), was assessed for application in a clinical setting. METHODS AND RESULTS: Pressure, flow, and anatomical values were obtained from patients in 251 vessels from two different centers. Fractional flow reserve (FFR), Coronary flow reserve (CFR), hyperemic stenosis resistance index (HSR) and hyperemic microvascular index (HMR) were calculated. Anatomical data was corrected for the presence of guidewire and the LFC values were calculated. LFC was correlated with FFR, CFR, HSR, HMR, individually and in combination with %AS. The p<0.05 was used for statistical significance. LFC correlated significantly when the FFR (pressure-based), CFR (flow-based), and anatomical measure %AS were combined (r=0.64; p<0.05). Similarly, LFC correlated significantly when HSR, HMR, and %AS were combined (r=0.72; p<0.05). LFC was able to significantly (p<0.05) distinguish between the two concordant and the two discordant groups of FFR and CFR, corresponding to the clinically used cut-off values (FFR=0.80 and CFR=2.0). The LFC could also significantly (p<0.05) distinguish between the normal and abnormal microvasculature conditions in the presence of non-significant epicardial stenosis, while the comparison was borderline significant (p=0.09) in the presence of significant stenosis. CONCLUSION: LFC, a parameter that combines both the anatomical and functional end-points, has the potential for application in a clinical setting for CAD evaluation.

Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry.

INTRODUCTION: Machine learning (ML) is a field in computer science that demonstrated to effectively integrate clinical and imaging data for the creation of prognostic scores. The current study investigated whether a ML score, incorporating only the 16 segment coronary tree information derived from coronary computed tomography angiography (CCTA), provides enhanced risk stratification compared with current CCTA based risk scores. METHODS: From the multi-center CONFIRM registry, patients were included with complete CCTA risk score information and ≥3 year follow-up for myocardial infarction and death (primary endpoint). Patients with prior coronary artery disease were excluded. Conventional CCTA risk scores (conventional CCTA approach, segment involvement score, duke prognostic index, segment stenosis score, and the Leaman risk score) and a score created using ML were compared for the area under the receiver operating characteristic curve (AUC). Only 16 segment based coronary stenosis (0%, 1-24%, 25-49%, 50-69%, 70-99% and 100%) and composition (calcified, mixed and non-calcified plaque) were provided to the ML model. A boosted ensemble algorithm (extreme gradient boosting; XGBoost) was used and the entire data was randomly split into a training set (80%) and testing set (20%). First, tuned hyperparameters were used to generate a trained model from the training data set (80% of data). Second, the performance of this trained model was independently tested on the unseen test set (20% of data). RESULTS: In total, 8844 patients (mean age 58.0 ±â€¯11.5 years, 57.7% male) were included. During a mean follow-up time of 4.6 ±â€¯1.5 years, 609 events occurred (6.9%). No CAD was observed in 48.7% (3.5% event), non-obstructive CAD in 31.8% (6.8% event), and obstructive CAD in 19.5% (15.6% event). Discrimination of events as expressed by AUC was significantly better for the ML based approach (0.771) vs the other scores (ranging from 0.685 to 0.701), P < 0.001. Net reclassification improvement analysis showed that the improved risk stratification was the result of down-classification of risk among patients that did not experience events (non-events). CONCLUSION: A risk score created by a ML based algorithm, that utilizes standard 16 coronary segment stenosis and composition information derived from detailed CCTA reading, has greater prognostic accuracy than current CCTA integrated risk scores. These findings indicate that a ML based algorithm can improve the integration of CCTA derived plaque information to improve risk stratification.

Effect of Varying Hemodynamic and Vascular Conditions on Fractional Flow Reserve: An In Vitro Study.

