Acute changes in myocardial tissue characteristics during hospitalization in patients with COVID-19.

Background: Patients with a history of COVID-19 infection are reported to have cardiac abnormalities on cardiovascular magnetic resonance (CMR) during convalescence. However, it is unclear whether these abnormalities were present during the acute COVID-19 illness and how they may evolve over time. Methods: We prospectively recruited unvaccinated patients hospitalized with acute COVID-19 (n = 23), and compared them with matched outpatient controls without COVID-19 (n = 19) between May 2020 and May 2021. Only those without a past history of cardiac disease were recruited. We performed in-hospital CMR at a median of 3 days (IQR 1-7 days) after admission, and assessed cardiac function, edema and necrosis/fibrosis, using left and right ventricular ejection fraction (LVEF, RVEF), T1-mapping, T2 signal intensity ratio (T2SI), late gadolinium enhancement (LGE) and extracellular volume (ECV). Acute COVID-19 patients were invited for follow-up CMR and blood tests at 6 months. Results: The two cohorts were well matched in baseline clinical characteristics. Both had normal LVEF (62 ± 7 vs. 65 ± 6%), RVEF (60 ± 6 vs. 58 ± 6%), ECV (31 ± 3 vs. 31 ± 4%), and similar frequency of LGE abnormalities (16 vs. 14%; all p > 0.05). However, measures of acute myocardial edema (T1 and T2SI) were significantly higher in patients with acute COVID-19 when compared to controls (T1 = 1,217 ± 41 ms vs. 1,183 ± 22 ms; p = 0.002; T2SI = 1.48 ± 0.36 vs. 1.13 ± 0.09; p < 0.001). All COVID-19 patients who returned for follow up (n = 12) at 6 months had normal biventricular function, T1 and T2SI. Conclusion: Unvaccinated patients hospitalized for acute COVID-19 demonstrated CMR imaging evidence of acute myocardial edema, which normalized at 6 months, while biventricular function and scar burden were similar when compared to controls. Acute COVID-19 appears to induce acute myocardial edema in some patients, which resolves in convalescence, without significant impact on biventricular structure and function in the acute and short-term. Further studies with larger numbers are needed to confirm these findings.

Current Toolset in Predicting Acute Coronary Thrombotic Events: The "Vulnerable Plaque" in a "Vulnerable Patient" Concept.

Despite major advances in pharmacotherapy and interventional procedures, coronary artery disease (CAD) remains a principal cause of morbidity and mortality worldwide. Invasive coronary imaging along with the computation of hemodynamic forces, primarily endothelial shear stress and plaque structural stress, have enabled a comprehensive identification of atherosclerotic plaque components, providing a unique insight into the understanding of plaque vulnerability and progression, which may help guide patient treatment. However, the invasive-only approach to CAD has failed to show high predictive value. Meanwhile, it is becoming increasingly evident that along with the "vulnerable plaque", the presence of a "vulnerable patient" state is also necessary to precipitate an acute coronary thrombotic event. Non-invasive imaging techniques have also evolved, providing new opportunities for the identification of high-risk plaques, the study of atherosclerosis in asymptomatic individuals, and general population screening. Additionally, risk stratification scores, circulating biomarkers, immunology, and genetics also complete the armamentarium of a broader "vulnerable plaque and patient" concept approach. In the current review article, the invasive and non-invasive modalities used for the detection of high-risk plaques in patients with CAD are summarized and critically appraised. The challenges of the vulnerable plaque concept are also discussed, highlighting the need to shift towards a more interdisciplinary approach that can identify the "vulnerable plaque" in a "vulnerable patient".

Rationale and Design of a Randomized Controlled Pilot Trial to Assess Stent Retriever Thrombectomy for Thrombus Burden Reduction in Patients with Acute Myocardial Infarction: The RETRIEVE-AMI Study.

