Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective

In this study, we leveraged machine learning techniques to identify risk factors associated with post-COVID-19 mental health disorders. Our analysis, based on data collected from 669 patients across various provinces in Iraq, yielded valuable insights. We found that age, gender, and geographical region of residence were significant demographic factors influencing the likelihood of developing mental health disorders in post-COVID-19 patients. Additionally, comorbidities and the severity of COVID-19 illness were important clinical predictors. Psychosocial factors, such as social support, coping strategies, and perceived stress levels, also played a substantial role. Our findings emphasize the complex interplay of multiple factors in the development of mental health disorders following COVID-19 recovery. Healthcare providers and policymakers should consider these risk factors when designing targeted interventions and support systems for individuals at risk. Machine learning-based approaches can provide a valuable tool for predicting and preventing adverse mental health outcomes in post-COVID-19 patients. Further research and prospective studies are needed to validate these findings and enhance our understanding of the long-term psychological impact of the COVID-19 pandemic. This study contributes to the growing body of knowledge regarding the mental health consequences of the COVID-19 pandemic and underscores the importance of a multidisciplinary approach to address the diverse needs of individuals on the path to recovery. Keywords: COVID-19, mental health, risk factors, machine learning, Ira

Metabolomic Profiling for Identification of Novel Potential Biomarkers in Cardiovascular Diseases

Metabolomics involves the identification and quantification of metabolites present in a biological system. Three different approaches can be used: metabolomic fingerprinting, metabolic profiling, and metabolic footprinting, in order to evaluate the clinical course of a disease, patient recovery, changes in response to surgical intervention or pharmacological treatment, as well as other associated features. Characteristic patterns of metabolites can be revealed that broaden our understanding of a particular disorder. In the present paper, common strategies and analytical techniques used in metabolomic studies are reviewed, particularly with reference to the cardiovascular field

Tracers for Cardiac Imaging: Targeting the Future of Viable Myocardium

Ischemic heart disease is the leading cause of mortality worldwide. In this context, myocar- dial viability is defined as the amount of myocardium that, despite contractile dysfunction, maintains metabolic and electrical function, having the potential for functional enhancement upon revascular- ization. Recent advances have improved methods to detect myocardial viability. The current paper summarizes the pathophysiological basis of the current methods used to detect myocardial viability in light of the advancements in the development of new radiotracers for cardiac imaging

The emerging role of metabolomics in the development of biomarkers for pulmonary hypertension and other cardiovascular diseases (2013 Grover Conference series)

Abstract The functional and prognostic significance of pulmonary hypertension (PH) is well established, yet our understanding of circulating peptides and metabolites that might mark or contribute to various forms of PH remains limited. Metabolites are the end result of all the regulatory complexity present in a cell, tissue, or organism and therefore serve as the most proximal reporters of the body’s response to a disease process or drug therapy. This review presents the rationale, methodology, and preliminary findings from studies that apply comprehensive metabolite profiling to gain knowledge of new circulating markers of PH

Does Transitioning to an OMI/NOMI Model for the Evaluation of Acute Coronary Syndrome in Adult Emergency Department Patients Improve Outcomes Compared to Contemporary STEMI/NSTEMI Model?

Acute coronary syndrome (ACS) continues to be the most common cause of death in the United States, and nearly every 34 seconds one American has a coronary event. Based on 12- lead electrocardiogram (ECG) findings myocardial infarction (MI) patients are treated, according to guidelines, emergently with reperfusion therapy if presenting with ST elevation myocardial infarction (STEMI), versus delayed revascularization if presenting with non-ST elevation myocardial infarction (NSTEMI). However, the evidence shows there is a lack of recognition of which patients require immediate catherization utilizing the current guidelines. In recent years, approximately 70% of acute MI (AMI) patients are classified as NSTEMI. Furthermore, it has been observed that approximately 30% of NSTEMI patients have total occluded coronary arteries (TOCA) on angiography yet face a delayed intervention approach that contributes to worsened clinical outcomes. The current STEMI/NSTEMI paradigm lacks the accuracy in triaging patients who have a suspected acute coronary occlusion (ACO) or near occlusion, with insufficient collateral circulation, whose myocardium is at imminent risk of irreversible infarction without immediate reperfusion. A more recent emerging paradigm to determine who warrants immediate reperfusion is ACO-MI/Non-ACO-MI or Occlusion Myocardial Infarction (OMI) versus Non- Occlusion Myocardial Infarction (NOMI) for short. To answer whether the OMI/NOMI paradigm was superior to STEMI/NSTEMI in evaluating ACS patients, a literature review was conducted primarily utilizing the database PubMed, and certain full-text articles were obtained through Augsburg University’s interlibrary loan system. Overall, literature shows limitations of the current STEMI/NSTEMI paradigm and shows that OMI/NOMI paradigm has superior diagnostic accuracy and earlier recognition abilities for treating patients that present with ACS

Métodos não invasivos para identificação da doença aterosclerótica: desafios para prevenção da doença e eventos clínicos

