Optimizing Percutaneous Mitral Valvuloplasty for Rheumatic Mitral Stenosis　- Clinical Significance of Changes in Mitral Valve Area.

BACKGROUND: Percutaneous mitral valvuloplasty (PMV) is a standard treatment for severe rheumatic mitral stenosis (RMS). However, the prognostic significance of the change in mitral valve area (∆MVA) during PMV is not fully understood.Methods and Results: This study analyzed data from the Multicenter mitrAl STEnosis with Rheumatic etiology (MASTER) registry, which included 3,140 patients with severe RMS. We focused on patients with severe RMS undergoing their first PMV. Changes in echocardiographic parameters, including MVA quantified before and after PMV, and composite outcomes, including mitral valve reintervention, heart failure admission, stroke, and all-cause death, were evaluated. An optimal result was defined as a postprocedural MVA ≥1.5 cm2without mitral regurgitation greater than Grade II. Of the 308 patients included in the study, those with optimal results and ∆MVA >0.5 cm² had a better prognosis (log-rank P<0.001). Patients who achieved optimal results but with ∆MVA ≤0.5 cm² had a greater risk of composite outcomes than those with optimal outcomes and ∆MVA >0.5 cm² (nested Cox regression analysis, hazard ratio 2.27; 95% confidence interval 1.09-4.73; P=0.028). CONCLUSIONS: Achieving an increase in ∆MVA of >0.5 cm2was found to be correlated with improved outcomes. This suggests that, in addition to achieving traditional optimal results, targeting an increase in ∆MVA of >0.5 cm2could be a beneficial objective in PMV treatment for RMS.

Shifts in Clinical Characteristics, Treatment, and Outcome for Rheumatic Mitral Stenosis: Insights From a 20-Year Multicentre Registry Study in Korea.

BACKGROUND: The rapid economic development of South Korea provides a unique model to study changes in the clinical characteristics, treatment approaches, and clinical outcomes of patients with rheumatic mitral stenosis (MS) relative to socioeconomic growth. METHODS: From the Multicenter mitrAl STEnosis with Rheumatic etiology (MASTER) registry, 2,337 patients diagnosed with moderate or severe rheumatic MS between January 2001 and December 2020 were analyzed. Patients were grouped into consecutive 5-year intervals based on their year of diagnosis. Clinical characteristics, echocardiographic data, and clinical outcomes were assessed. RESULTS: Over 20 years, the severity of mitral stenosis increased from 79.1% to 90.2%; similarly, the average age at diagnosis increased from 54.3 to 63.0 years (all P < 0.001). Comorbidities such as hypertension and atrial fibrillation increased (6.3% to 29.5% and 41.4% to 46.9%, respectively; all P for trend < 0.05). The rate of mitral intervention within five years after diagnosis increased from 31.2% to 47.4% (P for trend < 0.001). However, clinical outcomes of rheumatic mitral stenosis deteriorated over time in the composite outcomes (log-rank test, P < 0.001). Conversely, the incidence of stroke remained stable (60.6-73.7%; P < 0.001), which might be attributed to the increased use of anticoagulation therapy. CONCLUSION: This study observed an increase in patient age, comorbidities, and valve disease severity as the country transitioned from a developing to developed status. Despite a rise in mitral valve interventions, clinical outcomes deteriorated over 20 years, highlighting the need for modified treatment approaches to improve patient outcomes.

The Impact of Different Cooking Methods on the Flavor Profile of Fermented Chinese Spicy Cabbage.

Chinese spicy cabbage (CSC) is a common traditional fermented vegetable mainly made of Chinese cabbage. In addition to eating raw, boiling and stir-frying are the most common cooking methods for CSC. To identify the impacts of boiling or stir-frying on the quality of CSC, the physicochemical properties, flavor compounds, and sensory properties of CSC were analyzed. A total of 47 volatile flavor compounds (VFCs) were detected by gas chromatography-mass spectrometry. Sulfide was determined as the main flavor compound of CSC, mainly contributed by cabbage, garlic, and onion odors. The content of sulfide decreased significantly after cooking. Nonanal, geranyl acetate, and linalool were newly generated after boiling with odor activity value (OAV) > 1, and contributed fatty, sweet, fruity, and floral odors to BL-CSC. 1-Octen-3-one, 1-octen-3-ol, octanal, nonanal, and (E)-2-nonenal were newly generated after stir-frying with OAV > 1, and contributed mushroom, fatty, and green odors to SF-CSC. Diallyl trisulfide, nonanal, (E)-ß-ionone, ß-sesquiphellandrene, and (E)-2-decenal were considered as the potential key aroma compounds (KACs) to distinguish the CSCs after different heat treatment. After cooking, the total titratable acidity of CSC increased and the sensory properties changed significantly. This study provides valuable information and guidance on the sensory and flavor changes of thermal processing fermented vegetables.

