Determinants and long-term outcomes of largely uncovered struts in thin-struts drug-eluting stents assessed by optical coherence tomography.

BACKGROUND: Uncovered struts are a determinant of stent failure. The impact of plaque composition and procedural factors on the occurrence, evolution, and outcomes of uncovered struts in a high-risk setting has not been investigated. OBJECTIVE: To investigate the determinants and long-term clinical impact of largely uncovered struts (LUS) in thin-struts drug-eluting stents (DES) implanted in complex lesions by intracoronary optical coherence tomography (OCT). METHODS: Ninety patients with multivessel disease undergoing staged complete revascularization were randomly assigned to bioabsorbable or durable polymer DES. OCT were serially performed during the index procedure, at 3- and 18-month follow-up, and analyzed by an independent core lab. Struts were defined uncovered by OCT if no tissue was visible above the struts. LUS were defined as ≥30% of uncovered struts at 3-month follow-up. Clinical outcomes were the occurrence of target vessel failure (TVF) and major adverse cardiac and cerebrovascular events (MACCE) at 5-year follow-up. RESULTS: LUS occurred in 31 patients (34.4%) regardless of stent platform. At 5 years, no differences were observed in the rate of TVF (12.7% vs. 13.4%; p = 0.91) and MACCE (23.9% vs. 24.9%; p = 0.88) between the two groups. At multivariate logistic regression, plaque rupture, mean lumen diameter, proximal reference vessel area, and maximum stent deployment pressure were independent predictors of LUS. CONCLUSIONS: LUS are a frequent finding in complex coronary lesions treated with thin-struts DES, especially in the presence of plaque rupture. However, in this study, no significant safety signal related to LUS emerged in long-term follow-up.

Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death.

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98-5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800-0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

OCTOPUS - Optical coherence tomography plaque and stent analysis software.

Background and objective: Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions, assessing their outcomes, and characterizing plaque components. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software, which provides highly automated, comprehensive analysis of coronary plaques and stents in IVOCT images. Methods: User specifications for OCTOPUS were obtained from detailed, iterative discussions with IVOCT analysts in the Cardiovascular Imaging Core Laboratory at University Hospitals Cleveland Medical Center, a leading laboratory for IVOCT image analysis. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks for sequential comparisons. Intuitive, interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent area and stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays (e.g., segmented calcifications). To compare images over time, linked visualizations were enabled to display up to four registered vessel segments at a time. Results: OCTOPUS has been deployed for nearly 1 year and is currently being used in multiple IVOCT studies. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 s/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We demonstrated use cases for sequential analysis. To analyze plaque progression, we loaded multiple pullbacks acquired at different points (e.g., pre-stent, 3-month follow-up, and 18-month follow-up) and evaluated frame-level development of in-stent neo-atherosclerosis. In ex vivo cadaver experiments, the OCTOPUS software enabled visualization and quantitative evaluation of irregular stent deployment in the presence of calcifications identified in pre-stent images. Conclusions: We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning.

Prediction of stent under-expansion in calcified coronary arteries using machine learning on intravascular optical coherence tomography images.

It can be difficult/impossible to fully expand a coronary artery stent in a heavily calcified coronary artery lesion. Under-expanded stents are linked to later complications. Here we used machine/deep learning to analyze calcifications in pre-stent intravascular optical coherence tomography (IVOCT) images and predicted the success of vessel expansion. Pre- and post-stent IVOCT image data were obtained from 110 coronary lesions. Lumen and calcifications in pre-stent images were segmented using deep learning, and lesion features were extracted. We analyzed stent expansion along the lesion, enabling frame, segmental, and whole-lesion analyses. We trained regression models to predict the post-stent lumen area and then computed the stent expansion index (SEI). Best performance (root-mean-square-error = 0.04 ± 0.02 mm2, r = 0.94 ± 0.04, p < 0.0001) was achieved when we used features from both lumen and calcification to train a Gaussian regression model for segmental analysis of 31 frames in length. Stents with minimum SEI > 80% were classified as "well-expanded;" others were "under-expanded." Under-expansion classification results (e.g., AUC = 0.85 ± 0.02) were significantly improved over a previous, simple calculation, as well as other machine learning solutions. Promising results suggest that such methods can identify lesions at risk of under-expansion that would be candidates for intervention lesion preparation (e.g., atherectomy).

Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries.

Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

Automatic microchannel detection using deep learning in intravascular optical coherence tomography images.

Microchannel formation is known to be a significant marker of plaque vulnerability, plaque rupture, and intraplaque hemorrhage, which are responsible for plaque progression. We developed a fully-automated method for detecting microchannels in intravascular optical coherence tomography (IVOCT) images using deep learning. A total of 3,075 IVOCT image frames across 41 patients having 62 microchannel segments were analyzed. Microchannel was manually annotated by expert cardiologists, according to previously established criteria. In order to improve segmentation performance, pre-processing including guidewire detection/removal, lumen segmentation, pixel-shifting, and noise filtering was applied to the raw (r,θ) IVOCT image. We used the DeepLab-v3 plus deep learning model with the Xception backbone network for identifying microchannel candidates. After microchannel candidate detection, each candidate was classified as either microchannel or no-microchannel using a convolutional neural network (CNN) classification model. Our method provided excellent segmentation of microchannel with a Dice coefficient of 0.811, sensitivity of 92.4%, and specificity of 99.9%. We found that pre-processing and data augmentation were very important to improve results. In addition, a CNN classification step was also helpful to rule out false positives. Furthermore, automated analysis missed only 3% of frames having microchannels and showed no false positives. Our method has great potential to enable highly automated, objective, repeatable, and comprehensive evaluations of vulnerable plaques and treatments. We believe that this method is promising for both research and clinical applications.

Decision-Making During Percutaneous Coronary Intervention Guided by Optical Coherence Tomography: Insights From the LightLab Initiative.

BACKGROUND: Use of intracoronary imaging is associated with improved outcomes in patients undergoing percutaneous coronary intervention (PCI). Yet, the impact of intracoronary imaging on real-time physician decision-making during PCI is not fully known. METHODS: The LightLab Initiative is a multicenter, prospective, observational study designed to characterize the use of a standardized optical coherence tomography (OCT) workflow during PCI. Participating physicians performed pre-PCI and post-PCI OCT in accordance with this workflow and operator assessments of lesion characteristics and treatment plan were recorded for each lesion based on angiography alone and following OCT. Physicians were categorized as having low (n=15), intermediate (n=13), or high (n=14) OCT use in the year preceding participation. RESULTS: Among 925 patients with 1328 lesions undergoing PCI, the prescribed OCT workflow was followed in 773 (84%) of patients with 836 lesions. Operator lesion assessment and decision-making during PCI changed with OCT use in 86% (721/836) of lesions. Pre-PCI OCT use changed operator decision-making in 80% of lesions, including lesion assessment (45%), vessel preparation strategy (27%), stent diameter (37%), and stent length (36%). Post-PCI OCT changed stent optimization decision-making in 31% of lesions. These findings were consistent across strata of physician prior OCT experience. CONCLUSIONS: A standardized OCT workflow impacted PCI decision-making in 86% of lesions, with a predominant effect on pre-PCI lesion assessment and planning of treatment strategy. This finding was consistent regardless of operator experience level and provides insight into mechanisms by which intravascular imaging might improve PCI outcomes.

Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning.

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

Neoatherosclerosis prediction using plaque markers in intravascular optical coherence tomography images.

