When should fractional flow reserve be performed to assess the significance of borderline coronary artery lesions: Derivation of a simplified scoring system

Matar, Fadi; Falasiri, Shayan; Glover, Charles; Khaliq, Asma; Leung, Calvin C.; Mroue, Jad; Ebra, George

doi:10.1016/j.ijcard.2016.07.171

Cited by 5 publications

(12 citation statements)

References 20 publications

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“…In this study, only 33 vessels (31.73%) had hemodynamically relevant stenosis confirmed by invasive FFR measurements. It was in the range of the FFR positive percentage (28-44%) in recent reported studies based on CCTA and ICA [14][15][16][17][18][19], i.e., about two-thirds of FFR measurements finally have negative results. It is mainly contributed to the low sensitivity and PPV with 50% and 59%, respectively, when DS% of ≥ 70% was used as a cutoff value alone.…”

Section: Discussionmentioning

confidence: 71%

“…Besides stenosis severity, the amount of the myocardium in jeopardy is proved as another important factor that effects the FFR value of coronary lesions [13]. Numerous studies have confirmed that the diagnostic performance could be improved by taking into consideration the information about the amount of myocardium at risk, mostly based on ICA data [14][15][16][17][18], in which some different attempts has been used to estimate the myocardial volume perfused by stenotic coronary artery. Myocardial Jeopardy Index (MJI) is primary calculated based on the coronary size and the location in ICA images to estimate the percentage of the left ventricular myocardium subtended to a coronary lesion and has showed a modest correlation to FFR in invasive coronary angiography study [15].…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive prediction model of functionally significant coronary artery stenosis based on coronary computed tomography and the amount of myocardium in jeopardy assessed by fractional flow reserve

Yang

Han

et al. 2019

Chin J Acad Radiol

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Objective To explore a method for estimating the amount of myocardium in jeopardy based on CCTA and derive a comprehensive prediction model for coronary lesions with fractional flow reserve (FFR) value of 0.8 or less. Materials and methods Quantitative features and Myocardial Jeopardy Index (MJI) were measured in 104 coronary arteries of 91 CCTA exams, retrospectively. For 80 left anterior descending (LAD) arteries, the sum of vessel score, diameter stenosis, proximal lesions, apical wrap, long disease, and bifurcation disease were evaluated. The multivariate logistic regression analysis was applied to identify independent predictors for FFR ≤ 0.8. A LAD-Score was then derived to predict the hemodynamic significance of LAD lesions. Results MJI, diameter stenosis and minimum lesion diameter were significant independent predictors for FFR ≤ 0.8 in all 104 vessels at multivariate analysis. The integrated diagnostic accuracy of multi-predictor was increased to 83% from 72% when using stenosis of 70% alone. The area under the receiver operating characteristic curve (AUC) was increased from 0.738 to 0.839 correspondingly. For LAD lesions, the optional cutoff value of stenosis was 62.5%, with a diagnostic accuracy of 71% and AUC of 0.746. Using LAD-Score, the diagnosis accuracy was improved to 84% with AUC of 0.874. Conclusion The MJI, estimated in CCTA, can help to predict the coronary lesion's hemodynamic significance more accurately. The LAD-Score, combining LAD lesion features and the amount of myocardium in jeopardy estimated in CCTA, improves the diagnostic performance assessed using invasive FFR.

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Section: Discussionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

A comprehensive prediction model of functionally significant coronary artery stenosis based on coronary computed tomography and the amount of myocardium in jeopardy assessed by fractional flow reserve

Yang

Han

et al. 2019

Chin J Acad Radiol

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“…Among the 11 unique CPMs, 7 were developed by logistic regression analyses (21)(22)(23)(24)(25)27,29), 3 by machine-learning (ML) algorithms (30,31,35), and 1 by division (26). The number of final variables in each CPM varied greatly from 2 to 34.…”

Section: General Characteristics and Predictorsmentioning

confidence: 99%

“…Quantitative angiography was applied in all 11 models, suggesting its essential role in predicting hemodynamic significance. Of these imaging-based models, 8 used invasive coronary angiography (21,22,(24)(25)(26)(27)29,35), 2 used non-invasive CCTA (23,30), and 1 used both (31). Lesion-specific parameters were used in all models, and >90% of models contained angiographic features.…”

