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 non-ischemic 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. (PROSPERO registration number: CRD42019125011).

Keywords: Fractional flow reserve (FFR); clinical prediction models (CPMs); risk; systematic review.

Figure 1

Evolving techniques in the assessment of coronary stenosis severity. (A) Traditional quantitative coronary angiography with an intermediate coronary lesion in the left anterior descending artery; (B) physiological measurement with FFR of 0.77 during the hyperemic period; (C) three-dimensional angiography-based model with contrast-flow QFR of 0.83; (D) CPM, which contains variables from patient characteristics, laboratory tests, and lesion parameters, is developed by logistic or ML algorithms to classify coronary lesions into hemodynamic significance (FFR ≤0.80) and hemodynamic insignificance (FFR >0.80). FFR, fractional flow reserve; QFR, quantitative flow ratio; ML, machine learning; CPM, clinical prediction model.

Figure 2

PRISMA flow diagram of study selection. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analysis; CT-FFR, computed tomography-derived fractional flow reserve.

Figure 3

Key features of included studies and models. (A) Geographic origins of included studies; (B) common predictors used in each prediction model. The horizontal axis represents the percentage of models related to each variable. LL, lesion length; DS, diameter stenosis; MLD, minimal lumen diameter; AS, area stenosis; RVD, reference vessel diameter; LAD, left anterior descending artery.

Figure 4

The forest plot of AUCs reported in included studies. For studies reporting more than one AUC, only one value was presented following the priority order: (I) external validation; (II) temporal validation; and (III) derivation or internal validation. AUC, the area under the curve.