Fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation (FLASH): Study protocol for a randomized controlled noninferiority trial.

BACKGROUND: Artificial intelligence-based quantitative coronary angiography (AI-QCA) has been developed to provide a more objective and reproducible data about the severity of coronary artery stenosis and the dimensions of the vessel for intervention in real-time, overcoming the limitations of significant inter- and intraobserver variability, and time-consuming nature of on-site QCA, without requiring extra time and effort. Compared with the subjective nature of visually estimated conventional CAG guidance, AI-QCA guidance provides a more practical and standardized angiography-based approach. Although the advantage of intravascular imaging-guided PCI is increasingly recognized, their broader adoption is limited by clinical and economic barriers in many catheterization laboratories. METHODS: The FLASH (fully automated quantitative coronary angiography versus optical coherence tomography guidance for coronary stent implantation) trial is a randomized, investigator-initiated, multicenter, open-label, noninferiority trial comparing the AI-QCA-assisted PCI strategy with optical coherence tomography-guided PCI strategy in patients with significant coronary artery disease. All operators will utilize a novel, standardized AI-QCA software and PCI protocol in the AI-QCA-assisted group. A total of 400 patients will be randomized to either group at a 1:1 ratio. The primary endpoint is the minimal stent area (mm2), determined by the final OCT run after completion of PCI. Clinical follow-up and cost-effectiveness evaluations are planned at 1 month and 6 months for all patients enrolled in the study. RESULTS: Enrollment of a total of 400 patients from the 13 participating centers in South Korea will be completed in February 2024. Follow-up of the last enrolled patients will be completed in August 2024, and primary results will be available by late 2024. CONCLUSION: The FLASH is the first clinical trial to evaluate the feasibility of AI-QCA-assisted PCI, and will provide the clinical evidence on AI-QCA assistance in the field of coronary intervention. CLINICAL TRIAL REGISTRATION: URL: https://www. CLINICALTRIALS: gov. Unique identifier: NCT05388357.

Circulating miRNA-4701-3p as a predictive biomarker of cardiovascular disease which induces angiogenesis by inhibition of TOB2.

Angiogenesis is mainly regulated by the delivery of VEGF-dependent signaling to cells. However, the angiogenesis mechanism regulated by VEGF-induced miRNA is still not understood. After VEGF treatment in HUVECs, we screened the changed miRNAs through small-RNA sequencing and found VEGF-induced miR-4701-3p. Furthermore, the GFP reporter gene was used to reveal that TOB2 expression was regulated by miR-4701-3p, and it was found that TOB2 and miR-4701-3p modulation could cause angiogenesis in an in-vitro angiogenic assay. Through the luciferase assay, it was confirmed that the activation of the angiogenic transcription factor MEF2 was regulated by the suppression and overexpression of TOB2 and miR-4701-3p. As a result, MEF2 downstream gene mRNAs that induce angiogenic function were regulated. We used the NCBI GEO datasets to reveal that the expression of TOB2 and MEF2 was significantly changed in cardiovascular disease. Finally, it was confirmed that the expression of circulating miR-4701-3p in the blood of myocardial infarction patients was remarkably increased. In patients with myocardial infarction, circulating miR-4701-3p was increased regardless of age, BMI, and sex, and showed high AUC levels in specificity and sensitivity analysis (AUROC) (AUC = 0.8451, 95 % CI 0.78-0.90). Our data showed TOB2-mediated modulation of MEF2 and its angiogenesis by VEGF-induced miR-4701-3p in vascular endothelial cells. In addition, through bioinformatics analysis using GEO data, changes in TOB2 and MEF2 were revealed in cardiovascular disease. We suggest that circulating miR-4701-3p has high potential as a biomarker for myocardial infarction.

Association of metabolic health and obesity with coronary heart disease in adult cancer survivors.

