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Abstract Background Stress induced hyperglycemia (SIH) is common among patients with ST-elevation myocardial infarction (STEMI), even in patients without diabetes mellitus. However, evidence regarding its role on the angiographic outcomes and the prognosis of patients presenting with STEMI is scarce. Methods This study included 309 consecutively enrolled STEMI patients undergoing primary percutaneous coronary intervention (pPCI). Patients were diagnosed with SIH if blood glucose on admission was > 140 mg/dl. Also, patients had to fast for at least 8 hours before blood sampling. The objective was to assess whether SIH was associated with major adverse cardiovascular and cerebrovascular (MACCE) events and explore its relationship with angiographic predictors of worse prognosis such as poor initial TIMI flow, intracoronary thrombus burden, distal embolization, and presence of residual thrombus after pPCI. Results SIH in diabetic and non-diabetic patients was associated with a higher incidence of LTB (aOR = 2.171, 95% CI 1.27–3.71), distal embolization (aOR = 2.71, 95% CI 1.51–4.86), and pre-procedural TIMI flow grade = 0 (aOR = 2.69, 95% CI 1.43–5.04) after adjusting for relevant clinical variables. Importantly, during a median follow-up of 1.7 years STEMI patients with SIH with or without diabetes experienced increased occurrence of MACCE both in univariate (HR = 1.92, 95% CI 1.19–3.01) and multivariate analysis (aHR = 1.802, 95% CI 1.01–3.21). Conclusions SIH in STEMI patients with or without diabetes was independently associated with increased MACCE. This could be attributed to the fact that SIH was strongly correlated with poor pre-procedural TIMI flow, LTB, and distal embolization. Large clinical trials need to validate SIH as an independent predictor of adverse angiographic and clinical outcomes to provide optimal individualized care for patients with STEMI.

Abstract Background Diabetes mellitus (DM) and coronary artery disease (CAD) constitute inter-related clinical entities. Biomarker profiling emerges as a promising tool for the early diagnosis and risk stratification of either DM or CAD. However, studies assessing the predictive capacity of novel metabolomics biomarkers in coexistent CAD and DM are scarce. Methods This post-hoc analysis of the CorLipid trial (NCT04580173) included 316 patients with CAD and comorbid DM who underwent emergency or elective coronary angiography due to acute or chronic coronary syndrome. Cox regression analyses were performed to identify metabolomic predictors of the primary outcome, which was defined as the composite of major adverse cardiovascular or cerebrovascular events (MACCE: cardiovascular death, myocardial infarction, stroke, major bleeding), repeat unplanned revascularizations and cardiovascular hospitalizations. Linear regression analyses were also performed to detect significant predictors of CAD complexity, as assessed by the SYNTAX score. Results After a median 2-year follow up period (IQR = 0.7 years), the primary outcome occurred in 69 (21.8%) of patients. Acylcarnitine ratio C4/C18:2, apolipoprotein (apo) B, history of heart failure (HF), age > 65 years and presence of acute coronary syndrome were independent predictors of the primary outcome in diabetic patients with CAD (aHR = 1.89 [1.09, 3.29]; 1.02 [1.01, 1.04]; 1.28 [1.01, 1.41]; 1.04 [1.01, 1.05]; and 1.12 [1.05–1.21], respectively). Higher levels of ceramide ratio C24:1/C24:0, acylcarnitine ratio C4/C18:2, age > 65 and peripheral artery disease were independent predictors of higher CAD complexity (adjusted β = 7.36 [5.74, 20.47]; 3.02 [0.09 to 6.06]; 3.02 [0.09, 6.06], respectively), while higher levels of apoA1 were independent predictors of lower complexity (adjusted β= − 0.65 [− 1.31, − 0.02]). Conclusions In patients with comorbid DM and CAD, novel metabolomic biomarkers and metabolomics-based prediction models could be recruited to predict clinical outcomes and assess the complexity of CAD, thereby enabling the integration of personalized medicine into routine clinical practice. These associations should be interpreted taking into account the observational nature of this study, and thus, larger trials are needed to confirm its results and validate them in different and larger diabetic populations.

CRISSPAC is an open-source web-based platform for the prediction of the Syntax Score and the severity of coronary artery disease (CAD) providing a variety of data analytics and machine learning solutions presented via an-easy-to-use graphical interface environment. The aim of the software is two-fold: (1) to facilitate both retrospective and prospective objectives dedicated to the diagnosis of the complexity of CAD; (2) to support open science principles through the public availability of the source code. Our envision is to promote software extensibility and utilization towards accurate diagnosis, decision-making processes and personalized patient management and counseling activities.