Characteristics and outcomes of heart transplant recipients with a pretransplant history of malignancy.

We aimed to investigate the characteristics and outcomes of HTx recipients with a history of pretransplant malignancy (PTM). Among 1062 HTx recipients between 1997 and 2013, 73 (7.1%) patients had PTMs (77 cancer cases). We analyzed post-HTx outcome, recurrence of PTM, and development of de novo malignancies. Post-HTx outcome included overall survival, 10-year survival, 10-year freedom from cardiac allograft vasculopathy (CAV), non-fatal major adverse cardiac events (NF-MACE), any treated rejection (ATR), acute cellular rejection (ACR), and antibody-mediated rejection (AMR). Four most common PTMs were lymphoproliferative disorders (18.2%), prostate cancers (18.2%), non-melanoma skin cancers (18.2%), and breast cancers (13.0%). Median time from PTM and HTx was 9.0 years. During a median follow-up of 8.6 years after HTx, patients with PTM, compared to those without, showed significantly higher incidence of posttransplant malignancies (43.8% vs. 20.8%, p < .001) including 9.6% (n = 7) of PTM recurrences. However, patients with PTM, compared to those without, showed comparable overall survival, 10-year survival, 10-year freedom from CAV, NF-MACE, ATR, ACR, and AMR. Therefore, a history of PTM should not disqualify patients from HTx listing, while further research is necessary for early detection of posttransplant malignancies in these patients.

Screening of gamma-aminobutyric acid-producing lactic acid bacteria and the characteristic of glutamate decarboxylase from Levilactobacillus brevis F109-MD3 isolated from kimchi.

AIMS: This study aimed to screen the γ-aminobutyric acid (GABA)-producing lactic acid bacteria (LAB) from kimchi, and investigate the glutamate decarboxylase (GAD) activity of the highest GABA-producing strain. METHODS AND RESULTS: Seven strains of LAB were screened from kimchi with GABA-producing activity. Strain Levilactobacillus brevis F109-MD3 showed the highest GABA-producing ability. It produced GABA at a concentration of 520 mmol l-1 with a 97.4% GABA conversion rate in MRS broth containing 10% monosodium glutamate for 72 h. The addition of pyridoxal 5'-phosphate had no significant effect on the GAD activity of L. brevis F109-MD3. The optimal pH range of GAD was 3.0-5.0 and the optimal temperature was 65°C. The D value of GAD at 50, 60 and 70°C was 7143, 971 and 124 min respectively and Z value was 11.36°C. CONCLUSIONS: Seven strains isolated from kimchi, especially F109-MD3, showed high GABA-production ability even in the high concentrations of MSG at 7.5% and 10%. The GAD activity showed an effective broad pH range and higher optimal temperature. SIGNIFICANCE AND IMPACT OF THE STUDY: These seven strains could be potentially useful for food-grade GABA production and the development of healthy foods.

Ability of soluble ST2 to predict left ventricular remodeling in patients with acute coronary syndrome.

