Hasil untuk "Diseases of the circulatory (Cardiovascular) system"

Dongsuk Jang, Ziyao Shangguan, Kyle Tegtmeyer et al.

The learning process for medical residents presents significant challenges, demanding both the ability to interpret complex case reports and the rapid acquisition of accurate medical knowledge from reliable sources. Residents typically study case reports and engage in discussions with peers and mentors, but finding relevant educational materials and evidence to support their learning from these cases is often time-consuming and challenging. To address this, we introduce MedTutor, a novel system designed to augment resident training by automatically generating evidence-based educational content and multiple-choice questions from clinical case reports. MedTutor leverages a Retrieval-Augmented Generation (RAG) pipeline that takes clinical case reports as input and produces targeted educational materials. The system's architecture features a hybrid retrieval mechanism that synergistically queries a local knowledge base of medical textbooks and academic literature (using PubMed, Semantic Scholar APIs) for the latest related research, ensuring the generated content is both foundationally sound and current. The retrieved evidence is filtered and ordered using a state-of-the-art reranking model and then an LLM generates the final long-form output describing the main educational content regarding the case-report. We conduct a rigorous evaluation of the system. First, three radiologists assessed the quality of outputs, finding them to be of high clinical and educational value. Second, we perform a large scale evaluation using an LLM-as-a Judge to understand if LLMs can be used to evaluate the output of the system. Our analysis using correlation between LLMs outputs and human expert judgments reveals a moderate alignment and highlights the continued necessity of expert oversight.

Gyeo-Re Han, Merve Eryilmaz, Artem Goncharov et al.

Rapid and accessible cardiac biomarker testing is essential for the timely diagnosis and risk assessment of myocardial infarction (MI) and heart failure (HF), two interrelated conditions that frequently coexist and drive recurrent hospitalizations with high mortality. However, current laboratory and point-of-care testing systems are limited by long turnaround times, narrow dynamic ranges for the tested biomarkers, and single-analyte formats that fail to capture the complexity of cardiovascular disease. Here, we present a deep learning-enhanced dual-mode multiplexed vertical flow assay (xVFA) with a portable optical reader and a neural network-based quantification pipeline. This optical sensor integrates colorimetric and chemiluminescent detection within a single paper-based cartridge to complementarily cover a large dynamic range (spanning ~6 orders of magnitude) for both low- and high-abundance biomarkers, while maintaining quantitative accuracy. Using 50 uL of serum, the optical sensor simultaneously quantifies cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) within 23 min. The xVFA achieves sub-pg/mL sensitivity for cTnI and sub-ng/mL sensitivity for CK-MB and NT-proBNP, spanning the clinically relevant ranges for these biomarkers. Neural network models trained and blindly tested on 92 patient serum samples yielded a robust quantification performance (Pearson's r > 0.96 vs. reference assays). By combining high sensitivity, multiplexing, and automation in a compact and cost-effective optical sensor format, the dual-mode xVFA enables rapid and quantitative cardiovascular diagnostics at the point of care.

Jumpei Ohashi, Tatsuya Hayashi, Shingo Yamamoto et al.

Abstract Background In pulmonary vein isolation (PVI) for atrial fibrillation (AF), intraoperative defibrillation is often required. Intracardiac defibrillation catheters (ICDCs) are most effective when positioned to enclose the heart between the coronary sinus (CS) and right atrium (RA) (CS/RA configuration). However, achieving this positioning via the inferior vena cava (IVC) can be challenging, and alternative configurations remain underexplored. Methods This study included patients with paroxysmal or persistent AF who underwent cryoballoon ablation followed by intracardiac cardioversion using an ICDC via the IVC. The catheter was initially positioned with distal electrodes in the CS and proximal electrodes in the IVC (CS‐only configuration). If cardioversion failed, the catheter was repositioned to place distal electrodes in the superior vena cava (SVC configuration). A maximum of 30 J of energy was used for all cardioversion attempts. Results A total of 81 patients were included. Cardioversion in the CS‐only configuration restored sinus rhythm in 11% (9/81) of patients. Repositioning to the SVC configuration achieved successful cardioversion in 93.1% (67/72) of the remaining cases without complications. Patients requiring the SVC configuration had a significantly higher prevalence of persistent AF (33.3% vs. 80.6%; p = 0.045). No adverse events were observed following cardioversion in the SVC configuration. Conclusions While the CS‐only configuration offers ease of placement, its efficacy is limited. Repositioning to the SVC configuration significantly enhances cardioversion success and represents a safer, more effective alternative for ICDC use during AF ablation.