BACKGROUND: The metabolically healthy obese (MHO) phenotype is associated with an increased risk of coronary heart disease (CHD) in the general population. However, association of metabolic health and obesity phenotypes with CHD risk in adult cancer survivors remains unclear. We aimed to investigate the associations between different metabolic health and obesity phenotypes with incident CHD in adult cancer survivors. METHODS: We used National Health Insurance Service (NHIS) to identify a cohort of 173,951 adult cancer survivors aged more than 20 years free of cardiovascular complications. Metabolically healthy nonobese (MHN), MHO, metabolically unhealthy nonobese (MUN), metabolically unhealthy obese (MUO) phenotypes were created using as at least three out of five metabolic health criteria along with obesity (body mass index ≥ 25.0 kg/m2). We used Cox proportional hazards model to assess CHD risk in each metabolic health and obesity phenotypes. RESULTS: During 1,376,050 person-years of follow-up, adult cancer survivors with MHO phenotype had a significantly higher risk of CHD (hazard ratio [HR] = 1.52; 95% confidence intervals [CI]: 1.41 to 1.65) as compared to those without obesity and metabolic abnormalities. MUN (HR = 1.81; 95% CI: 1.59 to 2.06) and MUO (HR = 1.92; 95% CI: 1.72 to 2.15) phenotypes were also associated with an increased risk of CHD among adult cancer survivors. CONCLUSIONS: Adult cancer survivors with MHO phenotype had a higher risk of CHD than those who are MHN. Metabolic health status and obesity were jointly associated with CHD risk in adult cancer survivors.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Predictive value of p53 and AXL immunostaining for the efficacy of immune checkpoint inhibitor-based therapy after osimertinib treatment in patients with epidermal growth factor-mutant non-small cell lung cancer.

BACKGROUND: Current evidence indicates that immune checkpoint inhibitors (ICIs) have a limited efficacy in patients with lung cancer harboring epidermal growth factor receptor (EGFR) mutations. However, there is a lack of data on the efficacy of ICIs after osimertinib treatment, and the predictors of ICI efficacy are unclear. METHODS: We retrospectively assessed consecutive patients with EGFR-mutant NSCLC who received ICI-based therapy after osimertinib treatment at 10 institutions in Japan, between March 2016 and March 2021. Immunohistochemical staining was used to evaluate the expression of p53 and AXL. The deletions of exon 19 and the exon 21 L858R point mutation in EGFR were defined as common mutations; other mutations were defined as uncommon mutations. RESULTS: A total of 36 patients with advanced or recurrent EGFR-mutant NSCLC were analyzed. In multivariate analysis, p53 expression in tumors was an independent predictor of PFS after ICI-based therapy (p = 0.002). In patients with common EGFR mutations, high AXL expression was a predictor of shorter PFS and overall survival after ICI-based therapy (log-rank test; p = 0.04 and p = 0.02, respectively). CONCLUSION: The levels of p53 in pretreatment tumors may be a predictor of ICI-based therapy outcomes in patients with EGFR-mutant NSCLC after osimertinib treatment. High levels of AXL in tumors may also be a predictor of ICI-based therapy outcomes, specifically for patients with common EGFR mutations. Further prospective large-scale investigations on the predictors of ICI efficacy following osimertinib treatment are warranted.

Fully automatic coronary calcium scoring in non-ECG-gated low-dose chest CT: comparison with ECG-gated cardiac CT.

OBJECTIVES: To validate an artificial intelligence (AI)-based fully automatic coronary artery calcium (CAC) scoring system on non-electrocardiogram (ECG)-gated low-dose chest computed tomography (LDCT) using multi-institutional datasets with manual CAC scoring as the reference standard. METHODS: This retrospective study included 452 subjects from three academic institutions, who underwent both ECG-gated calcium scoring computed tomography (CSCT) and LDCT scans. For all CSCT and LDCT scans, automatic CAC scoring (CAC_auto) was performed using AI-based software, and manual CAC scoring (CAC_man) was set as the reference standard. The reliability and agreement of CAC_auto was evaluated and compared with that of CAC_man using intraclass correlation coefficients (ICCs) and Bland-Altman plots. The reliability between CAC_auto and CAC_man for CAC severity categories was analyzed using weighted kappa (κ) statistics. RESULTS: CAC_auto on CSCT and LDCT yielded a high ICC (0.998, 95% confidence interval (CI) 0.998-0.999 and 0.989, 95% CI 0.987-0.991, respectively) and a mean difference with 95% limits of agreement of 1.3 ± 37.1 and 0.8 ± 75.7, respectively. CAC_auto achieved excellent reliability for CAC severity (κ = 0.918-0.972) on CSCT and good to excellent but heterogenous reliability among datasets (κ = 0.748-0.924) on LDCT. CONCLUSIONS: The application of an AI-based automatic CAC scoring software to LDCT shows good to excellent reliability in CAC score and CAC severity categorization in multi-institutional datasets; however, the reliability varies among institutions. KEY POINTS: • AI-based automatic CAC scoring on LDCT shows excellent reliability with manual CAC scoring in multi-institutional datasets. • The reliability for CAC score-based severity categorization varies among datasets. • Automatic scoring for LDCT shows a higher false-positive rate than automatic scoring for CSCT, and most common causes of a false-positive are image noise and artifacts for both CSCT and LDCT.