The association of the soluble suppression of tumorigenicity 2 (sST2) and the prognosis of heart failure have been well evaluated. However, little is known about the prediction of sST2 for left ventricular (LV) remodeling in acute coronary syndrome (ACS). We investigated the ability of sST2 to predict LV remodeling following the revascularization of ACS. From May 2019 to December 2020, 95 patients with LV ejection fraction (EF) < 50% who underwent coronary revascularization for ACS (unstable angina, non-ST-elevation myocardial infarction, ST-elevation myocardial infarction) were enrolled. Echocardiography and sST2 were performed at baseline and at a 3-month follow-up. The association between LV remodeling, using the end-diastolic volume index, and sST2 at baseline and at the 3-month follow-up, and the difference between each value was explored. During follow-up, 41 patients showed LV adverse remodeling. The baseline sST2 increased in patients without adverse remodeling (32.05 ng/mL vs. 23.5 ng/mL, p < 0.001), although clinical characteristics were similar between the two groups. During the mean follow-up of 3 months, a significant correlation was found in the changes between sST2 and LV end-diastolic/systolic volume index (r = 0.649; p < 0.001, r = 0.618; p < 0.001, respectively), but not in the changes of LVEF (r = - 0.132, p = 0.204). The use of angiotensin-converting enzyme 2 inhibitors/receptor blockers was higher (90.7% vs. 53.7%, p < 0.001) and sST2 decreased more predominantly in patients without adverse remodeling (23.18 ng/mL vs 26.40 ng/mL, p = 0.003). However, the changes in sST2 and LV volume were not different according to the ACS types (p > 0.05, for all). Estimates of the odds ratio (OR) for remodeling according to the sST2 difference increased substantially with a negative increase in the sST2 difference. Multivariable analysis found that, the difference between the baseline and 3-month sST2 was the most important determinant of LV remodeling following the revascularization of ACS (OR 1.24; 95% confidence interval: 1.09 to 1.41; p = 0.001). In conclusion, an increase in sST2 during follow-up was a useful predictor of LV remodeling.

The role of cardiac CT throughout the full cardiac cycle in diagnosing patent foramen ovale in patients with acute stroke.

OBJECTIVES: We explored the hypothesis that the diagnostic performance of cardiac computed tomography (CT) throughout the full cardiac cycle would be superior to single-phase CT and comparable to transesophageal echocardiography (TEE) in diagnosing patent foramen ovale (PFO). METHODS AND RESULTS: From May 2011 to April 2015, 978 patients with stroke were diagnosed with PFO by TEE. In patients with stroke, cardiac CT was performed if the patients had more than two cardiovascular risk factors. We prospectively enrolled 70 patients with an indication for cardiac CT. Cardiac CT images were reconstructed at 10% increments of the R-R interval. The sensitivity of cardiac CT throughout the full cardiac cycle in diagnosing PFO was compared to that for TEE and single-phase cardiac CT. To evaluate the specificity of cardiac CT, we analyzed patients without PFO confirmed by TEE who underwent cardiac CT within 1 month of pre-cardiac surgery. Sixty-six patients with cardiac CT and TEE were included in the final analysis. Throughout the full cardiac cycle, cardiac CT had a sensitivity of 89.4% and a specificity of 92.3% in diagnosing PFO, compared to TEE as a reference. PFO was primarily detected in the 60% and 70% intervals in 10-phase reconstructed images. The sensitivity of PFO diagnosis with cardiac CT was 81.8% when analyzing both the 60% and 70% intervals instead of the full cardiac cycle. CONCLUSION: Cardiac CT throughout the full cardiac cycle outperforms single-phase cardiac CT in detecting PFO. Cardiac CT can be used as an alternative method to TEE for detecting PFO in stroke patients. KEY POINTS: • Throughout the full cardiac cycle, cardiac computed tomography (CT) had a sensitivity of 89.4% and a specificity of 92.3% in diagnosing patent foramen ovale (PFO), compared to transesophageal echocardiography. • The sensitivity of diagnosing patent foramen ovale with cardiac CT was 81.8% when analyzing 60% and 70% R-R intervals instead of the full cardiac cycle. • Cardiac CT with retrospective electrocardiographic gating throughout the full cardiac cycle can increase the detectability of PFO, compared to single-phase cardiac CT.

Joint Multimodal Embedding and Backtracking Search in Vision-and-Language Navigation.