Pankaj Garg, Mostafa Ali, Mohammad Alomari et al.

Abstract Cardiac benign metastatic leiomyoma (BML) is a rare cardiac tumor that is usually asymptomatic, frequently misdiagnosed and may result in serious complications, including embolization, heart failure and death. This review highlights the importance of considering cardiac BML in the differential diagnosis of cardiac masses, especially in women with a history of uterine leiomyomas. This review summarizes the current knowledge about cardiac BML, including its demographics, clinical presentation, etio-pathogenesis, diagnosis, and management. The authors discuss the challenges associated with diagnosing cardiac BML and emphasize the importance of a thorough history, physical examination, and imaging studies. They also review the different treatment options for cardiac BML, including surgical resection and role of medical and surgical castration. Early diagnosis and management of cardiac BML is crucial to prevent complications. This review provides valuable insights for clinicians who may encounter this rare condition. By raising awareness of cardiac BML and its management strategies, this review can improve patient care and outcomes.

Paula-Anca Sulea, Ioan Tilea, Florin Stoica et al.

Background: Age-related vascular stiffening increases cardiovascular risk by altering ventricular–arterial coupling (VAC). Physical activity, a modifiable factor, may improve cardiovascular health. This pilot study evaluated the relationship between physical activity evaluation and VAC, measured by the carotid–femoral pulse wave velocity to global longitudinal strain (cfPWV/GLS) ratio, in a Romanian primary care cohort. Methods: The prospective cohort analysis was performed on 81 adults (49 females, mean age 50.27 ± 12.93 years). Physical activity was quantified through anamnesis using metabolic equivalents (METs) according with Compendium of Physical Activities, and patients were stratified into four groups: G1 (METs < 1.5, <i>n</i> = 39), G2 (METs = 1.5–2.9, <i>n</i> = 2), G3 (METs = 3–5.9, <i>n</i> = 23), and G4 (METs ≥ 6, <i>n</i> = 17). Demographic and echocardiographic data were recorded to explore associations between physical activity and VAC. Results: The cfPWV/GLS ratio differed significantly across groups (<i>p</i> = 0.012), with the lowest values present in the moderate-intensity group (G3). VAC ≥ 0.391 can predict sedentary lifestyles (AUC = 0.730; CI: 0.617–0.833, <i>p</i> > 0.001). Multivariate analysis revealed that age, arterial age, and hypertension independently predict VAC. Conclusions: Higher physical activity is inversely associated with VAC (cfPWV/GLS ratio) and can predict sedentary lifestyles. Encouraging moderate-to-vigorous exercise in primary care may improve cardiovascular function and aid prevention.