Cardiovascular Event Rates in Statin-Treated Korean Patients with Cardiovascular Disease: Estimates from a Real-World Population Using Electronic Medical Record Data.

PURPOSE: To estimate the risk of recurrent cardiovascular events in a real-world population of very high-risk Korean patients with prior myocardial infarction (MI), ischemic stroke (IS), or symptomatic peripheral artery disease (sPAD), similar to the Further cardiovascular OUtcomes Research with proprotein convertase subtilisin-kexin type 9 Inhibition in subjects with Elevated Risk (FOURIER) trial population. METHODS: This retrospective study used the Asan Medical Center Heart Registry database built on electronic medical records (EMR) from 2000 to 2016. Patients with a history of clinically evident atherosclerotic cardiovascular disease (ASCVD) with multiple risk factors were followed up for 3 years. The primary endpoint was a composite of MI, stroke, hospitalization for unstable angina, coronary revascularization, and all-cause mortality. RESULTS: Among 15,820 patients, the 3-year cumulative incidence of the composite primary endpoint was 15.3% and the 3-year incidence rate was 5.7 (95% CI 5.5-5.9) per 100 person-years. At individual endpoints, the rates of deaths, MI, and IS were 0.4 (0.3-0.4), 0.9 (0.8-0.9), and 0.8 (0.7-0.9), respectively. The risk of the primary endpoint did not differ significantly between recipients of different intensities of statin therapy. Low-density lipoprotein cholesterol (LDL-C) goals were only achieved in 24.4% of patients during the first year of follow-up. CONCLUSION: By analyzing EMR data representing routine practice in Korea, we found that patients with very high-risk ASCVD were at substantial risk of further cardiovascular events in 3 years. Given the observed risk of recurrent events with suboptimal lipid management by statin, additional treatment to control LDL-C might be necessary to reduce the burden of further cardiovascular events for very high-risk ASCVD patients.

Significance of localized expression of full-length growth differentiation factor-15 in cachexia of advanced non-small cell lung cancer.

PURPOSE: Growth differentiation factor-15 (GDF-15) is one of the key cachexia-inducing factors. Clinical trials on therapies targeting GDF-15 for cancer and cancer cachexia are underway. While the role of circulating GDF-15 in cachexia has been clarified, the effects of GDF-15 expression within cancer cells remain to be fully elucidated. Hence, the objective of this study was to investigate the expression of GDF-15 in advanced lung cancer tissues and to understand its role in cachexia. METHODS: We retrospectively examined the expression level of full-length GDF-15 in advanced non-small cell lung cancer tissues and analyzed the relationship between the staining intensity and clinical data in 53 samples. RESULTS: We found that 52.8% of the total samples were GDF-15 positive, and GDF-15 expression significantly correlated with improved C-reactive protein/albumin ratio (p = 0.008). It did not correlate with the existence of cancer cachexia and overall survival (p = 0.43). CONCLUSION: Our findings show that GDF-15 expression significantly correlated with improved C-reactive protein/albumin ratio, but not the existence of cancer cachexia in advanced NSCLC patients.

Machine Learning-Based prediction of Post-Treatment ambulatory blood pressure in patients with hypertension.