Due to the development of computer vision and natural language processing technologies in recent years, there has been a growing interest in multimodal intelligent tasks that require the ability to concurrently understand various forms of input data such as images and text. Vision-and-language navigation (VLN) require the alignment and grounding of multimodal input data to enable real-time perception of the task status on panoramic images and natural language instruction. This study proposes a novel deep neural network model (JMEBS), with joint multimodal embedding and backtracking search for VLN tasks. The proposed JMEBS model uses a transformer-based joint multimodal embedding module. JMEBS uses both multimodal context and temporal context. It also employs backtracking-enabled greedy local search (BGLS), a novel algorithm with a backtracking feature designed to improve the task success rate and optimize the navigation path, based on the local and global scores related to candidate actions. A novel global scoring method is also used for performance improvement by comparing the partial trajectories searched thus far with a plurality of natural language instructions. The performance of the proposed model on various operations was then experimentally demonstrated and compared with other models using the Matterport3D Simulator and room-to-room (R2R) benchmark datasets.

Hybrid Imitation Learning Framework for Robotic Manipulation Tasks.

This study proposes a novel hybrid imitation learning (HIL) framework in which behavior cloning (BC) and state cloning (SC) methods are combined in a mutually complementary manner to enhance the efficiency of robotic manipulation task learning. The proposed HIL framework efficiently combines BC and SC losses using an adaptive loss mixing method. It uses pretrained dynamics networks to enhance SC efficiency and performs stochastic state recovery to ensure stable learning of policy networks by transforming the learner's task state into a demo state on the demo task trajectory during SC. The training efficiency and policy flexibility of the proposed HIL framework are demonstrated in a series of experiments conducted to perform major robotic manipulation tasks (pick-up, pick-and-place, and stack tasks). In the experiments, the HIL framework showed about a 2.6 times higher performance improvement than the pure BC and about a four times faster training time than the pure SC imitation learning method. In addition, the HIL framework also showed about a 1.6 times higher performance improvement and about a 2.2 times faster training time than the other hybrid learning method combining BC and reinforcement learning (BC + RL) in the experiments.

Vision-Language-Knowledge Co-Embedding for Visual Commonsense Reasoning.

Visual commonsense reasoning is an intelligent task performed to decide the most appropriate answer to a question while providing the rationale or reason for the answer when an image, a natural language question, and candidate responses are given. For effective visual commonsense reasoning, both the knowledge acquisition problem and the multimodal alignment problem need to be solved. Therefore, we propose a novel Vision-Language-Knowledge Co-embedding (ViLaKC) model that extracts knowledge graphs relevant to the question from an external knowledge base, ConceptNet, and uses them together with the input image to answer the question. The proposed model uses a pretrained vision-language-knowledge embedding module, which co-embeds multimodal data including images, natural language texts, and knowledge graphs into a single feature vector. To reflect the structural information of the knowledge graph, the proposed model uses the graph convolutional neural network layer to embed the knowledge graph first and then uses multi-head self-attention layers to co-embed it with the image and natural language question. The effectiveness and performance of the proposed model are experimentally validated using the VCR v1.0 benchmark dataset.

NMN-VD: A Neural Module Network for Visual Dialog.

Visual dialog demonstrates several important aspects of multimodal artificial intelligence; however, it is hindered by visual grounding and visual coreference resolution problems. To overcome these problems, we propose the novel neural module network for visual dialog (NMN-VD). NMN-VD is an efficient question-customized modular network model that combines only the modules required for deciding answers after analyzing input questions. In particular, the model includes a Refer module that effectively finds the visual area indicated by a pronoun using a reference pool to solve a visual coreference resolution problem, which is an important challenge in visual dialog. In addition, the proposed NMN-VD model includes a method for distinguishing and handling impersonal pronouns that do not require visual coreference resolution from general pronouns. Furthermore, a new Compare module that effectively handles comparison questions found in visual dialogs is included in the model, as well as a Find module that applies a triple-attention mechanism to solve visual grounding problems between the question and the image. The results of various experiments conducted using a set of large-scale benchmark data verify the efficacy and high performance of our proposed NMN-VD model.

Tracklet Pair Proposal and Context Reasoning for Video Scene Graph Generation.