Christian Basile, Stefan D. Anker, Gianluigi Savarese

Heart failure with reduced ejection fraction (HFrEF) exhibits significant sex-based differences in clinical presentation, management, and outcomes. This study aimed to evaluate these differences using data from the Swedish Heart Failure Registry (SwedeHF). We analyzed 65,605 patients with HFrEF (EF <40%) from the SwedeHF registry. Baseline characteristics, treatment patterns, and outcomes were compared between females and males. Multivariable logistic regression was used to evaluate predictors of treatment use. Cox proportional hazards models were used to assess the risk of cardiovascular mortality and heart failure (HF) hospitalization, adjusting for demographic and clinical variables. Odds ratios (OR) were reported for treatment use, and hazard ratios (HR) were used for outcome analyses. Females (29.0%) were older than males and had a higher prevalence of hypertension (61.3% vs 49.8%) and valvular disease (17.2% vs 11.1%), while males had a higher prevalence of ischemic heart disease (70.5% vs 40.1%) and diabetes (31.6% vs 28.4%). Males were less likely to receive beta-blockers (OR: 0.76, 95% CI 0.71-0.81), and more likely to receive sodium-glucose co-transporter-2 inhibitors (OR: 1.27, 95% CI 1.17-1.38) and implantable cardioverter-defibrillators/cardiac resynchronization therapy (OR: 1.41, 95% CI 1.30-1.52). During a median follow-up of 2.1 years, males had a higher risk of the composite outcome of cardiovascular death or HF hospitalization (HR: 1.19, 95% CI 1.16-1.22), cardiovascular death (HR: 1.33, 95% CI 1.28-1.37), and HF hospitalization (HR: 1.16, 95% CI 1.12-1.19). In this large cohort of patients with HFrEF, males had worse outcomes across all major cardiovascular endpoints. These findings highlight the need for tailored strategies to address sex-based disparities in HF management and improve outcomes for both sexes.

Michael J. Domanski, MD, Colin O. Wu, PhD, Xin Tian, PhD et al.

Background: In prior studies of cumulative risk factor exposure, self-identified race was independently associated with incident cardiovascular disease (CVD). A recent study suggests clinical, demographic, and socioeconomic factors explain racial differences. We used propensity score matching to study race as an independent incident CVD risk factor. Objectives: The purpose of this study was to assess race as an independent risk factor for incident CVD. Methods: We analyzed CARDIA (Coronary Artery Risk Development in Young Adults) study data using propensity score matching of White and Black women, and, separately, White and Black men, with respect to known CVD risk factors. Results: Black men (n = 487), compared to White men (n = 487), had higher risk of CVD (HR: 2.30; 95% CI: 1.36-3.89; P = 0.0014), stroke (HR: 5.00; 95% CI: 1.45-17.3; P = 0.0047), and congestive heart failure (CHF) (HR: 3.60; 95% CI: 1.34-9.70; P = 0.0067). Black women (n = 640), compared to White women (n = 640), had higher CVD risk (HR: 2.36; 95% CI: 1.17-4.78; P = 0.014) and stroke risk (HR: 2.80; 95% CI: 1.01-7.77; P = 0.039) and borderline significantly higher CHF risk (HR: 3.50; 95% CI: 0.73-16.9; P = 0.096). Risk of coronary heart disease did not differ significantly by race in either sex. Multivariable analyses showed racial differences in the associations of multiple risk factors with incident CVD events independent of other known CVD risk factors. Conclusions: Propensity score matching analyses demonstrate that race is an independent risk factor for incident CVD and its components, CHF, and stroke. Multivariable analyses suggest racial differences in Black vs White risk factor impact as the possible cause. Reasons for these differences remain to be explored.

Giacomo Buso, Thomas Weber, Christos Fragoulis et al.

Background Acute blood pressure (BP) elevations are common in emergency settings and are traditionally classified into hypertensive urgencies (HU) and hypertensive emergencies (HE). Malignant hypertension (MHT) represents a severe form of HE characterised by small vessel damage. Although international guidelines provide clear definitions and treatment strategies, real-world data have shown persistent fragmentation and heterogeneity in the diagnosis and management of these patients.Methods A web-based, anonymous survey promoted by the European Society of Hypertension (ESH) was distributed among physicians from 18 European and 4 non-European countries. The questionnaire assessed definitions, diagnostic work-up, BP measurement practices, and therapeutic strategies for HU, HE, and MHT.Results Sixty–four participants in 56 centres completed the survey. HU was correctly defined as a severe BP elevation without acute clinically symptomatic hypertension-mediated organ damage (A-HMOD) by 45.3% of respondents. Small cuffs were available to 79.7% and extra-large cuffs to 70.3% of respondents.. Intravenous antihypertensive therapy was used for HE by 88.7% of participants, while 20.6% also used intravenous drugs for HU. Parenteral clonidine and sublingual nifedipine were prescribed by 29.7% and 26.6% of respondents, respectively. Definitions and therapeutic approaches for MHT varied substantially, with 62.9% adopting a recently proposed definition involving at least three target organ damages in patients with BP >200/120 mmHg.Conclusions This international survey highlights considerable variability in the definition, diagnostic work-up, and therapeutic management of acute BP elevations, emphasising the need for harmonised protocols and further education.