Micro-computed tomography (micro-CT) constitutes an emerging imaging technique, which can be utilized in cardiovascular medicine to study in-detail the microstructure of heart and vessels. This paper aims to systematically review the clinical utility of micro-CT in cardiovascular imaging and propose future applications of micro-CT imaging in cardiovascular research. A systematic scoping review was conducted by searching for original studies written in English according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews. Medline, Scopus, ClinicalTrials.gov, and the Cochrane library were systematically searched through December 11, 2020 to identify publications concerning micro-CT applications in cardiovascular imaging. Preclinical-animal studies and case reports were excluded. The Newcastle-Ottawa assessment scale for observational studies was used to evaluate study quality. In total, the search strategy identified 30 studies that report on micro-CT–based cardiovascular imaging and satisfy our eligibility criteria. Across all included studies, the total number of micro-CT scanned specimens was 1,227. Six studies involved postmortem 3D-reconstruction of congenital heart defects, while eleven studies described atherosclerotic vessel (coronary or carotid) characteristics. Thirteen other studies employed micro-CT for the assessment of medical devices (mainly stents or prosthetic valves). In conclusion, micro-CT is a novel imaging modality, effectively adapted for the 3D visualization and analysis of cardiac soft tissues and devices at high spatial resolution. Its increasing use could make significant contributions to our improved understanding of the histopathophysiology of cardiovascular diseases, and, thus, has the potential to optimize interventional procedures and technologies, and ultimately improve patient outcomes.

Abstract Background Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide and is associated with multiple inherited and environmental risk factors. This study is designed to identify, design, and develop a panel of genetic markers that combined with clinical and angiographic information, will facilitate the creation of a personalized risk prediction algorithm (GEnetic Syntax Score—GESS). GESS score could be a reliable tool for predicting cardiovascular risk for future adverse events and for guiding therapeutic strategies. Methods GESS (ClinicalTrials.gov Identifier: NCT03150680) is a prospective, non-interventional clinical study designed to enroll 1080 consecutive patients with no prior history of coronary revascularization procedure, who undergo scheduled or emergency coronary angiography in AHEPA, University General Hospital of Thessaloniki. Next generation sequencing (NGS) technology will be used to genotype specific single-nucleotide polymorphisms (SNPs) across the genome of study participants, which were identified as clinically relevant to CAD after extensive bioinformatic analysis of literature-based SNPs. Enrichment analyses of Gene Ontology-Molecular Function, Reactome Pathways and Disease Ontology terms were also performed to identify the top 15 statistically significant terms and pathways. Furthermore, the SYNTAX score will be calculated for the assessment of CAD severity of all patients based on their angiographic findings. All patients will be followed-up for one-year, in order to record any major adverse cardiovascular events. Discussion A group of 228 SNPs was identified through bioinformatic and pharmacogenomic analysis to be involved in CAD through a wide range of pathways and was correlated with various laboratory and clinical parameters, along with the patients' response to clopidogrel and statin therapy. The annotation of these SNPs revealed 127 genes being affected by the presence of one or more SNPs. The first patient was enrolled in the study in February 2019 and enrollment is expected to be completed until June 2021. Hence, GESS is the first trial to date aspiring to develop a novel risk prediction algorithm, the GEnetic Syntax Score, able to identify patients at high risk for complex CAD based on their molecular signature profile and ultimately promote pharmacogenomics and precision medicine in routine clinical settings. Trial registration GESS trial registration: ClinicalTrials.gov Number: NCT03150680. Registered 12 May 2017- Prospectively registered, https://clinicaltrials.gov/ct2/show/NCT03150680 .

The sealing of a large vessel coronary perforation during percutaneous coronary intervention typically requires the deployment of 1 or more covered stents. A novel approach to seal a life-threatening perforation caused by unnoticed wire-exit and balloon dilation, utilizing retrograde techniques, without a covered-stent is described. (Level of Difficulty: Advanced.)