BACKGROUND: ST-elevation myocardial infarction (STEMI) is typically caused by thrombotic occlusion of a coronary artery with subsequent hypoperfusion and myocardial necrosis. In approximately half of patients with STEMI, despite successful restoration of epicardial coronary patency, downstream myocardium perfusion remains impeded. Coronary microvascular injury is one of the key mechanisms behind suboptimal myocardial perfusion and it is primarily, yet not exclusively, related to distal embolization of atherothrombotic material following recanalization of the culprit artery. Routine manual thrombus-aspiration has failed to show clinical efficacy in this scenario. This could be related with limitations in technology adopted as well as patients' selection. To this end, we set out to explore the efficacy and safety of stent retriever-assisted thrombectomy based on clot-removal device routinely used in stroke intervention. STUDY DESIGN AND OBJECTIVES: The stent RETRIEVEr thrombectomy for thrombus burden reduction in patients with Acute Myocardial Infarction (RETRIEVE-AMI) study has been designed to establish whether stent retriever-based thrombectomy is safe and more efficacious in thrombus modification than the current standard of care: manual thrombus aspiration or stenting. The RETRIEVE-AMI trial will enrol 81 participants admitted for primary PCI for inferior STEMI. Participants will be 1:1:1 randomised to receive either standalone PCI, thrombus aspiration and PCI, or retriever-based thrombectomy and PCI. Change in thrombus burden will be assessed via optical coherence tomography imaging. A telephone follow-up at 6 months will be arranged. CONCLUSIONS: It is anticipated by the investigators that stent retriever thrombectomy will more effectively reduce the thrombotic burden compared to current standard of care whilst being clinically safe.

Temporary Mechanical Circulatory Support in Cardiogenic Shock due to ST-Elevation Myocardial Infarction: Analysis of the National Readmissions Database.

Despite advances in temporary mechanical circulatory support (TMCS), in-hospital mortality and morbidity related to cardiogenic shock due to ST elevation myocardial infarction (CS-STEMI) are highly prevalent. We identified admissions with CS-STEMI between 2016 and 2019 from the National Readmission Database (NRD). Among 80 997 patients with CS-STEMI, we identified 42,139 without TMCS, while the remaining received various types of TMCS (Extra corporeal membrane oxygenation [ECMO] alone: n = 753; Intra-aortic balloon pump [IABP] alone: n = 27 556; Impella alone: n = 9055; ECMO with IABP or Impella: n = 1494). 30-day readmission rates did not differ among groups, whereas 90-day readmissions were higher among those with combined ECMO and IABP or Impella support (P = .027). In-hospital mortality and complications including hemodialysis, transfusion, and stroke were the highest in the Impella and combined ECMO and IABP/Impella groups. Heart failure was the most common cause of readmission. Multivariable logistic regression revealed female gender, diabetes, prior myocardial infarction, heart failure, chronic kidney, and peripheral artery disease as risk factors for 90-day readmissions. Our study unveiled several important factors associated with readmission and mortality related to TMCS in CS-STEMI. Approaches to identify and prevent readmissions by addressing these factors may lead to lower morbidity, healthcare cost related to readmission, and improved quality of life.

Machine learning-based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis.

BACKGROUND: Machine learning (ML) is increasingly being applied in Cardiology to predict outcomes and assist in clinical decision-making. We sought to develop and validate an ML model for the prediction of mortality after heart transplantation (HT) in adults with congenital heart disease (ACHD). METHODS: The United Network for Organ Sharing (UNOS) database was queried from 2000 to 2020 for ACHD patients who underwent isolated HT. The study cohort was randomly split into derivation (70%) and validation (30%) datasets that were used to train and test a CatBoost ML model. Feature selection was performed using SHapley Additive exPlanations (SHAP). Recipient, donor, procedural, and post-transplant characteristics were tested for their ability to predict mortality. We additionally used SHAP for explainability analysis, as well as individualized mortality risk assessment. RESULTS: The study cohort included 1033 recipients (median age 34 years, 61% male). At 1 year after HT, there were 205 deaths (19.9%). Out of a total of 49 variables, 10 were selected as highly predictive of 1-year mortality and were used to train the ML model. Area under the curve (AUC) and predictive accuracy for the 1-year ML model were .80 and 75.2%, respectively, and .69 and 74.2% for the 3-year model, respectively. Based on SHAP analysis, hemodialysis of the recipient post-HT had overall the strongest relative impact on 1-year mortality after HΤ, followed by recipient-estimated glomerular filtration rate, age and ischemic time. CONCLUSIONS: ML models showed satisfactory predictive accuracy of mortality after HT in ACHD and allowed for individualized mortality risk assessment.

Machine learning assisted reflectance spectral characterisation of coronary thrombi correlates with microvascular injury in patients with ST-segment elevation acute coronary syndrome.