Purpose. Pre-treatment prediction of individual blood pressure (BP) response to anti-hypertensive medication is important to determine the specific regimen for promptly and safely achieving a target BP. This study aimed to develop supervised machine learning (ML) models for predicting patient-specific treatment effects using 24-hour ambulatory BP monitoring (ABPM) data.Materials and Methods. A total of 1,129 patients who had both baseline and follow-up ABPM data were randomly assigned into training, validation and test sets in a 3:1:1 ratio. Utilising the features including clinical and laboratory findings, initial ABPM data, and anti-hypertensive medication at baseline and at follow-up, ML models were developed to predict post-treatment individual BP response. Each case was labelled by the mean 24-hour and daytime BPs derived from the follow-up ABPM.Results. At baseline, 616 (55%) patients had been treated using mono or combination therapy with 45 anti-hypertensive drugs and the remaining 513 (45%) patients had been untreated (drug-naïve). By using CatBoost, the difference between predicted vs. measured mean 24-hour systolic BP at follow-up was 8.4 ± 7.0 mm Hg (% difference of 6.6% ± 5.7%). The difference between predicted vs. measured mean 24-hour diastolic BP was 5.3 ± 4.3 mm Hg (% difference of 6.8% ± 5.5%). There were significant correlations between the CatBoost-predicted vs. the ABPM-measured changes in the mean 24-hour Systolic (r = 0.74) and diastolic (r = 0.68) BPs from baseline to follow-up. Even in the patients with renal insufficiency or diabetes, the correlations between CatBoost-predicted vs. ABPM-measured BP changes were significant.Conclusion. ML algorithms accurately predict the post-treatment ambulatory BP levels, which may assist clinicians in personalising anti-hypertensive treatment.

The prediction of post-treatment BP response is essential to plan the appropriate optimal treatment strategy for achieving the target BP level.The poor predictability of the post-treatment BP level is due to the complex pathophysiology of individual BP response, which can partly explain the poor rate to achieve the target systolic BP.In this current study including both treated and untreated patients with hypertension, machine leaning models predicted the post-treatment mean BP levels on 24-hr ABPM even in high-risk patients and patients with a high BP variability.Model-derived selection and optimisation of anti-hypertension drugs may facilitate prompt achievement of adequate BP control without drug-related complications and avoiding repeating 24-hour ABPM or multiple visits for drug readjustment.

Primary versus rescue retrograde approach for chronic total coronary occlusion.

OBJECTIVES: We aimed to assess the advantages of using the retrograde approach as an initial strategy rather than as a rescue strategy for complex chronic total occlusions (CTOs). BACKGROUND: Even for complex CTOs where a retrograde approach is deemed necessary, an antegrade approach is frequently used as an initial strategy in real-world practice. METHODS: We evaluated 352 retrograde procedures for CTO conducted at our high-volume center between January 2007 and January 2019. Procedural efficiency and safety was assessed based on the guidewire manipulation time (GWMT) and the occurrence of procedure-related adverse events for the primary retrograde approach (PRA) and the rescue retrograde approach (RRA). RESULTS: PRA and RRA were used in 191 (54.3%) and 161 (45.7%) of the CTO procedures, respectively. The complexity of the CTO lesion was significantly higher in the PRA group than in the RRA group (Japanese-CTO score, 2.62 ± 1.07 vs. 2.38 ± 1.06, p = 0.037). The technical success rate of two groups was similar (p = 0.47). The median GWMT required for PRA was significantly shorter than that for RRA (85 [interquartile range, 55-126] vs. 120 [85-157] min, p < 0.001). The total duration of the procedure and fluoroscopic time were shorter, and the number of guidewires and amount of contrast used during the index procedure were smaller in the PRA group. The incidence of procedure-related adverse events was not significantly different between the two groups. CONCLUSIONS: PRA showed higher procedural efficiency than RRA with comparable safety. Opting for PRA for complex CTOs might be a rational decision to enhance the procedural efficiency.

A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation.

