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

Muhammad Hammad Maqsood, Mubashir Sajid, Khubaib Ahmed et al.

This research paper outlines the development and implementation of a novel Clinical Decision Support System (CDSS) that integrates AI predictive modeling with medical knowledge bases. It utilizes the quantifiable information elements in lab results for inferring likely diagnoses a patient might have. Subsequently, suggesting investigations to confirm the likely diagnoses -- an assistive tool for physicians. The system fuses knowledge contained in a rule-base expert system with inferences of data driven predictors based on the features in labs. The data for 593,055 patients was collected from 547 primary care centers across the US to model our decision support system and derive Real-Word Evidence (RWE) to make it relevant for a large demographic of patients. Our Rule-Base comprises clinically validated rules, modeling 59 health conditions that can directly confirm one or more of diseases and assign ICD-10 codes to them. The Likely Diagnosis system uses multi-class classification, covering 37 ICD-10 codes, which are grouped together into 11 categories based on the labs that physicians prescribe to confirm the diagnosis. This research offers a novel system that assists a physician by utilizing medical profile of a patient and routine lab investigations to predict a group of likely diseases and then confirm them, coupled with providing explanations for inferences, thereby assisting physicians to reduce misdiagnosis of patients in clinical decision-making.

Rohit Kaushik, Eva Kaushik

Cardiovascular disease (CVD) continues to be the major cause of death globally, calling for predictive models that not only handle diverse and high-dimensional biomedical signals but also maintain interpretability and privacy. We create a single multimodal learning framework that integrates cross modal transformers with graph neural networks and causal representation learning to measure personalized CVD risk. The model combines genomic variation, cardiac MRI, ECG waveforms, wearable streams, and structured EHR data to predict risk while also implementing causal invariance constraints across different clinical subpopulations. To maintain transparency, we employ SHAP based feature attribution, counterfactual explanations and causal latent alignment for understandable risk factors. Besides, we position the design in a federated, privacy, preserving optimization protocol and establish rules for convergence, calibration and uncertainty quantification under distributional shift. Experimental studies based on large-scale biobank and multi institutional datasets reveal state discrimination and robustness, exhibiting fair performance across demographic strata and clinically distinct cohorts. This study paves the way for a principled approach to clinically trustworthy, interpretable and privacy respecting CVD prediction at the population level.

Volodymyr Sydorskyi, Igor Krashenyi, Oleksii Yakubenko

Melanoma detection is vital for early diagnosis and effective treatment. While deep learning models on dermoscopic images have shown promise, they require specialized equipment, limiting their use in broader clinical settings. This study introduces a multi-modal melanoma detection system using conventional photo images, making it more accessible and versatile. Our system integrates image data with tabular metadata, such as patient demographics and lesion characteristics, to improve detection accuracy. It employs a multi-modal neural network combining image and metadata processing and supports a two-step model for cases with or without metadata. A three-stage pipeline further refines predictions by boosting algorithms and enhancing performance. To address the challenges of a highly imbalanced dataset, specific techniques were implemented to ensure robust training. An ablation study evaluated recent vision architectures, boosting algorithms, and loss functions, achieving a peak Partial ROC AUC of 0.18068 (0.2 maximum) and top-15 retrieval sensitivity of 0.78371. Results demonstrate that integrating photo images with metadata in a structured, multi-stage pipeline yields significant performance improvements. This system advances melanoma detection by providing a scalable, equipment-independent solution suitable for diverse healthcare environments, bridging the gap between specialized and general clinical practices.

Antonietta Gigante, Rosa Cascone, Chiara Pellicano et al.