Aims: We set out to further develop reflectance spectroscopy for the characterisation and quantification of coronary thrombi. Additionally, we explore the potential of our approach for use as a risk stratification tool by exploring the relation of reflectance spectra to indices of coronary microvascular injury. Methods and results: We performed hyperspectral imaging of coronary thrombi aspirated from 306 patients presenting with ST-segment elevation acute coronary syndrome (STEACS). Spatially resolved reflected light spectra were analysed using unsupervised machine learning approaches. Invasive [index of coronary microvascular resistance (IMR)] and non-invasive [microvascular obstruction (MVO) at cardiac magnetic resonance imaging] indices of coronary microvascular injury were measured in a sub-cohort of 36 patients. The derived spectral signatures of coronary thrombi were correlated with both invasive and non-invasive indices of coronary microvascular injury. Successful machine-learning-based classification of the various thrombus image components, including differentiation between blood and thrombus, was achieved when classifying the pixel spectra into 11 groups. Fitting of the spectra to basis spectra recorded for separated blood components confirmed excellent correlation with visually inspected thrombi. In the 36 patients who underwent successful thrombectomy, spectral signatures were found to correlate well with the index of microcirculatory resistance and microvascular obstruction; R 2: 0.80, p < 0.0001, n = 21 and R 2: 0.64, p = 0.02, n = 17, respectively. Conclusion: Machine learning assisted reflectance spectral analysis can provide a measure of thrombus composition and evaluate coronary microvascular injury in patients with STEACS. Future work will further validate its deployment as a point-of-care diagnostic and risk stratification tool for STEACS care.

The role of cardiac computed tomography in predicting adverse coronary events.

Cardiac computed tomography (CCT) is now considered a first-line diagnostic test for suspected coronary artery disease (CAD) providing a non-invasive, qualitative, and quantitative assessment of the coronary arteries and pericoronary regions. CCT assesses vascular calcification and coronary lumen narrowing, measures total plaque burden, identifies plaque composition and high-risk plaque features and can even assist with hemodynamic evaluation of coronary lesions. Recent research focuses on computing coronary endothelial shear stress, a potent modulator in the development and progression of atherosclerosis, as well as differentiating an inflammatory from a non-inflammatory pericoronary artery environment using the simple measurement of pericoronary fat attenuation index. In the present review, we discuss the role of the above in the diagnosis of coronary atherosclerosis and the prediction of adverse cardiovascular events. Additionally, we review the current limitations of cardiac computed tomography as an imaging modality and highlight how rapid technological advancements can boost its capacity in predicting cardiovascular risk and guiding clinical decision-making.

Characteristics, Predictors, and Outcomes of Early mTOR Inhibitor Use After Heart Transplantation: Insights From the UNOS Database.

Background The clinical characteristics of mTOR (mammalian target of rapamycin) inhibitors use in heart transplant recipients and their outcomes have not been well described. Methods and Results We compared patients who received mTOR inhibitors within the first 2 years after heart transplantation to patients who did not by inquiring the United Network for Organ Sharing (UNOS) database between 2010 and 2018. The primary end point was all-cause mortality with retransplantation as a competing event. Rejection, malignancy, hospitalization for infection, and renal transplantation were secondary end points. There were 1619 (9%) and 15 686 (81%) mTOR inhibitors+ and mTOR inhibitors- patients, respectively. Body mass index, induction, cardiac allograft vasculopathy, calculated panel reactive antibody, and fewer days in 1A status were independently associated with mTOR inhibitors+ status. Over a follow-up of 10.4 years, there was no difference in all-cause mortality after adjusting for donor and recipient characteristics (adjusted subdistribution hazard ratio, 1.03 [0.90-1.19]; P=0.66). mTOR inhibitors+ were independently associated with increased risk for rejection (odds ratio [OR], 1.43 [1.11-1.83]; P=0.005) and basal skin cancer (OR, 1.35 [1.19-1.51]; P=0.012) but not for infection or renal transplantation. Conclusions mTOR inhibitors are used in <10% patients in the first 2 years after heart transplantation and are noninferior to contemporary immunosuppression regimens in terms of all-cause mortality, infection, malignancy, or renal transplantation. They are associated with risk for rejection.

A multi-center, international, randomized, 2-year, parallel-group study to assess the superiority of IVUS-guided PCI versus qualitative angio-guided PCI in unprotected left main coronary artery (ULMCA) disease: Study protocol for OPTIMAL trial.