OBJECTIVES: Cardiovascular border (CB) analysis is the primary method for detecting and quantifying the severity of cardiovascular disease using posterior-anterior chest radiographs (CXRs). This study aimed to develop and validate a deep learning-based automatic CXR CB analysis algorithm (CB_auto) for diagnosing and quantitatively evaluating valvular heart disease (VHD). METHODS: We developed CB_auto using 816 normal and 798 VHD CXRs. For validation, 640 normal and 542 VHD CXRs from three different hospitals and 132 CXRs from a public dataset were assigned. The reliability of the CB parameters determined by CB_auto was evaluated. To evaluate the differences between parameters determined by CB_auto and manual CB drawing (CB_hand), the absolute percentage measurement error (APE) was calculated. Pearson correlation coefficients were calculated between CB_hand and echocardiographic measurements. RESULTS: CB parameters determined by CB_auto yielded excellent reliability (intraclass correlation coefficient > 0.98). The 95% limits of agreement for the cardiothoracic ratio were 0.00 ± 0.04% without systemic bias. The differences between parameters determined by CB_auto and CB_hand as defined by the APE were < 10% for all parameters except for carinal angle and left atrial appendage. In the public dataset, all CB parameters were successfully drawn in 124 of 132 CXRs (93.9%). All CB parameters were significantly greater in VHD than in normal controls (all p < 0.05). All CB parameters showed significant correlations (p < 0.05) with echocardiographic measurements. CONCLUSIONS: The CB_auto system empowered by deep learning algorithm provided highly reliable CB measurements that could be useful not only in daily clinical practice but also for research purposes. KEY POINTS: • A deep learning-based automatic CB analysis algorithm for diagnosing and quantitatively evaluating VHD using posterior-anterior chest radiographs was developed and validated. • Our algorithm (CB_auto) yielded comparable reliability to manual CB drawing (CB_hand) in terms of various CB measurement variables, as confirmed by external validation with datasets from three different hospitals and a public dataset. • All CB parameters were significantly different between VHD and normal control measurements, and echocardiographic measurements were significantly correlated with CB parameters measured from normal control and VHD CXRs.

Ten-Year Outcomes After Drug-Eluting Stents Versus Coronary Artery Bypass Grafting for Left Main Coronary Disease: Extended Follow-Up of the PRECOMBAT Trial.

BACKGROUND: Long-term comparative outcomes after percutaneous coronary intervention (PCI) with drug-eluting stents and coronary-artery bypass grafting (CABG) for left main coronary artery disease are highly debated. METHODS: In the PRECOMBAT trial (Premier of Randomized Comparison of Bypass Surgery versus Angioplasty Using Sirolimus-Eluting Stent in Patients with Left Main Coronary Artery Disease), patients with unprotected left main coronary artery disease were randomly assigned to undergo PCI with sirolimus-eluting stents (n=300) or CABG (n=300) in 13 hospitals in Korea from April 2004 to August 2009. The follow-up was extended to at least 10 years for all patients (median, 11.3 years). The primary outcome was the incidence of major adverse cardiac or cerebrovascular events (composite of death from any cause, myocardial infarction, stroke, or ischemia-driven target-vessel revascularization). RESULTS: At 10 years, a primary outcome event occurred in 29.8% of the PCI group and in 24.7% of the CABG group (hazard ratio [HR] with PCI vs CABG, 1.25 [95% CI, 0.93-1.69]). The 10-year incidence of the composite of death, myocardial infarction, or stroke (18.2% vs 17.5%; HR 1.00 [95% CI, 0.70-1.44]) and all-cause mortality (14.5% vs 13.8%; HR 1.13 [95% CI, 0.75-1.70]) were not significantly different between the PCI and CABG groups. Ischemia-driven target-vessel revascularization was more frequent after PCI than after CABG (16.1% vs 8.0%; HR 1.98 [95% CI, 1.21-3.21). CONCLUSIONS: Ten-year follow-up of the PRECOMBAT trial of patients with left main coronary artery disease randomized to PCI or CABG did not demonstrate significant difference in the incidence of major adverse cardiac or cerebrovascular events. Because the study was underpowered, the results should be considered hypothesis-generating, highlighting the need for further research. Registration: URL: https://www.clinicaltrials.gov; Unique identifiers: NCT03871127 and NCT00422968.

CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. METHODS: To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. RESULTS: CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. CONCLUSIONS: CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

Development of Force Sensor System Based on Tri-Axial Fiber Bragg Grating with Flexure Structure.

Fiber Bragg grating (FBG) sensors have an advantage over optical sensors in that they are lightweight, easy to terminate, and have a high flexibility and a low cost. Additionally, FBG is highly sensitive to strain and temperature, which is why it has been used in FBG force sensor systems for cardiac catheterization. When manually inserting the catheter, the physician should sense the force at the catheter tip under the limitation of power (<0.5 N). The FBG force sensor can be optimal for a catheter as it can be small, low-cost, easy to manufacture, free of electromagnetic interference, and is materially biocompatible with humans. In this study, FBG fibers mounted on two different flexure structures were designed and simulated using ANSYS simulation software to verify their sensitivity and durability for use in a catheter tip. The selected flexure was combined with three FBGs and an interrogator to obtain the wavelength signals. To obtain a calibration curve, the FBG sensor obtained data on the change in wavelength with force at a high resolution of 0.01 N within the 0.1-0.5 N range. The calibration curve was used in the force sensor system by the LabVIEW program to measure the unknown force values in real time.