Video scene graph generation (ViDSGG), the creation of video scene graphs that helps in deeper and better visual scene understanding, is a challenging task. Segment-based and sliding-window based methods have been proposed to perform this task. However, they all have certain limitations. This study proposes a novel deep neural network model called VSGG-Net for video scene graph generation. The model uses a sliding window scheme to detect object tracklets of various lengths throughout the entire video. In particular, the proposed model presents a new tracklet pair proposal method that evaluates the relatedness of object tracklet pairs using a pretrained neural network and statistical information. To effectively utilize the spatio-temporal context, low-level visual context reasoning is performed using a spatio-temporal context graph and a graph neural network as well as high-level semantic context reasoning. To improve the detection performance for sparse relationships, the proposed model applies a class weighting technique that adjusts the weight of sparse relationships to a higher level. This study demonstrates the positive effect and high performance of the proposed model through experiments using the benchmark dataset VidOR and VidVRD.

Atrial Fibrillation and Heart Failure with Preserved Ejection Fraction: Two Chronic Troublemakers.

In this article, the definition; mechanisms; diagnostic strategies, including scoring systems; treatments; prognosis; and future perspectives in heart failure with preserved ejection fraction with atrial fibrillation, which are common comorbid conditions, are reviewed thoroughly.

Clinical significance of early-diastolic tissue velocity imaging of lateral mitral annulus for prognosis of nonischemic left ventricular dysfunction.

PURPOSE: We explored the potential of tissue velocity imaging (TVI) for prognosis of nonischemic left ventricular (LV) dysfunction (LVD). METHODS: We reviewed 138 nonischemic LVD patients (58 ± 14 years) who underwent both cardiac magnetic resonance (CMR) and echocardiography. Septal and lateral mitral annular TVI data were compared with late gadolinium enhancement (LGE) on CMR. During a mean follow-up of 24 months, recovery (>15%) of LV ejection fraction and clinical outcomes (cardiovascular death and heart failure hospitalization) were assessed. RESULTS: LGE was commonly observed in the basal anteroseptal, inferoseptal, and inferior segments, but infrequently observed in the anterolateral segment. LGE was associated with lower early diastolic, septal (Sep-e' = 5.2 ± 2.0 vs 6.9 ± 2.0 cm/s, P = .031) and lateral (Lat-e' = 7.3 ± 3.0 vs 9.5 ± 2.0 cm/s, P < .001) TVI. The relationship between Lat-e' and anterolateral LGE (area under the curve, AUC 0.834) was much better than that between Sep-e' and inferoseptal LGE (AUC 0.699). The 60 patients with LVD reversibility revealed higher Lat-e' (9.8 ± 2.0 vs 6.7 ± 2.2 cm/s, P < .001) and lower LGE burden (7.3 ± 9.0 vs 22 ± 10%, P < .001), while Lat-e' ≤ 7.8 cm/s appeared unfavorable for 31 events patients. On multivariate analyses, Lat-e' (HR 0.79, 95% CI 0.63-0.99, P = .044) and LVD reversibility (HR 0.53, 95% CI 0.16-0.90, P = .018) were still meaningful together with LGE segments and burden. CONCLUSION: Lat-e' was related with LVD reversibility and a significant predictor of clinical outcomes.

Spatio-Temporal Action Detection in Untrimmed Videos by Using Multimodal Features and Region Proposals.

This paper proposes a novel deep neural network model for solving the spatio-temporal-action-detection problem, by localizing all multiple-action regions and classifying the corresponding actions in an untrimmed video. The proposed model uses a spatio-temporal region proposal method to effectively detect multiple-action regions. First, in the temporal region proposal, anchor boxes were generated by targeting regions expected to potentially contain actions. Unlike the conventional temporal region proposal methods, the proposed method uses a complementary two-stage method to effectively detect the temporal regions of the respective actions occurring asynchronously. In addition, to detect a principal agent performing an action among the people appearing in a video, the spatial region proposal process was used. Further, coarse-level features contain comprehensive information of the whole video and have been frequently used in conventional action-detection studies. However, they cannot provide detailed information of each person performing an action in a video. In order to overcome the limitation of coarse-level features, the proposed model additionally learns fine-level features from the proposed action tubes in the video. Various experiments conducted using the LIRIS-HARL and UCF-10 datasets confirm the high performance and effectiveness of the proposed deep neural network model.

Intraventricular Flow: More than Pretty Pictures.

