Pulmonary Embolism and Heart Failure: A Reappraisal

Arrigo, Mattia; Huber, Lars C

doi:10.15420/cfr.2020.26

Cited by 15 publications

(9 citation statements)

References 47 publications

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“…The optimized network architecture has two hidden layers with dimension of 50 nodes and 80 nodes for layer 1 and layer 2, respectively, with sigmoid activation for both layers and a learning rate of 0.01, with the top three features being EF, prior CAD, and admission type. Indeed, pulmonary embolism and acute heart failure are known to be present concomitantly [32], which agrees with the clinical observations suggesting that the relative risk of pulmonary embolism is at least double to that of patients without heart failure and increases as LV systolic function declines [33], hence correlating well with EF. The feature importance score and ROC curves for the classifier evaluated using FS1-FS7 are shown in the Supplementary Materials Figure S4A-G and in the Supplementary Materials Figure S9, respectively.…”

Section: Pulmonary Embolismsupporting

confidence: 87%

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

et al. 2022

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Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972), heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.

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Section: Pulmonary Embolismsupporting

confidence: 87%

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

et al. 2022

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“…Development of this system can expand the application to the most common vascular episodes: myocardial infarction and cerebral stroke. PE can be manifested in various scenarios, from irrelevant small artery occlusions, through the embolism with acute clinical symptoms and finally to the life threatening massive disorder requiring immediate actions [16], [18]. A typical reference test for the diagnosis of acute PE, considered as the most reliable one, is called Computed Tomographic Pulmonary Angiography (CTPA), where a contrast agent is injected into a pulmonary artery to identify the blocked vessels (the contrast is unable to fill occluded arteries) [19], [20].…”

Section: Medical Applicationsmentioning

confidence: 99%

Bridging Nano and Body Area Networks: A Full Architecture for Cardiovascular Health Applications

Asorey-Cacheda

Correia

Garcia-Pardo

et al. 2023

IEEE Internet Things J.

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Cardiovascular events occurring in the bloodstream are responsible for about 40% of human deaths in developed countries. Motivated by this fact, we present a new global network architecture for a system for the diagnosis and treatment of cardiovascular events, focusing on problems related to pulmonary artery occlusion, i.e., situations of artery blockage by a blood clot. The proposed system is based on bio-sensors for detection of artery blockage and bio-actuators for releasing appropriate medicines, both types of devices being implanted in pulmonary arteries. The system can be used by a person leading an active life and provides bidirectional communication with medical personnel via nano-nodes circulating in the bloodstream constituting an in-body area network. We derive an analytical model for calculating the required number of nano-nodes to detect artery blockage and the probability of activating a bioactuator. We also analyze the performance of the body area component of the system in terms of path loss and of wireless links budget. Results show that the system can diagnose a blocked artery in about 3 hours and that after another 3 hours medicines can be released in the exact spot of the artery occlusion, while with current medical practices the average time for diagnosis varies between 5 to 9 days.

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“…Pulmonary embolism is a congestion of the major artery of the lungs and its branches caused by a blood clot (embolism), most commonly as a result of a deep vein thrombosis (DVT) complication. Due to the blockage of blood flow by the clot, the pulmonary pressure increases, which leads to high pressure in the right ventricles and prompts heart failure by ventricular dysfunction (cor pulmonale) [ 10 ]. Due to the variable clinical presentation of pulmonary embolism, it is often misinterpreted as a myocardial infarction because both of them present with similar symptoms of dyspnea, tachypnea, and chest pain [ 11 ].…”

Section: Reviewmentioning

confidence: 99%

ST-Segment Elevation in Conditions of Non-cardiovascular Origin Mimicking an Acute Myocardial Infarction: A Narrative Review

Khurana¹,

Ranjan²

2022

Cureus

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The most widespread presenting ailments among patients visiting the emergency department are chest pain and shortness of breath. These symptoms lead any doctor to a probable diagnosis of myocardial infarction (MI). Detailed patient history, testing of blood samples for cardiac biomarkers that are indicative of cardiovascular necrosis, ultrasound methods, electrocardiography, and coronary computed tomography (CT) could all be beneficial to support the diagnosis. Out of these, electrocardiography is the most important and commonly done investigation in the emergency departments for patients presenting with chest pain and shortness of breath. However, interpreting these patients' electrocardiograms (ECGs) may be a matter of concern and worry. T wave and ST-segment changes are often of interest in the early signs of myocardial ischemia. Despite its incredible sensitivity, ST-segment deviation (elevated or depressed) has a low specificity because it can be seen in a variety of other cardiac and non-cardiac diseases. When ST-segment anomalies are identified, clinicians must consider many additional characteristics (such as risk factors, symptoms, and anamnesis), as well as all other possible diagnoses.All of these scenarios of patients presenting in the emergency department with chest discomfort and shortness of breath showing ST-segment abnormalities can leave a healthcare professional wondering whether to start treatment for acute myocardial infarction, through either the administration of a fibrinolytic agent, exposing patients to both the benefits and risks of fibrinolysis, or invasive coronary angiography. An astute physician may be able to recognize fabricated differential diagnosis mimicking STsegment elevation myocardial infarction (STEMI) in some situations. Failure to recognize these imposters can result in inefficient resource utilization, which can expose patients to unjustified risk and increased rather than decreased death and morbidity. Since the danger of cerebral hemorrhage from blood thinners is significant, in patient-care scenarios, in order to rule out percutaneous coronary intervention (PCI), a thorough assessment of the ECG is essential to consider diseases other than acute myocardial infarction, especially the ones that are non-cardiac in origin. The goal of this narrative review is to give an overview of the significant disorders that are non-cardiac in origin that can mimic an ST-segment elevation myocardial infarction (STEMI).

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Pulmonary Embolism and Heart Failure: A Reappraisal

Cited by 15 publications

References 47 publications

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit

Bridging Nano and Body Area Networks: A Full Architecture for Cardiovascular Health Applications

ST-Segment Elevation in Conditions of Non-cardiovascular Origin Mimicking an Acute Myocardial Infarction: A Narrative Review

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