BackgroundRoutine coronary artery disease (CAD) secondary prevention strategies target standard modifiable cardiovascular risk factors (SMuRFs), which include: diabetes mellitus, dyslipidemia, hypertension, and smoking. However, a significant proportion of patients with acute coronary syndrome (ACS) present without any SMuRFs. The angiographic severity of disease in this population has not yet been investigated.MethodsAfter propensity score matching of patients without SMuRFs and patients with ≥1 SMuRFs (ratio 1:3), we used zero-inflated negative binomial regression modeling to investigate the relationship of SMuRF-less status with the angiographic severity of CAD, as measured by the SYNTAX score. Survival analysis was performed to investigate differences in all-cause mortality at 30 days and at the end of follow-up period.ResultsWe analyzed 534 patients presenting with ACS who underwent coronary angiography. Of them, 56 (10.5%) presented without any SMuRF. After propensity score matching, the median SYNTAX score was 13.8 (IQR 0–22.1) in 56 SMuRF-less patients and 14 (IQR 5–25) in 166 patients with ≥1 SMuRFs. SMuRF-less status was associated with increased odds of zero SYNTAX score [zero-part model: odds ratio = 2.11, 95% confidence interval (CI): 1.03–4.33], but not with decreased SYNTAX score among patients with non-zero SYNTAX score (count-part model: incidence rate ratio = 0.99, 95% CI: 0.79–1.24); the overall distribution of the SYNTAX score was similar between the two groups (p = 0.26). The 30-day risk for all-cause mortality was higher for SMuRF-less patients compared to patients with ≥1 SMuRFs [hazard ratio (HR) = 3.58, 95% CI: 1.30–9.88]; however, the all-cause mortality risk was not different between the two groups over a median 1.7-year follow-up (HR = 1.72, 95% CI: 0.83–3.57).ConclusionAmong patients with ACS, the absence of SMuRFs is associated with increased odds for non-obstructive CAD and with increased short-term mortality rates.

Abstract Background Coronary artery disease (CAD) remains one of the leading causes of mortality and morbidity worldwide. As oxygen and nutrient supply to the myocardium significantly decrease during ischemic periods, important changes occur regarding myocardial intermediary energy metabolism. Metabolomics is an emerging field in systems biology, which quantifies metabolic changes in response to disease progression. This study aims to evaluate the diagnostic utility of plasma metabolomics-based biomarkers for determining the complexity and severity of CAD, as it is assessed via the SYNTAX score. Methods Corlipid is a prospective, non-interventional cohort trial empowered to enroll 1065 patients with no previous coronary intervention history, who undergo coronary angiography in University Hospital AHEPA, Thessaloniki. Venous blood samples are collected before coronary angiography. State-of the-art analytical methods are performed to calculate the serum levels of novel biomarkers: ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and the ratio of apolipoprotein B/apolipoprotein A1. Furthermore, all patients will be categorized based on the indication for coronary angiography (acute coronary syndrome, chronic coronary syndrome, preoperative coronary angiography) and on the severity of CAD using the SYNTAX score. Follow-up of 12 months after enrollment will be performed to record the occurrence of major adverse cardiovascular events. A risk prediction algorithm will be developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD based on their metabolite signatures. The first patient was enrolled in July 2019 and completion of enrollment is expected until May 2021. Discussion CorLipid is an ongoing trial aiming to investigate the correlation between metabolic profile and complexity of coronary artery disease in a cohort of patients undergoing coronary angiography with the potential to suggest a decision-making tool with high discriminative power for patients with CAD. To our knowledge, Corlipid is the first study aspiring to create an integrative metabolomic biomarkers-based algorithm by combining metabolites from multiple classes, involved in a wide range of pathways with well-established biochemical markers. Trial registration CorLipid trial registration: ClinicalTrials.gov number: NCT04580173. Registered 8 October 2020—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04580173 .