BACKGROUND: Percutaneous coronary intervention (PCI) is used increasingly for revascularization of unprotected left main coronary artery (LMCA) disease. Observational studies and subgroup analyses from clinical trials, have suggested a possible benefit from the use of intravascular ultrasound (IVUS) guidance when performing unprotected LMCA PCI. However, the value of imaging with IVUS has never been proven in an appropriately powered randomized clinical trial. The OPtimizaTIon of Left MAin PCI With IntravascuLar Ultrasound (OPTIMAL) trial has been designed to establish whether IVUS-guided PCI optimization on LMCA is associated with superior clinical outcomes when compared with standard qualitative angiography-guided PCI. METHODS: The OPTIMAL trial is a randomized, multicenter, international study designed to enroll a total of 800 patients undergoing PCI for unprotected LMCA disease. Patients will be randomized in a 1:1 fashion to IVUS-guided PCI versus angiogram-guided PCI. In patients allocated to the angiogram-guided arm, use of IVUS is discouraged, unless there are safety concerns. In patients allocated to the IVUS guidance arm, pre-procedural IVUS assessment is highly recommended, whilst post-procedural IVUS assessment is mandatory to confirm appropriate stenting result and/or to guide stent result optimization, according to predefined criteria. Patients will be followed up to 2 years after the index procedure. The primary outcome measure is the Academic Research Consortium (ARC) patient-oriented composite endpoint (PoCE) which includes all-cause death, any stroke, any myocardial infarction and any repeat revascularization at 2 years follow-up. DISCUSSION: The OPTIMAL trial aims to provide definitive evidence about the clinical impact of IVUS-guidance during PCI to an unprotected LMCA. It is anticipated by the investigators, that an IVUS-guided strategy will be associated with less clinical events compared to a strategy guided by angiogram alone. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04111770. Registered on October 1, 2019.

Artificial intelligence in atherosclerotic disease: Applications and trends.

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

Pre-procedural ATI score (age-thrombus burden-index of microcirculatory resistance) predicts long-term clinical outcomes in patients with ST elevation myocardial infarction treated with primary percutaneous coronary intervention.

BACKGROUND: The ATI (Age-Thrombus burden-Index of Microvascular Resistance [IMR]) score was developed to predict suboptimal myocardial reperfusion in patients with ST-Elevation Myocardial Infarction (STEMI). When applied in the early phases of revascularization (e.g. before stent insertion), it predicts which patients are most likely to have a larger infarct size. In this study, we assessed the score's utility in determining which STEMI patients are at highest risk of clinical events during follow-up. METHODS: The ATI-score was calculated prospectively in 254 STEMI patients using age (>50 years = 1 point), pre-stenting IMR (>40 U and < 100 U = 1 point; ≥100 U = 2 points) and angiographic thrombus score (4 = 1 point, 5 = 3 points); the cohort was stratified in high vs. low-intermediate ATI-score strata (≥4 vs. < 4, respectively). RESULTS: After 3 years of follow-up, patients with high ATI-score presented a higher rate of Major Adverse Cardiac Events (MACE) defined as the composite of all-cause mortality, resuscitated cardiac arrest and new heart failure diagnosis (Hazard Ratio [HR]: 3.07; 95% Confidence Interval [CI]: 1.19-7.93; p = 0.02). The ATI-score showed a moderate discriminative power (c-stat: 0.69), not significantly different from that of other risk scores used in the STEMI setting. A high ATI-score was an independent predictor of MACE (HR: 3.24; 95% CI: 1.22-8.58; p = 0.018). CONCLUSIONS: The ATI-score can discriminate patients at higher risk of long-term adverse events. The score allows predication of subsequent events even before coronary stenting, and consequently it may allow the option of individualized therapy in the early stages of the clinical care-pathway.

Environmental factors, serum biomarkers and risk of atrial fibrillation: an exposure-wide umbrella review of meta-analyses.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. We designed an umbrella review to systematically assess the epidemiological credibility of the associations of non-genetic factors with risk of AF. We searched PubMed and EMBASE from inception to December 31, 2018 to identify systematic reviews and meta-analyses of observational studies for the association of non-genetic factors with risk of AF. For each meta-analysis, we used the random-effects model, and we estimated the 95% confidence and prediction intervals. We also assessed between-study heterogeneity, small-study effects and excess significance bias. We identified 34 eligible papers that examined 51 associations of 42 unique non-genetic factors with risk of AF. Eighteen associations remained statistically significant at P value < 1 × 10-6. Thirty-one associations presented large or very large between-study heterogeneity. Eight associations presented evidence for small-study effects and 13 associations had evidence for excess significance bias. Ten associations, i.e. corrected QT interval, alcohol consumption (highest vs. lowest category, per 1 drink/day increase), body mass index (> 30 units vs. < 30 units, per 5 units increase), waist circumference, body weight, type 2 diabetes mellitus, and smoking (ever vs. never, per 10 cigarettes/day increase) were supported by convincing or highly suggestive evidence in meta-analyses of prospective cohort studies. Type 2 diabetes mellitus, markers of adiposity, alcohol consumption, smoking, and corrected QT interval constitute credible risk factors of AF. Our proposed grading may guide the design of future studies, including Mendelian randomization studies, to assess whether these associations are causal.