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

Abstract

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.

Impact of calcification on Murray law-based quantitative flow ratio for physiological assessment of intermediate coronary stenoses

“…Coronary artery calcification was not associated with the pressure gradient before and after the administration of adenosine [ 22 ]. Lesion length (82%), QCA-DS (64%), and minimum lumen diameter (55%) were reported as the most frequently used variables in the 11 clinical prediction models for FFR whereas calcification was only used in one of them (9%) [ 23 ]. Velangi et al [ 24 ] also found that only lesion length and low-attenuation plaque were significant independent predictors of ischemia detected by FFR although spotty calcification was predictive of abnormal FFR on univariate analysis.…”

Effect of 320-row CT reconstruction technology on fractional flow reserve derived from coronary CT angiography based on machine learning: single- versus multiple-cardiac periodic images

“…Fractional flow reserve (FFR) is the "gold standard" for evaluating the physiological function of coronary arteries. FFR-guided percutaneous coronary intervention (PCI) can improve the prognosis while reducing the medical costs of patients (2). However, the use of FFR in China is limited due to various factors, including its low penetration rate, invasive nature, high cost, lengthy operation time, lack of a standard fee structure, and inadequate medical insurance coverage (3,4).…”