Abstract Background Although the presence of thrombus in patients with ST-elevation myocardial infarction (STEMI) has been linked to adverse outcomes, routine thrombus aspiration has not been proven effective. A potential explanation is that these patients should be risk-stratified. Traditional clinical, laboratory and angiographic parameters used in clinical trials have been proven inadequate to classify patients. Aspirated thrombotic material characteristics might be an additional important parameter that has not yet been addressed. In this report, we aim to describe a methodological analysis of thrombus aspirated from coronary arteries during primary PCI using micro-Computed Τomography (micro-CT). These data will be combined with traditional factors to develop a risk-stratification system with high discriminative power for these patients. Methods Eighty-seven patients with STEMI undergoing thrombus aspiration in AHEPA University Hospital, Greece, will be enrolled in the study. The first patient was enrolled in June 2018. After being aspirated, thrombi are preserved in formalin and their volume and density are calculated with micro-CT. Micro-CT allows us to create 3D models of thrombi from a series of x-ray projection images. These models are further analyzed to find the volume and density of extracted thrombi and to assess potential differences in their structure. Association of these variables with clinical parameters and angiographic outcomes will be explored. Discussion QUEST-STEMI is-to our knowledge-the first study of volumetric coronary thrombus assessment by micro-CT. This method could be used in larger, clinically-oriented trials to help stratify patients with thrombus burden according to their risk for adverse outcomes. Trial registration QUEST-STEMI trial ClinicalTrials.gov number: NCT03429608 Date of registration: February 12, 2018. The study was prospectively registered (registered prior to enrollment of the first participant).

An ever-growing amount of accumulated data has materialized in several scientific fields, due to recent technological progress. New challenges emerge in exploiting these data and utilizing the valuable available information. Causal models are a powerful tool that can be employed towards this aim, by unveiling the structure of causal relationships between different variables. The causal structure may avail experts to better understand relationships, or even uncover new knowledge. Based on 963 patients with coronary artery disease, the robustness of the causal structure of single nucleotide polymorphisms was assessed, taking into account the value of the Syntax Score, an index that evaluates the complexity of the disease. The causal structure was investigated, both locally and globally, under different levels of intervention, reflected in the number of patients that were randomly excluded from the original datasets corresponding to two categories of the Syntax Score, zero and positive. It is shown that the causal structure of single nucleotide polymorphisms was more robust under milder interventions, whereas in the case of stronger interventions, the impact increased. The local causal structure around the Syntax Score was studied in the case of a positive Syntax Score, and it was found to be resilient, even when the intervention was strong. Consequently, employing causal models in this context may increase the understanding of the biological aspects of coronary artery disease.

Our study aims to develop a data-driven framework utilizing heterogenous electronic medical and clinical records and advanced Machine Learning (ML) approaches for: (i) the identification of critical risk factors affecting the complexity of Coronary Artery Disease (CAD), as assessed via the SYNTAX score; and (ii) the development of ML prediction models for accurate estimation of the expected SYNTAX score. We propose a two-part modeling technique separating the process into two distinct phases: (a) a binary classification task for predicting, whether a patient is more likely to present with a non-zero SYNTAX score; and (b) a regression task to predict the expected SYNTAX score accountable to individual patients with a non-zero SYNTAX score. The framework is based on data collected from the GESS trial (NCT03150680) comprising electronic medical and clinical records for 303 adult patients with suspected CAD, having undergone invasive coronary angiography in AHEPA University Hospital of Thessaloniki, Greece. The deployment of the proposed approach demonstrated that atherogenic index of plasma levels, diabetes mellitus and hypertension can be considered as important risk factors for discriminating patients into zero- and non-zero SYNTAX score groups, whereas diastolic and systolic arterial blood pressure, peripheral vascular disease and body mass index can be considered as significant risk factors for providing an accurate estimation of the expected SYNTAX score, given that a patient belongs to the non-zero SYNTAX score group. The experimental findings utilizing the identified set of important risk factors indicate a sufficient prediction performance for the Support Vector Machine model (classification task) with an F-measure score of ~0.71 and the Support Vector Regression model (regression task) with a median absolute error value of ~6.5. The proposed data-driven framework described herein present evidence of the prediction capacity and the potential clinical usefulness of the developed risk-stratification models. However, further experimentation in a larger clinical setting is needed to ensure the practical utility of the presented models in a way to contribute to a more personalized management and counseling of CAD patients.

Background: An alarming cerebro/cardiovascular collateral damage, reflected by a decline in admissions for acute stroke (AS) and acute coronary syndrome (ACS), was observed during the initial phase of the COVID-19 pandemic, thereby leading to a re-design of public campaigns. However, there are limited data regarding the AS and ACS hospitalization rates during the second wave of the pandemic, which was followed by re-imposition of lockdowns. Methods: We calculated the rate of AS and ACS hospitalizations from three representative tertiary care hospitals in Greece during a 2-month period (November–December 2020) of the second wave of the COVID-19 pandemic compared with the corresponding control period in 2019 from three representative tertiary care hospitals in Greece. This was a follow-up study with identical design to our previous report evaluating AS and ACS hospitalizations during the first wave of the pandemic (March–April 2020). Results: Compared with 2019, there was a 34% relative reduction of AS hospitalizations [incidence rate ratio (IRR): 0.66, 95% confidence interval (CI): 0.48–0.92, p = 0.013] and 33% relative reduction of ACS hospitalizations (IRR: 0.67, 95% CI: 0.54–0.83, p

