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 (FFR_3D).

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 (P_d/P_a) were assessed for patient-specific aortic pressure (P_a) and differing flow rates (Q) to evaluate FFR_3D using the 3D printed model.

Results

There was a good positive correlation (r = 0.87, p < 0.0001) between FFR_3D and invasive FFR. Bland-Altman analysis revealed a good concordance between the FFR_3D 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.

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.

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 XGBoost (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.

See Editorial by Garratt and Schneider

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.

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.

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.

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 [P_d] and proximal [P_a] 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 (P_d/P_a) 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.

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.

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.

Hyperemia-free delineation of epicardial and microvascular impairments using a basal index

The assessment of functional coronary lesion severity using intracoronary hemodynamic parameters like the pressure-derived fractional flow reserve and the flow-derived coronary flow reserve are known to rely critically on the establishment of maximal hyperemia. We evaluated a hyperemia-free index, basal pressure drop coefficient (bCDP), that combines pressure and velocity for simultaneous assessment of the status of both epicardial and microvascular circulations. In 23 pigs, simultaneous measurements of distal coronary arterial pressure and flow were performed using a dual-sensor tipped guidewire in the settings of both normal and abnormal microcirculation with the presence of epicardial lesions of area stenosis (AS) < 50% and AS > 50%. The bCDP, a parameter based on fundamental fluid dynamics principles, was calculated as the transtenotic pressure-drop divided by the dynamic pressure in the distal vessel, measured under baseline (without hyperemia) conditions. The group mean values of bCDP for normal (84 ± 18) and abnormal (124.5 ± 15.6) microcirculation were significantly different. Similarly, the mean values of bCDP from AS < 50% (72.5 ± 16.1) and AS > 50% (136 ± 17.2) were also significantly different (p < 0.05). The bCDP could significantly distinguish between lesions of AS < 50% to AS > 50% under normal microcirculation (52.1 vs. 85.8; p < 0.05) and abnormal microcirculation (84.9 vs. 172; p < 0.05). Further, the bCDP correlated linearly and significantly with the hyperemic parameters FFR (r = 0.42, p < 0.05) and CDP (r = 0.50, p < 0.05). The bCDP is a promising clinical diagnostic parameter that can independently assess the severity of epicardial stenosis and microvascular impairment. We believe that it has an immediate appeal for detection of coronary artery disease if validated clinically.

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.

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.

Effect of heart rate on hemodynamic endpoints under concomitant microvascular disease in a porcine model

Diagnosis of the ischemic power of epicardial stenosis with concomitant microvascular disease (MVD) is challenging during coronary interventions, especially under variable hemodynamic factors like heart rate (HR). The goal of this study is to assess the influence of variable HR and percent area stenosis (%AS) in the presence of MVD on pressure drop coefficient (CDP; ratio of transstenotic pressure drop to the distal dynamic pressure) and lesion flow coefficient (LFC; ratio of %AS to the CDP at the throat region). We hypothesize that CDP and LFC are independent of HR. %AS and MVD were created using angioplasty balloons and 90-μm microspheres, respectively. Simultaneous measurements of pressure drop (DP) and velocity were done in 11 Yorkshire pigs. Fractional flow reserve (FFR), CDP, and LFC were calculated for the groups HR < 120 and HR > 120 beats/min, %AS < 50 and %AS > 50, and additionally for DP < 14 and DP > 14 mmHg, and analyzed using regression and ANOVA analysis. Regression analysis showed independence between HR and the FFR, CDP, and LFC while it showed dependence between %AS and the FFR, CDP, and LFC. In the ANOVA analysis, for the HR < 120 beats/min and HR > 120 beats/min groups, the values of FFR (0.82 ± 0.02 and 0.82 ± 0.02), CDP (83.15 ± 26.19 and 98.62 ± 26.04), and LFC (0.16 ± 0.03 and 0.15 ± 0.03) were not significantly different (P > 0.05). However, for %AS < 50 and %AS > 50, the FFR (0.89 ± 0.02 and 0.75 ± 0.02), CDP (35.97 ± 25.79.10 and 143.80 ± 25.41), and LFC (0.09 ± 0.03 and 0.22 ± 0.03) were significantly different (P < 0.05). A similar trend was observed between the DP groups. Under MVD conditions, FFR, CDP, and LFC were not significantly influenced by changes in HR, while they can significantly distinguish %AS and DP groups.

Effect of changes in contractility on pressure drop coefficient and fractional flow reserve in a porcine model

OBJECTIVES AND BACKGROUND

Decisions based on invasive functional diagnostic measurements are often made in the setting of fluctuating hemodynamic variables that may alter resting or hyperemic measurements. The purpose of this investigation is to analyze the effect of myocardial contractility (CY) on invasive functional parameters. We hypothesize that the pressure drop coefficient (CDPe; ratio of pressure drop to distal dynamic pressure) and fractional flow reserve (FFR; ratio of average pressures distal and proximal to a stenosis) are not affected by fluctuations in CY and can distinguish between different severities of epicardial stenosis.

METHODS

Simultaneous measurements of distal coronary-arterial pressure and velocity were performed in 10 pigs using a dual-sensor tipped guidewire for heart rate (HR) <110 bpm and HR >110 bpm, in the presence of coronary lesions of <50% area stenosis (AS) and >50% AS. Variations in myocardial function and vascular resistance were induced by atrial pacing, papaverine and balloon obstruction, respectively. The maximum rate of rise of left ventricular pressure ([dp/dt]max) was the index of contractility. The contractile function of the heart was empirically defined as CY >900 mm Hg/sec (higher) and CY <900 mm Hg/sec (normal).

RESULTS

For CY >900 mm Hg/sec, under AS <50% and AS >50%, the mean values of FFR (0.91 ± 0.02 and 0.78 ± 0.02), and CDPe (15.6 ± 5.3 and 70.7 ± 24.7) were significantly different (P<.05). Similarly, for CY <900 mm Hg/sec, under AS <50% and AS >50%, the mean values of FFR (0.83 ± 0.04 and 0.63 ± 0.04), and CDPe (43.8 ± 14.9 and 191.8 ± 61.4) were also significantly different (P<.05).

CONCLUSIONS

Both FFR and CDPe could effectively distinguish between stenosis severity at normal and higher levels of myocardial contractility.

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.