Atherosclerosis is a chronic inflammatory disease that affects essentially all arterial beds including the aorta, coronaries, carotids, and peripheral arteries. It is the main cause of death in the western hemisphere, due to cardiovascular syndromes such as myocardial infarction, heart failure, and cerebrovascular accidents. Very substantial economic and human resources have been used on treatments of its complications, including imaging studies, coronary bypass surgery, catheter interventions, pacemakers, and medical treatments. Treating complications, however, are remedial actions. A better alternative is to prevent the development of atherosclerosis, or at least to identify patients who are at risk of acute events and intervene before they occur. The aims of this review are to discuss the predictive value of traditional and emerging risk factors, as well as the role of noninvasive diagnostic methods for coronary atherosclerosis, including exercise stress test, echo stress test, duplex ultrasound, computed tomography, and magnetic resonance. A combination of serum biomarkers and noninvasive approaches is of practical utility for identifying early disease. It is to be expected that future developments will soon perfect our ability to identify the vulnerable patient and allow a more individualized approach.A aterosclerose é uma doença inflamatória crônica que afeta essencialmente todas as artérias incluindo a aorta, coronárias, carótidas e artérias periféricas. É a causa principal de morte no hemisfério ocidental, devido as síndromes cardiovasculares, tais como o infarto do miocárdio, insuficiência cardíaca e acidentes cerebrovasculares. Quantidades enormes de recursos econômicos e humanos são usadas em tratamentos de suas complicações, inclusive estudos de imagem, cirurgias coronárias, intervenções com cateteres, marcapasso e tratamentos médicos. Tratar complicações, entretanto, são ações a posteriori. Uma alternativa melhor seria prevenir o desenvolvimento da aterosclerose, ou pelo menos identificar os pacientes que tenham risco de eventos agudos e intervir antes de sua ocorrência. O objetivo desta revisão é discutir o valor prognóstico dos fatores de riscos tradicionais e emergentes, e o papel dos métodos diagnósticos não invasivos para a doença coronária - teste de esforço, eco estresse, ultra-sonografia dúplex, tomografia computadorizada e a ressonância magnética. A combinação de marcadores biológicos e de métodos não invasivos, é de grande utilidade na identificação precoce da doença aterosclerótica. Futuros desenvolvimentos logo aperfeiçoarão nossa capacidade de identificar o paciente vulnerável e nos permitir um manejo mais individualizado

The Current Role of Viability Imaging to Guide Revascularization and Therapy Decisions in Patients With Heart Failure and Reduced Left Ventricular Function

This review describes the current evidence and controversies for viability imaging to direct revascularization decisions and the impact on patient outcomes. Balancing procedural risks and possible benefit from revascularization is a key question in patients with heart failure of ischemic origin (IHF). Different stages of ischemia induce adaptive changes in myocardial metabolism and function. Viable but dysfunctional myocardium has the potential to recover after restoring blood flow. Modern imaging techniques demonstrate different aspects of viable myocardium; perfusion (single-photon emission computed tomography [SPECT], positron emission tomography [PET], cardiovascular magnetic resonance [CMR]), cell metabolism (PET), cell membrane integrity and mitochondrial function (201Tl and 99mTc-based SPECT), contractile reserve (stress echocardiography, CMR) and scar (CMR). Observational studies suggest that patients with IHF and significant viable myocardium may benefit from revascularization compared with medical treatment alone but that in patients without significant viability, revascularization appears to offer no survival benefit or could even worsen the outcome. This was not supported by 2 randomized trials (Surgical Treatment for Ischemic Heart Failure [STICH] and PET and Recovery Following Revascularization [PARR] -2) although post-hoc analyses suggest that benefit can be achieved if decisions had been strictly based on viability imaging recommendations. Based on current evidence, viability testing should not be the routine for all patients with IHF considered for revascularization but rather integrated with clinical data to guide decisions on revascularization of high-risk patients with comorbidities.Peer reviewe

Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models

The COVID-19 pandemic has globally posed numerous health challenges, notably the emergence of post-COVID-19 cardiovascular complications. This study addresses this by utilizing data-driven machine learning models to predict such complications in 352 post-COVID-19 patients from Iraq. Clinical data, including demographics, comorbidities, lab results, and imaging, were collected and used to construct predictive models. These models, leveraging various machine learning algorithms, demonstrated commendable performance in identifying patients at risk. Early detection through these models promises timely interventions and improved outcomes. In conclusion, this research underscores the potential of data-driven machine learning for predicting post-COVID-19 cardiovascular complications, emphasizing the need for continued validation and research in diverse clinical settings

Modelling mitral valvular dynamics–current trend and future directions

Dysfunction of mitral valve causes morbidity and premature mortality and remains a leading medical problem worldwide. Computational modelling aims to understand the biomechanics of human mitral valve and could lead to the development of new treatment, prevention and diagnosis of mitral valve diseases. Compared with the aortic valve, the mitral valve has been much less studied owing to its highly complex structure and strong interaction with the blood flow and the ventricles. However, the interest in mitral valve modelling is growing, and the sophistication level is increasing with the advanced development of computational technology and imaging tools. This review summarises the state-of-the-art modelling of the mitral valve, including static and dynamics models, models with fluid-structure interaction, and models with the left ventricle interaction. Challenges and future directions are also discussed

Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling

Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information