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Journal of Cardiothoracic Surgery Jun 2024Following an acute myocardial infarction (AMI), surgery for left ventricular free wall rupture (LVFWR) and ventricular septal rupture (VSR) has a high in-hospital...
BACKGROUND
Following an acute myocardial infarction (AMI), surgery for left ventricular free wall rupture (LVFWR) and ventricular septal rupture (VSR) has a high in-hospital mortality rate, which has not improved significantly over time. Unloading the LV is critical to preventing excessive stress on the repair site and avoiding problems such as bleeding, leaks, patch dehiscence, and recurrence of LVFWR and VSR because the tissue is so fragile. We present two cases of patients who used Impella 5.5 for LV unloading following emergency surgery for AMI mechanical complications.
CASE PRESENTATION
A 76-year-old male STEMI patient underwent fibrinolysis of the distal right coronary artery. Three days later, he passed out and went into shock. Echocardiography revealed a cardiac tamponade. We found an oozing-type LVFWR on the posterolateral wall and treated it with a non-suture technique using TachoSil. Before the patient was taken off CPB, Impella 5.5 was inserted into the LV via a 10 mm synthetic graft connected to the right axillary artery. We kept the flow rate above 4.0 to 4.5 L/min until POD 3 to reduce LV wall tension while minimizing pulsatility. On POD 6, we weaned the patient from Impella 5.5. A postoperative cardiac CT scan showed no contrast leakage from the LV. However, a cerebral hemorrhage on POD 4 during heparin administration complicated his hospitalization. Case 2: A diagnosis of cardiogenic shock caused by STEMI occurred in an 84-year-old male patient, who underwent PCI of the LAD with IABP support. Three days after PCI, echocardiography revealed VSR, and the patient underwent emergency VSR repair with two separate patches and BioGlue applied to the suture line between them. Before weaning from CPB, we implanted Impella 5.5 in the LV and added venoarterial extracorporeal membrane oxygenation (VA-ECMO) support for right heart failure. The postoperative echocardiography revealed no residual shunt.
CONCLUSIONS
Patients undergoing emergency surgery for mechanical complications of AMI may find Impella 5.5 to be an effective tool for LV unloading. The use of VA-ECMO in conjunction with Impella may be an effective strategy for managing VSR associated with concurrent right-sided heart failure.
Topics: Humans; Male; Aged; Heart-Assist Devices; Myocardial Infarction; Heart Ventricles; Heart Rupture, Post-Infarction; Ventricular Septal Rupture; Echocardiography; Postoperative Complications
PubMed: 38926884
DOI: 10.1186/s13019-024-02879-5 -
BMJ Open Jun 2024The treatment of patients with cardiogenic shock (CS) encompasses several health technologies including Impella pumps and venoarterial extracorporeal membrane... (Comparative Study)
Comparative Study
Impella versus VA-ECMO for the treatment of patients with cardiogenic shock: the Impella Network Project - observational study protocol for cost-effectiveness and budget impact analyses.
INTRODUCTION
The treatment of patients with cardiogenic shock (CS) encompasses several health technologies including Impella pumps and venoarterial extracorporeal membrane oxygenation (VA-ECMO). However, while they are widely used in clinical practice, information on resource use and quality of life (QoL) associated with these devices is scarce. The aim of this study is, therefore, to collect and comparatively assess clinical and socioeconomic data of Impella versus VA-ECMO for the treatment of patients with severe CS, to ultimately conduct both a cost-effectiveness (CEA) and budget impact (BIA) analyses.
METHODS AND ANALYSIS
This is a prospective plus retrospective, multicentre study conducted under the scientific coordination of the Center for Research on Health and Social Care Management of SDA Bocconi School of Management and clinical coordination of Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) San Raffaele Scientific Institute in Milan. The Impella Network stemmed for the purposes of this study and comprises 17 Italian clinical centres from Northern to Southern Regions in Italy. The Italian network qualifies as a subgroup of the international Impella Cardiac Surgery Registry. Patients with CS treated with Impella pumps (CP, 5.0 or 5.5) will be prospectively recruited, and information on clinical outcomes, resource use and QoL collected. Economic data will be retrospectively matched with data from comparable patients treated with VA-ECMO. Both CEA and BIA will be conducted adopting the societal perspective in Italy. This study will contribute to generate new socioeconomic evidence to inform future coverage decisions.
ETHICS AND DISSEMINATION
As of May 2024, most of the clinical centres submitted the documentation to their ethical committee (N=13; 76%), six centres received ethical approval and two centres started to enrol patients. Study results will be published in peer-reviewed publications and disseminated through conference presentations.
Topics: Humans; Shock, Cardiogenic; Cost-Benefit Analysis; Extracorporeal Membrane Oxygenation; Heart-Assist Devices; Prospective Studies; Retrospective Studies; Italy; Quality of Life; Multicenter Studies as Topic; Budgets; Observational Studies as Topic
PubMed: 38926145
DOI: 10.1136/bmjopen-2023-078358 -
BMJ Open Jun 2024Left ventricular assist devices (LVADs) have emerged as a successful treatment option for patients with end-stage heart failure. Compared with the best medical therapy,...
BACKGROUND
Left ventricular assist devices (LVADs) have emerged as a successful treatment option for patients with end-stage heart failure. Compared with the best medical therapy, LVADs improve survival and enhance functional capacity and quality of life. However, two major complications compromise this patient population's outcomes: thrombosis and bleeding. Despite technological innovations and better hemocompatibility, these devices alter the rheology, triggering the coagulation cascade and, therefore, require antithrombotic therapy. Anticoagulation and antiplatelet therapies represent the current standard of care. Still, inconsistency in the literature exists, especially whether antiplatelet therapy is required, whether direct oral anticoagulants can replace vitamin K antagonists and even whether phosphodiesterase type 5 inhibitors with their antithrombotic effects could be added to the regimen of anticoagulation.