Shanti M. Pinto, Bhaskar Thakur, Raj G. Kumar et al.

OBJECTIVE To characterize factors associated with death due to cardiovascular causes following complicated mild to severe traumatic brain injury (TBI). SETTING Chart review or telephonic interviews. PARTICIPANTS Participants enrolled in the TBI Model Systems database. DESIGN Retrospective. MAIN MEASURES Primary cause of death due to cardiovascular causes coded with ICD-9 codes 390-459 or ICD-10 codes 100-199 (diseases of the circulatory system) on death certificates. A competing risk cause-specific Cox proportional hazards regression analysis was completed to identify demographic and injury-related factors associated with increased risk of cardiovascular-related mortality. RESULTS Overall, 15,370 participants were included. Overall, 2,770 (18.0%) individuals died, of which 595 (21.5%) died due to cardiovascular-related causes. Those who died due to cardiovascular causes were older (hazard ratio [HR] 1.08, 95% 1.07-1.09, P 30 days (HR 0.71, 95% CI 0.55-0.92, P = .010) were less likely to die due to cardiovascular causes. Alcohol or drug use and education level were not significantly associated with death due to cardiovascular causes. CONCLUSION Over 1 in 5 deaths following TBI were due to cardiovascular causes. Older age, male sex, being divorced, and having lower FIM motor scores are risk factors, whereas being employed, having private health insurance, and PTA >30 days are protective factors for cardiovascular mortality.

A. Marengo, Alessandro Pagano, V. Santamato

Conditions affecting the circulatory system and blood vessels are referred to as cardiovascular diseases that include strokes and heart attacks. Internet of Things (IoT) technologies monitor health metrics, identify irregularities and enable remote patient care, resulting in earlier intervention and more individualized therapy. This research aims to establish an efficient cardiovascular disease prediction model through Artificial intelligence (AI)-driven IoT technology. We propose a novel Shuffled Frog leaping-tuned Iterative Improved Adaptive Boosting (SF-IIAdaboost) algorithm for predicting cardiovascular disease with the implementation of IoT device data. IoT medical sensors and wearable devices will collect the patient's clinical data in our proposed framework. Z-score normalization is used to preprocess the gathered data and optimize its quality. Kernel principal component analysis (Kernel-PCA) extracts the relevant features from the processed data. We obtained a dataset that contains various health data gathered from numerous sensing devices to train our recommended model. Our proposed methodology is implemented using Python software. During the evaluation phase, we assess the effectiveness of our model across different parameters. We conduct comparative analyses against conventional methods to ascertain the superiority of our approach. Experimental findings demonstrate the superior performance of our recognition method over traditional approaches. The proposed SF-IIAdaboost algorithm, integrated with IoT device data, presents a promising avenue for predicting cardiovascular disease. The SF-IIAdaboost model demonstrated notable enhancements, attaining 95.37 % accuracy, 93.51 % precision, 94.3 % sensitivity, 96.31 % specificity, and 95.72 % F-measure. Future developments are predicted to involve computing on the edge, where immediate evaluations can be performed in the edge layer to avoid the basic constraints of the clouds, such as high latency, utilization of bandwidth and performing the growth of IoT data. Edge computing can revolutionize the healthcare industry's efficacy by enabling providers to make flexible decisions, operate quickly, and accurately anticipate diseases. It can improve the average level of service standards.