Background: Serum uric acid and serum creatinine ratio (SUA/sCr) is strongly linked to increased cardiovascular risk. Atherosclerotic renal artery stenosis (ARAS) is a secondary cause of hypertension and is associated with ischemic nephropathy, congestive heart failure, accelerated cardiovascular disease, and autonomic dysfunction. The aim of this study was to investigate whether SUA levels and SUA/sCr could represent markers of autonomic dysfunction and increased left ventricular mass index (LVMI) in patients with ARAS. Methods: Patients diagnosed with ARAS were enrolled in the study. All patients underwent clinical evaluation, biochemical analysis, 24 h electrocardiogram (ECG), and Renal Doppler Ultrasound with renal resistive index parameters. Heart rate variability for global autonomic dysfunction was assessed through the analysis of a 24 h ECG to detect the standard deviation of normal-to-normal RR intervals (SDNN). Echocardiographic measurement of LVMI was performed. Results: A total of 27 patients (F = 16 (59%), median age 67 years (IQR 60–77)) diagnosed with ARAS were enrolled in the study. We found a statistically significant negative linear correlation between SUA/sCr and SDNN (r = −0.519, <i>p</i> < 0.01). We found a statistically significant positive linear correlation between SUA/sCr and LVMI (r = 0.413, <i>p</i> < 0.05). Median SDNN was significantly lower in patients with SUA ≥ 5.6 mg/dL than in patients with SUA < 5.6 mg/dL (94.2 (IQR 86.8–108.1) vs. 112.8 (IQR 108.9–114.7), <i>p</i> < 0.01). Median LVMI was significantly higher in patients with SUA ≥ 5.6 mg/dL compared to patients with SUA < 5.6 mg/dL (133 g/m² (IQR 120–149) vs. 111 g/m² (IQR 99–129), <i>p</i> < 0.05). Conclusion: In patients with ARAS, SUA/sCr is associated with autonomic dysfunction and LVMI in ARAS patients. The ratio and related cut-off value of SUA/sCr could represent a useful biomarker to evaluate cardiovascular risk in ARAS patients.

Daniel Alazard, Francesco Sanfedino, Ervan Kassarian

This paper presents causal block-diagram models to represent the equations of motion of multi-body systems in a very compact and simple closed form. Both the forward dynamics (from the forces and torques imposed at the various degrees-of-freedom to the motions of these degrees-of-freedom) or the inverse dynamics (from the motions imposed at the degrees-of-freedom to the resulting forces and torques) can be considered and described by a block diagram model. This work extends the Two-Input Two-Output Port (TITOP) theory by including all non-linear terms and uniform or gravitational acceleration fields. Connection among different blocks is possible through the definition of the motion vector. The model of a system composed of a floating base, rigid bodies, revolute and prismatic joints, working under gravity is developed to illustrate the methodology. The proposed model is validated by simulation and cross-checking with a model built using an alternative modeling tool on a scenario where the nonlinear terms are determining.

Yuri Gardinazzi, Roger Gonzaléz March, Suprabhath Kalahasti et al.

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian hospital stays from 1997 to 2014, covering 8.9 million patients. These networks grow and become denser with age. We identified groups of diseases that exhibit similar patterns of structural centrality throughout the lifespan, revealing three dominant age-related components with peaks in early childhood, midlife, and late life. To uncover the drivers of this structural change, we examined the relationship between prevalence and degree. This allowed us to identify conditions that were disproportionately connected to other diseases. Using betweenness centrality in combination with mortality data, we further identified high-mortality bridging diseases. Several diseases show high connectivity relative to their prevalence, such as iron deficiency anemia (D50) in children, nicotine dependence (F17), and lipoprotein metabolism disorders (E78) in adults. We also highlight structurally central diseases with high mortality that emerge at different life stages, including cancers (C group), liver cirrhosis (K74), subarachnoid hemorrhage (I60), and chronic kidney disease (N18). These findings underscore the importance of targeting age-specific, network-central conditions with high mortality for prevention and integrated care.

Guoliang Liang, Guoliang Liang, Wenhao Zhang et al.

BackgroundAlthough a few studies have examined the correlation between low-density lipoprotein cholesterol (LDL-C) and mortality, no study has explored these associations in hypertensive populations. This study aims to investigate the relationship between low-density lipoprotein cholesterol and cardiovascular and all-cause mortality in adults with hypertension.MethodsHypertensive participants aged ≥18 years from the National Health and Nutrition Examination Survey 1999–2018 with blood lipid testing data and complete follow-up data until 31 December 2019 were enrolled in the analysis. Univariate and multivariate Cox regression were conducted for the calculation of hazard ratios and 95% confidence intervals. A restricted cubic spline curve was performed to visually represent the relationship between LDL-C and mortality. Kaplan–Meier survival analysis and stratification analysis were also carried out.ResultsWe finally analysed a cohort of 9,635 participants (49.6% male, mean age of 59.4 years). After a median follow-up of 98 months, there were 2,283 (23.7%) instances of all-cause fatalities, with 758 (7.9%) cases attributed to cardiovascular disease. Multivariate Cox regression analysis showed that lower levels of LDL-C were associated with a higher risk of all-cause and cardiovascular mortality; the LDL-C group’s lowest level (&lt;2.198 mmol/L) still showed a 19.6% increased risk of all-cause mortality (p = 0.0068) in the model that was completely adjusted. Both all-cause mortality and cardiovascular mortality showed a non-linear association with LDL-C concentration in restricted cubic spline regression analysis.ConclusionsIn individuals with hypertension, LDL-C was linked to cardiovascular and all-cause mortality. It was further demonstrated that this relationship was non-linear.