Patients with heart failure show myocardial, valvular, and electrical dysfunction, which results in enlarged cardiac chambers and increased intracardiac volume and pressure. Intracardiac flow analysis can provide information regarding the shape and wall properties, chamber dimensions, and flow efficiency throughout the cardiac cycle. There is increasing interest in vortex flow analysis for patients with heart failure to overcome limitations of conventional parameters. In conjunction with the conventional structural and functional parameters, vortex flow analysis-guided treatment in heart failure might be a novel option for cardiac physicians.

A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology.

Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge, but also the past. In addition, ST-RCQL includes a variety of time operators and time constants; thus, queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

Implication of ultrasound contrast-enhancement of carotid plaques in prevalence of acute coronary syndrome and occurrence of cardiovascular outcomes.

PURPOSE: Ultrasonographic contrast enhancement of carotid plaque (CECP) has been used to detect neovascularization of vasa vasorum and plaque. However, it is uncertain whether CECP can provide risk stratification of coronary artery disease (CAD). This study aimed to evaluate the relationship between CECP and manifestations of acute coronary syndrome (ACS) in established CAD patients and to explore the prognostic implication of CECP for cardiovascular (CV) clinical outcomes. METHODS: A medical record review revealed that contrast-enhanced ultrasonography was performed to evaluate carotid atherosclerosis in 209 coronary artery-stented and 105 non-stented patients. The rate of ACS manifestations was compared depending on contrast uptake patterns: grade 0, absent; grade 1, dot; and grade 2, diffuse pattern. CV primary outcomes were assessed during a mean 7.6 months of follow-up. RESULTS: Male sex, smoking, history of old myocardial infarction, intensive medications, and a favorable lipid profile were common in the stented versus non-stented group. Patients with grade 2 CECP had a higher rate of ACS, greater plaque thickness, and class I-II of Gray-Weale plaque echogenicity. During follow-up, 10 coronary revascularizations (nine ACSs), six strokes, and four heart failures occurred. Grade 2 CECP was more closely related with CV primary outcomes and showed a tendency toward more acute CV outcomes. CONCLUSION: ACS manifestations were proportionate to CECP grade. Diffuse CECP uptake could be a risk factor for acute CV outcomes.

Usefulness of Frequency Domain Optical Coherence Tomography Compared with Intravascular Ultrasound as a Guidance for Percutaneous Coronary Intervention.

OBJECTIVES: To compare outcomes and rates of optimal stent placement between optical coherence tomography (OCT) and intravascular ultrasound (IVUS) guided percutaneous coronary intervention (PCI). BACKGROUND: Unlike IVUS-guided PCI, rates of clinical outcomes and optimal stent placement have not been well characterized for OCT-guided PCI. METHODS: The study enrolled 290 patients who underwent implantation of a second generation drug eluting stent under OCT (122 patients) or IVUS (168 patients) guidance. The two groups were compared after adjusting for baseline differences using 1:1 propensity score matching (PSM) (114 patients in each group). Optimal stent placement was defined as achieving an adequate lumen (optimal minimum stent area [MSA > 4.85 mm(2) for OCT, >5 mm(2) for IVUS] or a final MSA ≥ 90% of the distal reference lumen area, without edge dissection, incomplete stent apposition, or tissue prolapse), or otherwise performing additional interventions to address suboptimal post-stenting OCT or IVUS findings. The primary endpoint was one-year cumulative incidence of major adverse cardiac events (MACE; cardiac death, myocardial infarction and target lesion revascularization). Definite or probable stent thrombosis (ST) rates were evaluated. RESULTS: In adjusted comparisons between OCT and IVUS groups, there was no significant difference in rates of MACE (3.5% vs. 3.5%, P = 1.000) and ST (0% vs. 0.9%, P = 1.000) at 1 year, optimal stent placement (89.5% vs. 92.1%, P = 0.492), and further intervention (7.9% vs.13.2%, P = 0.234), despite OCT significantly more frequently detecting tissue prolapse (97.4% vs. 47.4%, P < 0.001), and numerically more edge dissection (10.5% vs. 4.4%, P = 0.078) or incomplete stent apposition (48.2% vs. 36.8%, P = 0.082). CONCLUSIONS: OCT guidance showed comparable results to IVUS in mid-term clinical outcomes, suggesting that OCT can be an alternative tool for stent placement optimization.