Section: General Characteristics and Predictorsmentioning

confidence: 99%

“…Lesion-specific parameters were used in all models, and >90% of models contained angiographic features. Conversely, patient characteristics (e.g., age and sex) were only considered in 27% of the models (27,30,35). The most commonly used predictors in the FFR CPMs were lesion length (82%), percent diameter stenosis (64%), and minimal lumen diameter (55%) (see Figure 3B).…”

Section: General Characteristics and Predictorsmentioning

confidence: 99%

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Clinical prediction models of fractional flow reserve: an exploration of the current evidence and appraisal of model performance

Zuo¹,

Zhang²,

Yang³

et al. 2021

Quant Imaging Med Surg

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Background: Invasive fractional flow reserve (FFR) is a standard indicator of coronary stenoses' hemodynamic severity. Clinical prediction models (CPMs) may help differentiate ischemic from nonischemic lesions without using a pressure wire but by integrating related variables. This approach differs from that of physics-based models. However, it is not yet known which CPMs are the most reliable at detecting hemodynamic significance.Methods: A systematic review was performed of relevant publications that developed or validated any FFR CPMs from inception to April 2019 in the PubMed, EMBASE, and Cochrane Library databases by two independent authors. The risk of bias and applicability were assessed using the prediction model risk of the bias assessment tool (PROBAST).Results: A total of 11 unique CPMs and 5 subsequent external validation studies were identified. The prevalence of hemodynamically significant lesions (FFR ≤0.80) across the studies had a median of 37.1% (range: 20.7-68.0%). Lesion length, percent diameter stenosis, and minimal lumen diameter were the three most frequently used variables in the CPMs. Of the 11 FFR CPMs, 9 (82%) exhibited strong discrimination [area under the curve (AUC) >0.75], and 5 (45%) had been subject to external validation; however, calibration was only available for 3 models (27%). There was a high degree of applicability; however, none of the studies was assessed as having a low risk of bias. A CPM was identified that had undergone rigorous validation and calibration: the DILEMMA score (three validations; median AUC, 0.83).Conclusions: Almost half of the existing FFR CPMs had been externally validated. Due to their good discrimination abilities, these FFR CPMs are useful tools that could reduce the need for invasive hemodynamic measurements. Future research that adheres to methodological guidelines should be undertaken to develop high-quality models in this setting.

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Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR

Yang

Han

et al. 2018

Int J Cardiovasc Imaging

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To explore the diagnostic performance of a machine-learning-based (ML-based) computed fractional flow reserve (cFFR) derived from coronary computed tomography angiography (CCTA) in identifying ischemia-causing lesions verified by invasive FFR in catheter coronary angiography (ICA). We retrospectively studied 117 intermediate coronary artery lesions [40-80% diameter stenosis (DS)] from 105 patients (mean age 62 years, 32 female) who had undergone invasive FFR. CCTA images were used to compute cFFR values on the workstation. DS and the myocardium jeopardy index (MJI) of coronary stenosis were also assessed with CCTA. The diagnostic performance of cFFR was evaluated, including its correlation with invasive FFR and its diagnostic accuracy. Then, its performance was compared to that of combined DS and MJI. Of the 117 lesions, 36 (30.8%) had invasive FFR ≤ 0.80; 22 cFFR were measured as true positives and 74 cFFR as true negatives. The average time of cFFR assessment was 18 ± 7 min. The cFFR correlated strongly to invasive FFR (Spearman's coefficient 0.665, p < 0.01). When diagnosing invasive FFR ≤ 0.80, the accuracy of cFFR was 82% with an AUC of 0.864, which was significantly higher than that of DS (accuracy 75%, AUC 0.777, p = 0.013). The AUC of cFFR was not significantly different from that of combined DS and MJI (0.846, p = 0.743). cFFR ≤ 0.80 based on CCTA showed good diagnostic performance for detecting ischemia-producing lesions verified by invasive FFR. The short calculation time required renders cFFR promising for clinical use.

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When should fractional flow reserve be performed to assess the significance of borderline coronary artery lesions: Derivation of a simplified scoring system

Cited by 5 publications

References 20 publications

A comprehensive prediction model of functionally significant coronary artery stenosis based on coronary computed tomography and the amount of myocardium in jeopardy assessed by fractional flow reserve

A comprehensive prediction model of functionally significant coronary artery stenosis based on coronary computed tomography and the amount of myocardium in jeopardy assessed by fractional flow reserve

Clinical prediction models of fractional flow reserve: an exploration of the current evidence and appraisal of model performance

Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR

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