Background: Angiographic detection of thrombus in STEMI is associated with adverse outcomes. However, routine thrombus aspiration failed to demonstrate the anticipated benefit. Hence, management of high coronary thrombus burden remains challenging. We sought to assess for the first time extracted thrombotic material characteristics utilizing micro-computed tomography (micro-CT).Methods: One hundred thirteen STEMI patients undergoing thrombus aspiration were enrolled. Micro-CT was undertaken to quantify retrieved thrombus volume, surface, and density. Correlation of these indices with angiographic and electrocardiographic outcomes was performed.Results: Mean aspirated thrombus volume, surface, and density (±standard deviation) were 15.71 ± 20.10 mm3, 302.89 ± 692.54 mm2, and 3139.04 ± 901.88 Hounsfield units, respectively. Aspirated volume and surface were significantly higher (p < 0.001) in patients with higher angiographic thrombus burden. After multivariable analysis, independent predictors for thrombus volume were reference vessel diameter (RVD) (p = 0.011), right coronary artery (RCA) (p = 0.039), and smoking (p = 0.027), whereas RVD (p = 0.018) and RCA (p = 0.019) were predictive for thrombus surface. Thrombus volume and surface were independently associated with distal embolization (p = 0.007 and p = 0.028, respectively), no-reflow phenomenon (p = 0.002 and p = 0.006, respectively), and angiographically evident residual thrombus (p = 0.007 and p = 0.002, respectively). Higher thrombus density was correlated with worse pre-procedural TIMI flow (p < 0.001). Patients with higher aspirated volume and surface developed less ST resolution (p = 0.042 and p = 0.023, respectively).Conclusions: Angiographic outcomes linked with worse prognosis were more frequent among patients with larger extracted thrombus. Despite retrieving larger thrombus load in these patients, current thrombectomy devices fail to deal with thrombotic material adequately. Further studies of novel thrombus aspiration technologies are warranted to improve patient outcomes.Clinical Trial Registration: QUEST-STEMI trial ClinicalTrials.gov number: NCT03429608 Date of registration: February 12, 2018. The study was prospectively registered.

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691–0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.

MicroRNAs (miRNAs) create systems networks and gene-expression circuits through molecular signaling and cell interactions that contribute to health imbalance and the emergence of cardiovascular disorders (CVDs). Because the clinical phenotypes of CVD patients present a diversity in their pathophysiology and heterogeneity at the molecular level, it is essential to establish genomic signatures to delineate multifactorial correlations, and to unveil the variability seen in therapeutic intervention outcomes. The clinically validated miRNA biomarkers, along with the relevant SNPs identified, have to be suitably implemented in the clinical setting in order to enhance patient stratification capacity, to contribute to a better understanding of the underlying pathophysiological mechanisms, to guide the selection of innovative therapeutic schemes, and to identify innovative drugs and delivery systems. In this article, the miRNA–gene networks and the genomic signatures resulting from the SNPs will be analyzed as a method of highlighting specific gene-signaling circuits as sources of molecular knowledge which is relevant to CVDs. In concordance with this concept, and as a case study, the design of the clinical trial GESS (NCT03150680) is referenced. The latter is presented in a manner to provide a direction for the improvement of the implementation of pharmacogenomics and precision cardiovascular medicine trials.