Introduction: In-stent neoatherosclerosis has emerged as a crucial factor in post-stent complications including late in-stent restenosis and very late stent thrombosis. In this study, we investigated the ability of quantitative plaque characteristics from intravascular optical coherence tomography (IVOCT) images taken just prior to stent implantation to predict neoatherosclerosis after implantation. Methods: This was a sub-study of the TRiple Assessment of Neointima Stent FOrmation to Reabsorbable polyMer with Optical Coherence Tomography (TRANSFORM-OCT) trial. Images were obtained before and 18 months after stent implantation. Final analysis included images of 180 lesions from 90 patients; each patient had images of two lesions in different coronary arteries. A total of 17 IVOCT plaque features, including lesion length, lumen (e.g., area and diameter); calcium (e.g., angle and thickness); and fibrous cap (FC) features (e.g., thickness, surface area, and burden), were automatically extracted from the baseline IVOCT images before stenting using dedicated software developed by our group (OCTOPUS). The predictive value of baseline IVOCT plaque features for neoatherosclerosis development after stent implantation was assessed using univariate/multivariate logistic regression and receiver operating characteristic (ROC) analyses. Results: Follow-up IVOCT identified stents with (n = 19) and without (n = 161) neoatherosclerosis. Greater lesion length and maximum calcium angle and features related to FC were associated with a higher prevalence of neoatherosclerosis after stent implantation (p < 0.05). Hierarchical clustering identified six clusters with the best prediction p-values. In univariate logistic regression analysis, maximum calcium angle, minimum calcium thickness, maximum FC angle, maximum FC area, FC surface area, and FC burden were significant predictors of neoatherosclerosis. Lesion length and features related to the lumen were not significantly different between the two groups. In multivariate logistic regression analysis, only larger FC surface area was strongly associated with neoatherosclerosis (odds ratio 1.38, 95% confidence interval [CI] 1.05-1.80, p < 0.05). The area under the ROC curve was 0.901 (95% CI 0.859-0.946, p < 0.05) for FC surface area. Conclusion: Post-stent neoatherosclerosis can be predicted by quantitative IVOCT imaging of plaque characteristics prior to stent implantation. Our findings highlight the additional clinical benefits of utilizing IVOCT imaging in the catheterization laboratory to inform treatment decision-making and improve outcomes.

Assessment of Post-Dilatation Strategies for Optimal Stent Expansion in Calcified Coronary Lesions: Ex Vivo Analysis With Optical Coherence Tomography.

INTRODUCTION: Interventional cardiologists make adjustments in the presence of coronary calcifications known to limit stent expansion, but proper balloon sizing, plaque-modification approaches, and high-pressure regimens are not well established. Intravascular optical coherence tomography (IVOCT) provides high-resolution images of coronary tissues, including detailed imaging of calcifications, and accurate measurements of stent deployment, providing a means for detailed study of stent deployment. OBJECTIVE: Evaluate stent expansion in an ex vivo model of calcified coronary arteries as a function of balloon size and high-pressure, post-dilatation strategies. METHODS: We conducted experiments on cadaver hearts with calcified coronary lesions. We assessed stent expansion as a function of size and pressure of non-compliant (NC) balloons (i.e., nominal, 0.5, 1.0, and 1.5 mm balloons at 10, 20 and 30 atm). IVOCT images were acquired pre-stent, post-stent, and at all post-dilatations. Stent expansion was calculated using minimum expansion index (MEI). RESULTS: We analyzed 134 IVOCT pullbacks from ten ex-vivo experiments. The mean distal and proximal reference lumen diameters were 2.2 ± 0.5 mm and 2.5 ± 0.7 mm, respectively, 80% of times using a 3.0 mm diameter stent. Overall, based on stent sizing, a good expansion (MEI ≥ 80%) was reached using the 1:1 NC balloon at 20 atm, and expansion > 100% was reached using the 1:1 NC balloon at 30 atm. In the subgroup analysis, comparing low-calcified and high-calcified lesions, good expansion (MEI ≥ 80%) was reached using the 1:1 NC balloon at nominal pressure (10 atm) versus using 1:1 NC balloon at 30 atm, respectively. Significant vessel rupture was identified in all the vessels mainly upon post-dilatation with larger balloons, and 60% of the experiments (6 vessels, 3 in each calcium subgroup) presented rupture with the +1.0 mm NC balloon at 20 atm. CONCLUSION: When treating calcified lesions, good stent expansion was reached using smaller balloons at higher pressures without coronary injuries, whereas bigger balloons yielded unpredictable expansion even at lower pressures and demonstrated potential harmful damages to the vessels. As these findings could help physicians with appropriate planning of stent post-dilatation for calcified lesions, it will be important to clinically evaluate the recommended protocol.