Randomized Trial Evaluating Percutaneous Coronary Intervention for the Treatment of Chronic Total Occlusion.

BACKGROUND: Procedural results for percutaneous coronary intervention (PCI) in coronary vessels with chronic total occlusion (CTO) have improved in recent years, and PCI strategies have moved toward more complete revascularization with more liberal use of CTO-PCI. However, evidence evaluating CTO-PCI is limited to observational studies and small clinical trials. METHODS: In this open-label, multicenter, randomized, noninferiority trial, PCI-eligible patients were assigned to receive either 1 of 2 strategies: PCI or no PCI for the qualifying de novo CTO lesion with the option for PCI of obstructive non-CTO lesions at the discretion of the operator. The primary end point was a composite of death, myocardial infarction, stroke, or any revascularization. Health-related quality of life was assessed at baseline and at 1, 6, 12, 24, and 36 months. Because of slow recruitment, the trial was stopped before completion of the 1284 planned enrollments. RESULTS: Between March 2010 and September 2016, 834 patients were randomly assigned to the CTO-PCI (n=417) or no CTO-PCI (n=398) strategy. Among the patients assigned to the no CTO-PCI strategy, 78 (19.6%) crossed over to receive staged CTO-PCI within 3 days of randomization. The overall CTO-PCI success rate was 90.6%. Serious nonfatal complications associated with CTO-PCI occurred in 3 patients (1 stroke, 1 cardiac tamponade, and 1 patient with recurrent episodes of ventricular tachyarrhythmia induced by intracoronary thrombus). Approximately half of the patients in each group underwent PCI for an average of 1.3 non-CTO lesions, resulting in a comparable residual SYNTAX score (Synergy Between PCI With TAXUS and Cardiac Surgery; 3.7±5.4 versus 4.0±5.9, P=0.42) confined to non-CTO vessels. During a median follow-up of 4.0 years (interquartile range, 2.4 to 5.1 years), there was no significant difference between the CTO-PCI and the no CTO-PCI strategies in the incidence of the primary end point (22.3% versus 22.4%, hazard ratio, 1.03; 95% CI, 0.77 to 1.37; P=0.86). Both CTO-PCI and no CTO-PCI strategy were associated with significant improvements but without between-group differences in disease-specific health status that was sustained through 36 months. CONCLUSIONS: CTO-PCI was feasible with high success rates. There was no difference in the incidence of major adverse cardiovascular events with CTO-PCI versus no CTO-PCI, but the study was limited by low power for clinical end points and high crossover rates between groups. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01078051.

Comparison of optical coherence tomography-guided versus intravascular ultrasound-guided percutaneous coronary intervention: Rationale and design of a randomized, controlled OCTIVUS trial.

BACKGROUND: The clinical value of intracoronary imaging for percutaneous coronary intervention (PCI) guidance is well acknowledged. Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) are the most commonly used intravascular imaging to guide and optimize PCI in day-to-day practice. However, the comparative effectiveness of IVUS-guided versus OCT-guided PCI with respect to clinical end points remains unknown. METHODS AND DESIGN: The OCTIVUS study is a prospective, multicenter, open-label, parallel-arm, randomized trial comparing the effectiveness of 2 imaging-guided strategies in patients with stable angina or acute coronary syndromes undergoing PCI in Korea. A total of 2,000 patients are randomly assigned in a 1:1 ratio to either an OCT-guided PCI strategy or an IVUS-guided PCI strategy. The trial uses a pragmatic comparative effectiveness design with inclusion criteria designed to capture a broad range of real-world patients with diverse clinical and anatomical features. PCI optimization criteria are predefined using a common algorithm for online OCT or IVUS. The primary end point, which was tested for both noninferiority (margin, 3.1 percentage points for the risk difference) and superiority, is target-vessel failure (cardiac death, target-vessel myocardial infarction, or ischemia-driven target-vessel revascularization) at 1 year. RESULTS: Up to the end of July 2020, approximately 1,200 "real-world" PCI patients have been randomly enrolled over 2 years. Enrollment is expected to be completed around the midterm of 2021, and primary results will be available by late 2022 or early 2023. CONCLUSION: This large-scale, multicenter, pragmatic-design clinical trial will provide valuable clinical evidence on the relative efficacy and safety of OCT-guided versus IVUS-guided PCI strategies in a broad population of patients undergoing PCI in the daily clinical practice.

