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17.04.2021 | Computed Tomography

Feasibility and prognostic role of machine learning-based FFR_CT in patients with stent implantation

verfasst von: Chun Xiang Tang, Bang Jun Guo, Joseph U. Schoepf, Richard R. Bayer II, Chun Yu Liu, Hong Yan Qiao, Fan Zhou, Guang Ming Lu, Chang Sheng Zhou, Long Jiang Zhang

Erschienen in: European Radiology | Ausgabe 9/2021

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Abstract

Objectives

To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFR_CT) in patients who have undergone stents implantation.

Methods

Firstly, the feasibility of FFR_CT in stented vessels was validated. The diagnostic performance of FFR_CT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFR_CT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFR_CT measurements (FFR_CT, ΔFFR_CT, ΔFFR_CT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFR_CT. The primary endpoint was major adverse cardiovascular events (MACE).

Results

Per-patient accuracy of FFR_CT was 0.85 in identifying hemodynamically ISR. FFR_CT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFR_CT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032–1.177]; p = 0.004) and follow-up ΔFFR_CT/length (HR, 1.014 [95% CI, 1.006–1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594–0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846.

Conclusions

Noninvasive ML-based FFR_CT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients.

Key Points

• Machine-learning-based FFR_CT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation.

• Follow-up △FFR_CT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFR_CT in patients with moderate-to-high or high risk needs to be further studied.

• FFR _CT might refine the clinical pathway of patients with stent implantation to invasive catheterization.

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Titel: Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation
verfasst von: Chun Xiang Tang
Bang Jun Guo
Joseph U. Schoepf
Richard R. Bayer II
Chun Yu Liu
Hong Yan Qiao
Fan Zhou
Guang Ming Lu
Chang Sheng Zhou
Long Jiang Zhang
Publikationsdatum: 17.04.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 9/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-07922-w

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Feasibility and prognostic role of machine learning-based FFR_CT in patients with stent implantation