BACKGROUND: The aim of this study was to investigate the impact of varying hemodynamic conditions on fractional flow reserve (ratio of pressure distal [Pd] and proximal [Pa] to stenosis under hyperemia) in an in vitro setting. Failure to achieve maximal hyperemia and the choice of hyperemic agents may have differential effects on coronary hemodynamics and, consequently, on the determination of fractional flow reserve. METHODS AND RESULTS: An in vitro flow system was developed to experimentally model the physiological coronary circulation as flow-dependent stenosis resistance in series with variable downstream resistance. Five idealized models with 30% to 70% diameter stenosis severity were fabricated using VeroClear rigid material in an Objet260 Connex printer. Mean aortic pressure was maintained at 7 levels (60-140 mm Hg) from hypotension to hypertension using a needle valve that mimicked adjustable microcirculatory resistance. A range of physiological flow rates was applied by a steady flow pump and titrated by a flow sensor. The pressure drop and the pressure ratio (Pd/Pa) were assessed for the 7 levels of aortic pressure and differing flow rates. The in vitro experimental data were coupled with pressure-flow relationships from clinical data for populations with and without myocardial infarction, respectively, to evaluate fractional flow reserve. The curve for pressure ratio and flow rate demonstrated a quadratic relationship with a decreasing slope. The absolute decrease in fractional flow reserve in the group without myocardial infarction (with myocardial infarction) was on the order of 0.03 (0.02), 0.05 (0.02), 0.07 (0.05), 0.17 (0.13) and 0.20 (0.24), respectively, for 30%, 40%, 50%, 60%, and 70% diameter stenosis, for an increase in aortic pressure from 60 to 140 mm Hg. CONCLUSIONS: The fractional flow reserve value, an index of physiological stenosis significance, was observed to decrease with increasing aortic pressure for a given stenosis in this idealized in vitro experiment for vascular groups with and without myocardial infarction.

Relationship Between Endothelial Wall Shear Stress and High-Risk Atherosclerotic Plaque Characteristics for Identification of Coronary Lesions That Cause Ischemia: A Direct Comparison With Fractional Flow Reserve.

BACKGROUND: Wall shear stress (WSS) is an established predictor of coronary atherosclerosis progression. Prior studies have reported that high WSS has been associated with high-risk atherosclerotic plaque characteristics (APCs). WSS and APCs are quantifiable by coronary computed tomography angiography, but the relationship of coronary lesion ischemia-evaluated by fractional flow reserve-to WSS and APCs has not been examined. METHODS AND RESULTS: WSS measures were obtained from 100 evaluable patients who underwent coronary computed tomography angiography and invasive coronary angiography with fractional flow reserve. Patients were categorized according to tertiles of mean WSS values defined as low, intermediate, and high. Coronary ischemia was defined as fractional flow reserve ≤0.80. Stenosis severity was determined by minimal luminal diameter. APCs were defined as positive remodeling, low attenuation plaque, and spotty calcification. The likelihood of having positive remodeling and low-attenuation plaque was greater in the high WSS group compared with the low WSS group after adjusting for minimal luminal diameter (odds ratio for positive remodeling: 2.54, 95% CI 1.12-5.77; odds ratio for low-attenuation plaque: 2.68, 95% CI 1.02-7.06; both P<0.05). No significant relationship was observed between WSS and fractional flow reserve when adjusting for either minimal luminal diameter or APCs. WSS displayed no incremental benefit above stenosis severity and APCs for detecting lesions that caused ischemia (area under the curve for stenosis and APCs: 0.87, 95% CI 0.81-0.93; area under the curve for stenosis, APCs, and WSS: 0.88, 95% CI 0.82-0.93; P=0.30 for difference). CONCLUSIONS: High WSS is associated with APCs independent of stenosis severity. WSS provided no added value beyond stenosis severity and APCs for detecting lesions with significant ischemia.

Benefit of ECG-gated rest and stress N-13 cardiac PET imaging for quantification of LVEF in ischemic patients.

BACKGROUND: ECG-gated rest-stress cardiac PET can lead to simultaneous quantification of both left ventricular ejection fraction and flow impairment. In this study, our aim was to assess the benefit of rest and stress PET ejection fraction (EF) (EFp) in relation to single-photon emission computed tomography (SPECT) EF (EFs) and echocardiography EF (EFe). To this effect, the EFp was compared with EFs and EFe. Further, the relation between rest and stress EFp was also assessed. METHODS: ECG-gated N-13 ammonia rest and stress PET imaging was performed in 26 patients. EFp values were obtained using gated reconstruction of the data in Flowquant. In 13 patients, EFs and EFe values were obtained through chart review. Correlation, analysis of variance, and Bland-Altman analyses were performed. P values less than 0.05 were used for statistical significance. RESULTS: The rest and stress EFp values correlated significantly (r=0.80 and 0.71, respectively; P<0.05) with EFs values. There was moderate correlation with statistical significance (P<0.05) between the rest and stress EFp and EFe values (r=0.58 and 0.50, respectively). The mean rest and stress EFp values were not significantly different from mean EFs values. Also, the rest EFp and stress EFp values correlated well (r=0.81, P<0.05) and were not significantly different. Bland-Altman analysis showed no significant bias between the rest and stress EFp, and EFs, and EFe values. CONCLUSION: Rest and stress EFp values obtained through an ECG-gated PET scan can be used for clinical diagnosis in place of conventional methods like SPECT and echocardiography.