Mobilization of progenitor cells and vessel healing after implantation of SYNERGY in acute coronary syndrome.

This study was aimed to compare the vascular healing process of a SYNERGY stent with that of a PROMUS PREMIER stent in patients with acute coronary syndrome (ACS). In 71 patients with ACS, undergoing coronary stent implantation using the SYNERGY stent (n = 52) or PROMUS PREMIER stent (n = 19), we measured circulating CD34+/CD133+/CD45null cells and CD34+/KDR+ cells and observed vascular healing at the stented sites using optical coherence tomography (OCT) and coronary angioscopy. On the day 7, circulating CD34+/CD133+/CD45null cells increased in SYNERGY group (P < 0.0001), while it did not change in PROMUS group. The CD34+/KDR+ cells also increased in SYNERGY group (P < 0.0001) but less significantly in the PROMUS group (P < 0.05). The OCT-based neointimal thickness (P < 0.0005) and neointimal coverage rate (P < 0.05) at 12 months were greater in SYNERGY group, compared with PROMUS group. The coronary angioscopy-based neointimal coverage grade at 12 months was also greater in SYNERGY group (P < 0.001). In overall patients, the change in CD34+/KDR+ cells on the day 7 correlated with the OCT-based neointimal thickness at 12 months (R = 0.288, P < 0.05). SYNERGY stent seems to have potential advantages over PROMUS PREMIER stent for ACS patients in terms of vascular healing process at the stented sites.

Degradation modeling of poly-l-lactide acid (PLLA) bioresorbable vascular scaffold within a coronary artery.

In this work, a strain-based degradation model was implemented and validated to better understand the dynamic interactions between the bioresorbable vascular scaffold (BVS) and the artery during the degradation process. Integrating the strain-modulated degradation equation into commercial finite element codes allows a better control and visualization of local mechanical parameters. Both strut thinning and discontinuity of the stent struts within an artery were captured and visualized. The predicted results in terms of mass loss and fracture locations were validated by the documented experimental observations. In addition, results suggested that the heterogeneous degradation of the stent depends on its strain distribution following deployment. Degradation is faster at the locations with higher strains and resulted in the strut thinning and discontinuity, which contributes to the continuous mass loss, and the reduced contact force between the BVS and artery. A nonlinear relationship between the maximum principal strain of the stent and the fracture time was obtained, which could be transformed to predict the degradation process of the BVS in different mechanical environments. The developed computational model provided more insights into the degradation process, which could complement the discrete experimental data for improving the design and clinical management of the BVS.

Optical coherence tomography evaluation of the absorb bioresorbable scaffold performance for overlap versus non-overlap segments in patients with coronary chronic total occlusion: insight from the GHOST-CTO registry.

The Absorb bioresorbable vascular scaffold (BVS) promised to avoid some of the disadvantages of its metal predecessors. Even though it has been taken off the market, limited data is available about its use in coronary chronic total occlusion (CTO) and its performance in overlap segments, which would be of special research interest due to its large thickness. This data is still pertinent since the platform of bioresorbable devices has not been abandoned, with several companies working on it. We aimed to compare healing and performance between overlap (OL) and non-overlap regions (NOL) of CTO lesions treated with BVS, using optical coherence tomography (OCT). Fourteen patients with overlapping BVS were included from the GHOST-CTO registry, resulting in 25 OL and 38 NOL regions. OCT based parameters were compared between OL and NOL groups at baseline (post-implantation) and 12-month follow-up. The mean age was 61.7 ± 7.2 years and 12 (86%) were males. Twelve (86%) patients underwent PCI for stable coronary artery disease and 2 (14%) had unstable angina. At 12-month follow-up, mean lumen area decreased in both NOL and OL regions, but the decrease was significantly larger in the OL region (NOL - 0.7 ± 1.33 vs. OL - 2.4 ± 1.54 mm2; p = 0.002). Mean scaffold area increased in both regions, but increased significantly more in NOL ( + 1.1 ± 1.54 vs. + 0.4 ± 1.16 mm2; p = 0.016). The percent of uncovered struts was lower in the OL group (5.0 ± 6.6% vs. 3.75 ± 8.7%, p = 0.043), whereas the percentage of malapposed struts was similar (0.3 ± 0.5% vs. 0.7 ± 2.3%, p = 0.441). Neointimal hyperplasia (NIH) was more pronounced in the OL region (0.13 ± 0.04 vs. 0.24 ± 0.10 mm2, p = 0.001). The OL and NOL segments showed comparable healing in terms of coverage and malapposition. However, NIH was more prominent in OL region. The long-term clinical implications of these findings needs further evaluation. The present study provides important insights for future development of BVS technology.