METHODS AND ANALYSIS
We will perform a living systematic review with network meta-analysis and indirect comparison between current antithrombotic therapies, which have and have not been directly compared within clinical trials and observational studies. We will systematically search the following electronic sources: Cochrane Central Register of Controlled Trials (CENTRAL), Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE). We will exclusively examine studies published in English from 2016 to the present. Studies conducted before 2016 will be omitted since our primary focus is evaluating continuous flow devices. Two independent reviewers will assess the articles by title, abstract and full text; any disagreement will be resolved through discussion, and a third reviewer will be involved if necessary. The Cochrane Risk of Bias tool will be used to assess the risk of bias. We will then conduct a pairwise meta-analysis; if the assumption of transitivity is satisfied, we will proceed with network meta-analysis using Bayesian methodology.
ETHICS AND DISSEMINATION
Formal ethical approval is not required as no primary data are collected. This systematic review and network meta-analysis will delineate the risks of stroke, thromboembolic events, pump thrombosis, gastrointestinal bleeding and mortality in patients equipped with LVADs who are subjected to various antithrombotic regimens. The findings will be disseminated via a peer-reviewed publication and presented at conference meetings. This will enhance clinical practice and guide future research on anticoagulation strategies within this distinct patient cohort.
PROSPERO REGISTRATION NUMBER
CRD42023465288.
Topics: Humans; Heart-Assist Devices; Systematic Reviews as Topic; Network Meta-Analysis; Fibrinolytic Agents; Anticoagulants; Thrombosis; Heart Failure; Platelet Aggregation Inhibitors; Research Design; Hemorrhage
PubMed: 38925683
DOI: 10.1136/bmjopen-2023-080110 -
Sports (Basel, Switzerland) May 2024Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of... (Review)
Review
Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular health. Cardiovascular concerns, including sudden cardiac death, pose significant risks during sports activities. Traditional ECG, essential yet limited, often fails to distinguish between benign cardiac adaptations and serious conditions. This narrative review investigates the application of machine learning (ML) and deep learning (DL) in ECG interpretation, aiming to improve the detection of arrhythmias, channelopathies, and hypertrophic cardiomyopathies. A literature review over the past decade, sourcing from PubMed and Google Scholar, highlights the growing adoption of AI in sports medicine for its precision and predictive capabilities. AI algorithms excel at identifying complex cardiac patterns, potentially overlooked by traditional methods, and are increasingly integrated into wearable technologies for continuous monitoring. Overall, by offering a comprehensive overview of current innovations and outlining future advancements, this review supports sports medicine professionals in merging traditional screening methods with state-of-the-art AI technologies. This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs.
PubMed: 38921838
DOI: 10.3390/sports12060144 -
Cells Jun 2024Alkaptonuria (AKU) is a genetic disorder that affects connective tissues of several body compartments causing cartilage degeneration, tendon calcification, heart... (Review)
Review
Alkaptonuria (AKU) is a genetic disorder that affects connective tissues of several body compartments causing cartilage degeneration, tendon calcification, heart problems, and an invalidating, early-onset form of osteoarthritis. The molecular mechanisms underlying AKU involve homogentisic acid (HGA) accumulation in cells and tissues. HGA is highly reactive, able to modify several macromolecules, and activates different pathways, mostly involved in the onset and propagation of oxidative stress and inflammation, with consequences spreading from the microscopic to the macroscopic level leading to irreversible damage. Gaining a deeper understanding of AKU molecular mechanisms may provide novel possible therapeutical approaches to counteract disease progression. In this review, we first describe inflammation and oxidative stress in AKU and discuss similarities with other more common disorders. Then, we focus on HGA reactivity and AKU molecular mechanisms. We finally describe a multi-purpose digital platform, named ApreciseKUre, created to facilitate data collection, integration, and analysis of AKU-related data.
Topics: Alkaptonuria; Humans; Oxidative Stress; Homogentisic Acid; Inflammation; Animals
PubMed: 38920699
DOI: 10.3390/cells13121072 -
NPJ Digital Medicine Jun 2024The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an...
The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.
PubMed: 38918595
DOI: 10.1038/s41746-024-01170-0 -
Scientific Data Jun 2024Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging...
Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The 'CMRxRecon' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.
Topics: Humans; Algorithms; Deep Learning; Heart; Heart Diseases; Image Processing, Computer-Assisted; Magnetic Resonance Imaging
PubMed: 38918497
DOI: 10.1038/s41597-024-03525-4 -
Texas Heart Institute Journal Jun 2024
Topics: Humans; Hemodynamics; Heart-Assist Devices; Aorta; Models, Cardiovascular; Computer Simulation; Blood Vessel Prosthesis Implantation; Heart Failure
PubMed: 38917113
DOI: 10.14503/THIJ-24-8472 -
Frontiers in Medicine 2024[This corrects the article DOI: 10.3389/fmed.2024.1285067.].
[This corrects the article DOI: 10.3389/fmed.2024.1285067.].
PubMed: 38915763
DOI: 10.3389/fmed.2024.1431299 -
BMC Medical Informatics and Decision... Jun 2024With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI)...
With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .
Topics: Humans; COVID-19; Artificial Intelligence; Early Diagnosis; Heart Rate; Wearable Electronic Devices
PubMed: 38915001
DOI: 10.1186/s12911-024-02576-2