Lesion flow coefficient: a combined anatomical and functional parameter for detection of coronary artery disease--a clinical study.

Invasive diagnosis of coronary artery disease utilizes either anatomical or functional measurements. In this study, we tested a futuristic parameter, lesion flow coefficient (LFC, defined as the ratio of percent coronary area stenosis (%AS) to the square root of the ratio of the pressure drop across the stenosis to the dynamic pressure in the throat region), that combines both the anatomical (%AS) and functional measurements (pressure and flow) for application in a clinical setting. In 51 vessels, simultaneous pressure and flow readings were obtained using a 0.014" Combowire (Volcano Corporation). Anatomical details were assessed using quantitative coronary angiography (QCA). Fractional flow reserve (FFR), coronary flow reserve (CFR), hyperemic stenosis resistance index (HSR), and hyperemic microvascular index (HMR) were obtained at baseline and adenosine-induced hyperemia. QCA data were corrected for the presence of guidewire and then the LFC values were calculated. LFC was correlated with FFR, CFR, HSR, and HMR, individually and in combination with %AS, under both baseline and hyperemic conditions. Further, in 5 vessels, LFC group mean values were compared between pre-PCI and post-PCI groups. P<.05 was considered statistically significant. LFC measured at hyperemia correlated significantly when the pressure-based FFR, flow-based CFR, and anatomically measured %AS were combined (r = 0.64; P<.05). Similarly, LFC correlated significantly when HSR, HMR, and %AS were combined (r = 0.72; P<.05). LFC was able to significantly distinguish between pre-PCI and post-PCI groups (0.42 ± 0.05 and 0.05 ± 0.004, respectively; P<.05). Similar results were obtained for the LFC at baseline conditions. LFC, a futuristic parameter that combines both the anatomical and functional endpoints, has potential for application in a clinical setting for stenosis evaluation, under both hyperemic and baseline conditions.

Diagnostic performance of pressure drop coefficient in relation to fractional flow reserve and coronary flow reserve.

OBJECTIVES AND BACKGROUND: Functional assessment of coronary lesion severity during cardiac catheterization is conducted using diagnostic parameters like fractional flow reserve (FFR; pressure derived) and coronary flow reserve (CFR; flow derived). However, the complex hemodynamics of stenosis might not be sufficiently explained by either pressure or flow alone, particularly in the case of intermediate stenosis. CDP (ratio of pressure drop across a stenosis to distal dynamic pressure), a non-dimensional index derived from fundamental fluid dynamic principles based on a combination of intracoronary pressure and flow, may improve the functional assessment of coronary lesion severity. METHODS: We performed a meta-analysis of seven studies, retrieved from MEDLINE and PubMed, comparing the results of FFR and CFR of the same lesions. Two studies reported functional measurements (pressure and flow) obtained in individual patients. Five studies reported two-dimensional plots of FFR vs. CFR. The FFR and CFR data were digitized and corresponding functional measurements were extracted using the reported mean values of hemodynamic data from each of the five studies. The receiver operating characteristic (ROC) curve was used to identify the optimal cut-off point of CDP, which corresponds to the clinically used cut-off values (FFR = 0.80, FFR = 0.75, and CFR = 2.0). RESULTS: CDP correlated significantly with FFR (r = 0.78; P<.001) and had significant diagnostic efficiency (area under the ROC curve = 89%), specificity (83% and 85%), and sensitivity (81% and 76%) at FFR <0.8 and FFR <0.75, respectively. The corresponding cut-off value for CDP to detect FFR <0.80 and FFR <0.75 was at CDP >27.1 and CDP >27.9, respectively. CONCLUSIONS: CDP, a functional parameter based on both intracoronary pressure and flow measurements, has close agreement (area under the ROC curve = 89%) with FFR, the most frequently used method for evaluation of coronary stenosis severity.