Management and Outcomes of Newly Diagnosed Non-Small Cell Lung Cancer Patients with Brain Metastases: A Real-World Study in Japan.

BACKGROUND: Brain metastases (BM) are one of the strongest negative prognostic factors in patients with non-small cell lung cancer (NSCLC). Molecularly targeted agents are standard of care for NSCLC patients with a driver mutation; however, their efficacy in patients with BM is not fully understood because patients with BM are usually excluded from clinical studies. This study investigated the current management and outcomes of newly diagnosed NSCLC patients with BM in Japanese clinical practice, focusing on their driver mutation status. PATIENTS AND METHODS: We enrolled newly diagnosed, treatment-naïve NSCLC patients with BM between January 2012 and December 2015 from 4 institutions in Japan. The medical records of each patient were retrospectively reviewed, and the treatment details and outcomes were evaluated. RESULTS: In total, 203 patients with BM were enrolled in this study and 73 (36%) were neurologically symptomatic. Regarding initial treatment, 110 patients (54%) received local therapy, including radiotherapy and surgery, whereas 77 (38%) received systemic therapy. The median overall survival (OS) was 14.3 months for all patients, and it was significantly longer among patients with a driver mutation (28.9 months; 95% confidence interval [CI], 20.9-41.0) than among patients without a driver mutation (9.9 months; 95% CI, 7.0-13.4) (hazard ratio [HR] 0.39; 95% CI, 0.27-0.57; p < 0.001). Multivariate analysis identified performance status (HR 1.78; 95% CI, 1.16-2.72; p = 0.009) and driver mutation status (HR 0.27; 95% CI, 0.17-0.44; p < 0.001) as significant prognostic factors. No significant difference in OS was noted according to the type of initial treatment, i.e., local versus systemic. CONCLUSION: The median OS of patients with a driver mutation was longer than 2 years, even of patients with BM, and it was significantly longer than that of patients without a driver mutation. Driver mutation status, in addition to performance status, was a significant prognostic factor in NSCLC patients with BM.

Clinical outcomes of contemporary drug-eluting stents in patients with and without diabetes mellitus: Multigroup propensity-score analysis using data from stent-specific, multicenter, prospective registries.

BACKGROUND: Whether the diabetic status differentially affects the clinical outcomes with different drug-eluting stents (DES) has been controversial. METHODS AND RESULTS: From stent-specific, prospective DES registries, we evaluated 17,184 patients (11,428 in non-diabetics and 5,756 in diabetics) who received several contemporary DES: 3570 sirolimus-eluting stents (SES), 5,023 cobalt-chromium everolimus-eluting stents (CoCr-EES), 2,985 platinum-chromium EES (PtCr-EES), 2,913 Resolute zotarolimus-eluting stents (Re-ZES), and 2,693 biodegradable-polymer biolimus-eluting stents (BP-BES). The primary outcome was patient-oriented composite endpoint (POCE, a composite of all-cause death, any myocardial infarction, and any revascularization) at 3-year follow-up and target-vessel failure (a composite of cardiac death, target-vessel myocardial infarction, and target-vessel revascularization) at 3 years was also evaluated. In non-diabetics, the rates of POCE were not significantly different (CoCr-EES 14.3%, PtCr-EES 13.0%, Re-ZES 14.3%, BP-BES 13.4%, and SES 14.6%; overall p = .39). In diabetics, similar results were revealed (CoCr-EES 18.4%, PtCr-EES 20.3%, Re-ZES 17.3%, BP-BES 17.7%, and SES 17.8%; overall p = .44). In multiple treatment propensity-score weighting analysis, regardless of the diabetic status, the hazard ratios for POCE between-individual comparison were similar. Target-vessel failure (a composite of cardiac death, target-vessel myocardial infarction, and target-vessel revascularization) was also comparable except the higher ratio of Re-ZES than PtCr-EES (hazard ratio 1.25, 1.26, 95% confidence interval 1.00-1.55, p = .048) in patients without diabetes. CONCLUSIONS: In this clinical-practice registry study, regardless the diabetic status, the 3-year rates of the primary outcome were similar among different types of DES, suggesting no differential clinical response between contemporary DES in patients with or without diabetes.