Recent studies support that acylcarnitines exert a significant role in cardiovascular disease development and progression. The aim of this metabolomics-based study was to investigate the association of serum acylcarnitine levels with coronary artery disease (CAD) severity, as assessed via SYNTAX Score. Within the context of the prospective CorLipid trial (NCT04580173), the levels of 13 circulating acylcarnitines were accurately determined through a newly developed HILIC-MS/MS method in 958 patients undergoing coronary angiography in the AHEPA University Hospital of Thessaloniki, Greece. Patients presenting with acute coronary syndrome had significantly lower median acylcarnitine C8, C10, C16, C18:1 and C18:2 values, compared to patients with chronic coronary syndrome (p = 0.012, 0.007, 0.018, 0.011 and p = 0.026), while median C10, C16, C18:1 and C18:2 levels were higher in stable angina compared to STEMI (p = 0.019 p = 0.012, p = 0.013 and p < 0.001, respectively). Moreover, median C2, C3, C4 and C8 levels were significantly elevated in patients with diabetes mellitus (p < 0.001, p-values less than 0.05). With regard to CAD severity, median C4 and C5 levels were elevated and C16 and C18:2 levels were reduced in the high CAD complexity group with SYNTAX Score > 22 (p = 0.002, 0.024, 0.044 and 0.012, respectively), indicating a potential prognostic capability of those metabolites and of the ratio C4/C18:2 for the prediction of CAD severity. In conclusion, serum acylcarnitines could serve as clinically useful biomarkers leading to a more individualized management of patients with CAD, once further clinically oriented metabolomics-based studies provide similar evidence.

Objectives: Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare, distinct pulmonary vascular disease, and therefore, there is a lack of data regarding its clinical presentation, diagnosis, and management at a national basis. We aimed to describe the demographics and management of patients with CTEPH in Northern Greece. Methods: We conducted a retrospective, observational study by a joint collaboration between two pulmonary hypertension expert centers in Greece, and the study included patients diagnosed with CTEPH. The patient population was divided into two groups depending on their operability. Results: Overall, 27 consecutive patients were included (59% female, mean age 59.3±15.1 years). Dyspnea and fatigue were the most common presenting symptoms. History of pulmonary embolism was present in 82%. Of patients, 18 (67%) were assessed as operable, of whom 10 (55%) finally underwent pulmonary endarterectomy (PEA). There were no differences in symptoms, WHO functional class, 6-min walking test distance, and hemodynamics between the operable and nonoperable groups. At the end of follow-up, all non-operable and operable patients who did not receive surgical treatment were treated with at least one pulmonary hypertension-specific drug. Conclusion: This is the first report that presents data of patients diagnosed with CTEPH in Greece. The percentage of patients who underwent surgical treatment is lower but approaches the reported rates in large registries. Considering that PEA is a relatively safe and potentially curative surgical procedure, we emphasize the need for establishing a designated PEA center in Greece. Keywords: Chronic thromboembolic pulmonary hypertension, pulmonary endarterectomy, registry, riociguat

Micro-computed tomography (micro-CT) is a promising novel medical imaging modality that allows for non-destructive volumetric imaging of surgical tissue specimens at high spatial resolution. The aim of this study is to provide a comprehensive assessment of the clinical applications of micro-CT for the tissue-based diagnosis of lung diseases. This scoping review was conducted in accordance with the PRISMA Extension for Scoping Reviews, aiming to include every clinical study reporting on micro-CT imaging of human lung tissues. A literature search yielded 570 candidate articles, out of which 37 were finally included in the review. Of the selected studies, 9 studies explored via micro-CT imaging the morphology and anatomy of normal human lung tissue; 21 studies investigated microanatomic pulmonary alterations due to obstructive or restrictive lung diseases, such as chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and cystic fibrosis; and 7 studies examined the utility of micro-CT imaging in assessing lung cancer lesions (n = 4) or in transplantation-related pulmonary alterations (n = 3). The selected studies reported that micro-CT could successfully detect several lung diseases providing three-dimensional images of greater detail and resolution than routine optical slide microscopy, and could additionally provide valuable volumetric insight in both restrictive and obstructive lung diseases. In conclusion, micro-CT-based volumetric measurements and qualitative evaluations of pulmonary tissue structures can be utilized for the clinical management of a variety of lung diseases. With micro-CT devices becoming more accessible, the technology has the potential to establish itself as a core diagnostic imaging modality in pathology and to enable integrated histopathologic and radiologic assessment of lung cancer and other lung diseases.

Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This study is an effort to address the problem of identifying important factors related to the success or failure of the PCI. Methods: The analysis is based on the EuroCTO Registry, which is the largest database available worldwide, consisting of 164 variables and 29,995 cases for the period 2008–2018. The aim is to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models use graph structure to assess the cause–effect relationships between variables. In this study, the constrained-based algorithm PC was employed. The focus was to find the local causal structure around the PCI result and use it as a feature selection tool for building a predictive model. Results: The model developed was compared with other modeling approaches from the literature, and it was found to perform equally well or better. Conclusions: The analysis showcased the potential of employing local causal structure in predictive model development.