Mingliang Tan, Y. Yeo, Noreen Mirza et al.

Background Despite significant cardiac involvement in sarcoidosis, real‐world data on death due to cardiovascular disease among patients with sarcoidosis is not well established. Methods and Results We queried the Centers for Disease Control and Prevention's Wide‐Ranging Online Data for Epidemiologic Research database for data on patients with sarcoidosis aged ≥25 years from 1999 to 2020. Diseases of the circulatory system except ischemic heart disease were listed as the underlying cause of death, and sarcoidosis was stated as a contributing cause of death. We calculated age‐adjusted mortality rate (AAMR) per 1 million individuals and determined the trends over time by estimating the annual percentage change using the Joinpoint Regression Program. Subgroup analyses were performed on the basis of demographic and geographic factors. In the 22‐year study period, 3301 cardiovascular deaths with comorbid sarcoidosis were identified. The AAMR from cardiovascular deaths with comorbid sarcoidosis increased from 0.53 (95% CI, 0.43–0.65) per 1 million individuals in 1999 to 0.87 (95% CI, 0.75–0.98) per 1 million individuals in 2020. Overall, women recorded a higher AAMR compared with men (0.77 [95% CI, 0.74–0.81] versus 0.58 [95% CI, 0.55–0.62]). People with Black ancestry had higher AAMR than people with White ancestry (3.23 [95% CI, 3.07–3.39] versus 0.39 [95% CI, 0.37–0.41]). A higher percentage of death was seen in the age groups of 55 to 64 years in men (23.11%) and women (21.81%), respectively. In terms of US census regions, the South region has the highest AAMR from cardiovascular deaths with comorbid sarcoidosis compared with other regions (0.78 [95% CI, 0.74–0.82]). Conclusions The increase of AAMR from cardiovascular deaths with comorbid sarcoidosis and higher cardiovascular mortality rates among adults aged 55 to 64 years highlight the importance of early screening for cardiovascular diseases among patients with sarcoidosis.

Dirk Douwes-Schultz, Alexandra M. Schmidt, Laís Picinini Freitas et al.

Univariate zero-inflated models are increasingly being used to account for excess zeros in spatio-temporal infectious disease counts. However, the multivariate case is challenging due to the need to account for correlations across space, time and disease in both the count and zero-inflated components of the model. We are interested in comparing the transmission dynamics of several co-circulating infectious diseases across space and time, where some of the diseases can be absent for long periods. We first assume there is a baseline disease that is well-established and always present in the region. The other diseases switch between periods of presence and absence in each area through a series of coupled Markov chains, which account for long periods of disease absence, disease interactions and disease spread from neighboring areas. Since we are mainly interested in comparing the diseases, we assume the cases of the present diseases in an area jointly follow an autoregressive multinomial model. We use the multinomial model to investigate whether there are associations between certain factors, such as temperature, and differences in the transmission intensity of the diseases. Inference is performed using efficient Bayesian Markov chain Monte Carlo methods based on jointly sampling all unknown presence indicators. We apply the model to spatio-temporal counts of dengue, Zika, and chikungunya cases in Rio de Janeiro, during the first triple epidemic there.

Ju-Hyeon Nam, Seo-Hyung Park, Su Jung Kim et al.

An electrocardiogram (ECG) captures the heart's electrical signal to assess various heart conditions. In practice, ECG data is stored as either digitized signals or printed images. Despite the emergence of numerous deep learning models for digitized signals, many hospitals prefer image storage due to cost considerations. Recognizing the unavailability of raw ECG signals in many clinical settings, we propose VizECGNet, which uses only printed ECG graphics to determine the prognosis of multiple cardiovascular diseases. During training, cross-modal attention modules (CMAM) are used to integrate information from two modalities - image and signal, while self-modality attention modules (SMAM) capture inherent long-range dependencies in ECG data of each modality. Additionally, we utilize knowledge distillation to improve the similarity between two distinct predictions from each modality stream. This innovative multi-modal deep learning architecture enables the utilization of only ECG images during inference. VizECGNet with image input achieves higher performance in precision, recall, and F1-Score compared to signal-based ECG classification models, with improvements of 3.50%, 8.21%, and 7.38%, respectively.