Triglyceride glucose index is a useful marker for predicting subclinical coronary artery disease in the absence of traditional risk factors.

BACKGROUND: Atherosclerotic cardiovascular (CV) events commonly occur in individuals with a low CV risk burden. This study evaluated the ability of the triglyceride glucose (TyG) index to predict subclinical coronary artery disease (CAD) in asymptomatic subjects without traditional CV risk factors (CVRFs). METHODS: This retrospective, cross-sectional, and observational study evaluated the association of TyG index with CAD in 1250 (52.8 ± 6.5 years, 46.9% male) asymptomatic individuals without traditional CVRFs (defined as systolic/diastolic blood pressure ≥ 140/90 mmHg; fasting glucose ≥126 mg/dL; total cholesterol ≥240 mg/dL; low-density lipoprotein cholesterol ≥160 mg/dL; high-density lipoprotein cholesterol < 40 mg/dL; body mass index ≥25.0 kg/m2; current smoking; and previous medical history of hypertension, diabetes, or dyslipidemia). CAD was defined as the presence of any coronary plaque on coronary computed tomographic angiography. The participants were divided into three groups based on TyG index tertiles. RESULTS: The prevalence of CAD increased with elevating TyG index tertiles (group I: 14.8% vs. group II: 19.3% vs. group III: 27.6%; P < 0.001). Multivariate logistic regression models showed that TyG index was associated with an increased risk of CAD (odds ratio [OR] 1.473, 95% confidence interval [CI] 1.026-2.166); especially non-calcified (OR 1.581, 95% CI 1.002-2.493) and mixed plaques (OR 2.419, 95% CI 1.051-5.569) (all P < 0.05). The optimal TyG index cut-off for predicting CAD was 8.44 (sensitivity 47.9%; specificity 68.5%; area under the curve 0.600; P < 0.001). The predictive value of this cut-off improved after considering the non-modifiable factors of old age and male sex. CONCLUSIONS: TyG index is an independent marker for predicting subclinical CAD in individuals conventionally considered healthy.

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology.

BACKGROUND: High-resolution medical images that include facial regions can be used to recognize the subject's face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data. According to the Health Insurance Portability and Accountability Act (HIPAA) privacy rules, full-face photographic images and any comparable image are direct identifiers and considered as protected health information. Moreover, the General Data Protection Regulation (GDPR) categorizes facial images as biometric data and stipulates that special restrictions should be placed on the processing of biometric data. OBJECTIVE: This study aimed to develop software that can remove the header information from Digital Imaging and Communications in Medicine (DICOM) format files and facial features (eyes, nose, and ears) at the 2D sliced-image level to anonymize personal information in medical images. METHODS: A total of 240 cranial magnetic resonance (MR) images were used to train the deep learning model (144, 48, and 48 for the training, validation, and test sets, respectively, from the Alzheimer's Disease Neuroimaging Initiative [ADNI] database). To overcome the small sample size problem, we used a data augmentation technique to create 576 images per epoch. We used attention-gated U-net for the basic structure of our deep learning model. To validate the performance of the software, we adapted an external test set comprising 100 cranial MR images from the Open Access Series of Imaging Studies (OASIS) database. RESULTS: The facial features (eyes, nose, and ears) were successfully detected and anonymized in both test sets (48 from ADNI and 100 from OASIS). Each result was manually validated in both the 2D image plane and the 3D-rendered images. Furthermore, the ADNI test set was verified using Microsoft Azure's face recognition artificial intelligence service. By adding a user interface, we developed and distributed (via GitHub) software named "Deface program" for medical images as an open-source project. CONCLUSIONS: We developed deep learning-based software for the anonymization of MR images that distorts the eyes, nose, and ears to prevent facial identification of the subject in reconstructed 3D images. It could be used to share medical big data for secondary research while making both data providers and recipients compliant with the relevant privacy regulations.