Efficacy of a Standardized Computer-Based Training Curriculum to Teach Echocardiographic Identification of Rheumatic Heart Disease to Nonexpert Users.

The ability to integrate echocardiographic for rheumatic heart disease (RHD) into RHD prevention programs is limited because of lack of financial and expert human resources in endemic areas. Task shifting to nonexperts is promising; but investigations into workforce composition and training schemes are needed. The objective of this study was to test nonexperts' ability to interpret RHD screening echocardiograms after a brief, standardized, computer-based training course. Six nonexperts completed a 3-week curriculum on image interpretation. Participant performance was tested in a school-screening environment in comparison to the reference approach (cardiologists, standard portable echocardiography machines, and 2012 World Heart Federation criteria). All participants successfully completed the curriculum, and feedback was universally positive. Screening was performed in 1,381 children (5 to 18 years, 60% female), with 397 (47 borderline RHD, 6 definite RHD, 336 normal, and 8 other) referred for handheld echo. Overall sensitivity of the simplified approach was 83% (95% CI 76% to 89%), with an overall specificity of 85% (95% CI 82% to 87%). The most common reasons for false-negative screens (n = 16) were missed mitral regurgitation (MR; 44%) and MR ≤1.5 cm (29%). The most common reasons for false-positive screens (n = 179) included identification of erroneous color jets (25%), incorrect MR measurement (24%), and appropriate application of simplified guidelines (39.4%). In conclusion, a short, independent computer-based curriculum can be successfully used to train a heterogeneous group of nonexperts to interpret RHD screening echocardiograms. This approach helps address prohibitive financial and workforce barriers to widespread RHD screening.

Meta-analysis of deferral versus performance of coronary intervention based on coronary pressure-derived fractional flow reserve.

Fractional flow reserve (FFR) has been proposed as the gold standard to assess functional severity of coronary artery stenosis and to stratify which lesions should be subjected to intervention (percutaneous coronary intervention [PCI]). A systematic review was performed in MEDLINE and EMBASE including studies indexed until November 2013 that used FFR for deferral or performance of PCI. Outcomes of interest were death, acute myocardial infarction (AMI), and new revascularization (RV). Nineteen studies were included, totaling 3,097 patients (3,796 lesions). Mean follow-up was 21.2 months. In indirect comparisons, FFR-PCI and FFR-defer groups had similar death (2.2% vs 2.0%, respectively, p = 0.86) and AMI rates (1.9% vs 1.9%, respectively, p = 1.00). RV rates were higher in the FFR-PCI group (14.0% vs 4.4%, p = 0.002). Direct comparisons (2-arm trials) also showed no differences in death (odds ratio [OR] 1.86 [95% CI 0.81 to 4.27], I(2) = 11.5, p = 0.14) and AMI rates (OR 0.75 [95% CI 0.21 to 2.69], I(2) = 47.1, p = 0.66); RV rates were again higher in the FFR-PCI (OR 3.10 [95% CI 1.25 to 7.70], I(2) = 72.2, p = 0.015). Meta-regression suggests influence of male gender on RV rates (ß = 0.058, p = 0.026). In conclusion, deferral of PCI based on FFR is a safe strategy. Considerable heterogeneity was observed, however.