Sheikh Mohammed Shariful Islam, Moloud Abrar, Teketo Tegegne et al.

Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely treatment and management. Although population-based CVD risk models have been used traditionally, these models often do not consider variations in lifestyles, socioeconomic conditions, or genetic predispositions. Therefore, we aimed to develop machine learning models for CVD detection using primary healthcare data, compare the performance of different models, and identify the best models. We used data from the UK Biobank study, which included over 500,000 middle-aged participants from different primary healthcare centers in the UK. Data collected at baseline (2006--2010) and during imaging visits after 2014 were used in this study. Baseline characteristics, including sex, age, and the Townsend Deprivation Index, were included. Participants were classified as having CVD if they reported at least one of the following conditions: heart attack, angina, stroke, or high blood pressure. Cardiac imaging data such as electrocardiogram and echocardiography data, including left ventricular size and function, cardiac output, and stroke volume, were also used. We used 9 machine learning models (LSVM, RBFSVM, GP, DT, RF, NN, AdaBoost, NB, and QDA), which are explainable and easily interpretable. We reported the accuracy, precision, recall, and F-1 scores; confusion matrices; and area under the curve (AUC) curves.

Ken Zhao, Sam Y. Son, Debkumar Sarkar et al.

Abstract Sinistral portal hypertension, also known as left-sided portal hypertension, is a rare cause of gastric variceal bleeding which occurs secondary to occlusion of the splenic vein. We present a case of venous occlusion and sinistral portal hypertension secondary to distal pancreatic cancer requiring treatment of gastric variceal bleeding. After failing conservative management, transvenous intervention was attempted, but a venous communication with the gastric varices was unable to be identified on multiple venograms. A percutaneous trans-splenic approach using a 21-G needle and ultrasound guidance was successful in directly accessing an intraparenchymal vein feeding the gastric varices, and glue embolization was performed directly through the access needle with excellent results.

Paul Valensi

Abstract Prevalence of heart failure (HF) and diabetes are markedly increasing globally. In a population of HF patients, approximately 40% have diabetes which is associated with a more severe HF, poorer cardiovascular outcomes and higher hospitalization rates for HF than HF patients without diabetes. Similar trends were shown in HF patients with prediabetes. In addition, the association between HF and renal function decline was demonstrated in patients with or without diabetes. However, the exact prevalence of dysglycemia in HF patients requires further investigation aiming to clarify the most accurate test to detect dysglycemia in this population. The relationship between HF and diabetes is complex and probably bidirectional. In one way, patients with diabetes have a more than two-fold risk of developing incident HF with reduced or preserved ejection fraction than those without diabetes. In the other way, patients with HF, when compared with those without HF, show an increased risk for the onset of diabetes due to several mechanisms including insulin resistance (IR), which makes HF emerging as a precursor for diabetes development. This article provides epidemiological evidence of undetected dysglycemia (prediabetes or diabetes) in HF patients and reviews the pathophysiological mechanisms which favor the development of IR and the risks associated with these disorders in HF patients. This review also offers a discussion of various strategies for the prevention of diabetes in HF patients, based first on fasting plasma glucose and HbA1c measurement and if normal on an oral glucose tolerance test as diagnostic tools for prediabetes and unknown diabetes that should be performed more extensively in those patients. It discusses the implementation of diabetes prevention measures and well-structured management programs for HF patients who are generally overweight or obese, as well as current pharmacotherapeutic options for prediabetes, including sodium–glucose cotransporter 2 inhibitors which are among the pillars of HF treatment and which recently showed a benefit in the reduction of incident diabetes in HF patients. Thus, there is an urgent need of routine screening for dysglycemia in all HF patients, which should contribute to reduce the incidence of diabetes and to treat earlier diabetes when already present.

Aleksanteri Aspelund, M. Robciuc, Sinem Karaman et al.

Varunsiri Atti, Division of Cardiovascular Diseases, West Virginia University Heart and Vascular Institute, Morgantown, WV, USA, Mahesh Anantha Narayanan et al.

Treatment strategies to combat cardiogenic shock (CS) have remained stagnant over the past decade. Mortality rates among patients who suffer CS after acute myocardial infarction (AMI) remain high at 50%. Mechanical circulatory support (MCS) devices have evolved as novel treatment strategies to restore systemic perfusion to allow cardiac recovery in the short term, or as durable support devices in refractory heart failure in the long term. Haemodynamic parameters derived from right heart catheterization assist in the selection of an appropriate MCS device and escalation of mechanical support where needed. Evidence favouring the use of one MCS device over another is scant. An intra-aortic balloon pump is the most commonly used short-term MCS device, despite providing only modest haemodynamic support. Impella CP® has been increasingly used for CS in recent times and remains an important focus of research for patients with AMI-CS. Among durable devices, Heartmate® 3 is the most widely used in the USA. Adequately powered randomized controlled trials are needed to compare these MCS devices and to guide the operator for their use in CS. This article provides a brief overview of the types of currently available MCS devices and the indications for their use.

Huy Pham, Konstantin Egorov, Alexey Kazakov et al.

Cardiovascular disease remains a significant problem in modern society. Among non-invasive techniques, the electrocardiogram (ECG) is one of the most reliable methods for detecting abnormalities in cardiac activities. However, ECG interpretation requires expert knowledge and it is time-consuming. Developing a novel method to detect the disease early could prevent death and complication. The paper presents novel various approaches for classifying cardiac diseases from ECG recordings. The first approach suggests the Poincare representation of ECG signal and deep-learning-based image classifiers (ResNet50 and DenseNet121 were learned over Poincare diagrams), which showed decent performance in predicting AF (atrial fibrillation) but not other types of arrhythmia. XGBoost, a gradient-boosting model, showed an acceptable performance in long-term data but had a long inference time due to highly-consuming calculation within the pre-processing phase. Finally, the 1D convolutional model, specifically the 1D ResNet, showed the best results in both studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and 71%, respectively, and that was superior to the first-ranking solution of each challenge. The paper also investigated efficiency metrics such as power consumption and equivalent CO2 emissions, with one-dimensional models like 1D CNN and 1D ResNet being the most energy efficient. Model interpretation analysis showed that the DenseNet detected AF using heart rate variability while the 1DResNet assessed AF pattern in raw ECG signals.

Jonayet Miah, Duc M Ca, Md Abu Sayed et al.

Cardiovascular disease remains a leading cause of mortality in the contemporary world. Its association with smoking, elevated blood pressure, and cholesterol levels underscores the significance of these risk factors. This study addresses the challenge of predicting myocardial illness, a formidable task in medical research. Accurate predictions are pivotal for refining healthcare strategies. This investigation conducts a comparative analysis of six distinct machine learning models: Logistic Regression, Support Vector Machine, Decision Tree, Bagging, XGBoost, and LightGBM. The attained outcomes exhibit promise, with accuracy rates as follows: Logistic Regression (81.00%), Support Vector Machine (75.01%), XGBoost (92.72%), LightGBM (90.60%), Decision Tree (82.30%), and Bagging (83.01%). Notably, XGBoost emerges as the top-performing model. These findings underscore its potential to enhance predictive precision for coronary infarction. As the prevalence of cardiovascular risk factors persists, incorporating advanced machine learning techniques holds the potential to refine proactive medical interventions.