Xiaofan Mu, Merna Bibars, Salman Seyedi et al.

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 older adults with normal cognition or Mild Cognitive Impairment (MCI), derived from remote video conversations and quantified their cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia Rating Scale (CDR) of 0.5 (vs. 0) with 0.77 area under the receiver operating characteristic curve (AUC), social isolation with 0.74 AUC, social satisfaction with 0.75 AUC, psychological well-being with 0.72 AUC, and negative affect with 0.74 AUC. Our feature importance analysis showed that speech and language patterns were useful for quantifying cognitive impairment, whereas facial expressions and cardiovascular patterns were useful for quantifying social and psychological well-being. Our bias analysis showed that the best-performing models for quantifying psychological well-being and cognitive states in older adults exhibited significant biases concerning their age, sex, disease condition, and education levels. Our comprehensive analysis shows the feasibility of monitoring the cognitive and psychological health of older adults, as well as the need for collecting largescale interview datasets of older adults to benefit from the latest advances in deep learning technologies to develop generalizable models across older adults with diverse demographic backgrounds and disease conditions.

Yalda Nahidi, Vahid Mashayekhi Goyonlo, Malihe Dadgarmoghaddam et al.

Background: Systemic or topical form of pentavalent antimony compounds such as Meglumine Antimoniate (MA) are used as Standard treatment for cutaneous leishmaniasis (CL). However an increasing number of studies demonstrate evidence of treatment failure with said drugs. The objective of this study was to determine the factors associated with systemic MA treatment failure in patients with acute cutaneous leishmaniasis. Methods: In this case-control study, patients with urban CL who were referred to leishmaniasis clinics in Mashhad from 2017 to 2018 were followed up 12 months after the start of treatment and were evaluated for improvement or failure according to the national leishmaniasis protocol. Results: 112 cases of CL, 59 men and 53 women with a mean age of 23.3 ± 21.11 years were studied. The number of patients with clinical improvement was significantly higher in women (P = 0.005). Also age, BMI, occupation and education, the possible infection and living location, past medical, drug and leishmaniasis recurrence history, lesion’s characteristics, ulceration were also significantly different between the two groups of improved and unhealed patients. Conclusion: The results of this study showed that the male sex, age less than 18 years, receiving pentostam, previous treatment history, lymphadenopathy, urban leishmaniasis, duration of illness more than 4 months, having a single lesion especially on the face, BMI less than 18 and a lesion size of more than 3 cm is more common in patients with treatment failure.

Haider Sultan, Hafiza Farwa Mahmood, Noor Fatima et al.

Like other fields of Traditional Medicines, Unani Medicines have been found as an effective medical practice for ages. It is still widely used in the subcontinent, particularly in Pakistan and India. However, Unani Medicines Practitioners are lacking modern IT applications in their everyday clinical practices. An Online Clinical Decision Support System may address this challenge to assist apprentice Unani Medicines practitioners in their diagnostic processes. The proposed system provides a web-based interface to enter the patient's symptoms, which are then automatically analyzed by our system to generate a list of probable diseases. The system allows practitioners to choose the most likely disease and inform patients about the associated treatment options remotely. The system consists of three modules: an Online Clinical Decision Support System, an Artificial Intelligence Inference Engine, and a comprehensive Unani Medicines Database. The system employs advanced AI techniques such as Decision Trees, Deep Learning, and Natural Language Processing. For system development, the project team used a technology stack that includes React, FastAPI, and MySQL. Data and functionality of the application is exposed using APIs for integration and extension with similar domain applications. The novelty of the project is that it addresses the challenge of diagnosing diseases accurately and efficiently in the context of Unani Medicines principles. By leveraging the power of technology, the proposed Clinical Decision Support System has the potential to ease access to healthcare services and information, reduce cost, boost practitioner and patient satisfaction, improve speed and accuracy of the diagnostic process, and provide effective treatments remotely. The application will be useful for Unani Medicines Practitioners, Patients, Government Drug Regulators, Software Developers, and Medical Researchers.

Luca Pegolotti, Martin R. Pfaller, Natalia L. Rubio et al.

Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience reduced accuracy when working with anatomies that contain numerous junctions or pathological conditions. We develop one-dimensional reduced-order models that simulate blood flow dynamics using a graph neural network trained on three-dimensional hemodynamic simulation data. Given the initial condition of the system, the network iteratively predicts the pressure and flow rate at the vessel centerline nodes. Our numerical results demonstrate the accuracy and generalizability of our method in physiological geometries comprising a variety of anatomies and boundary conditions. Our findings demonstrate that our approach can achieve errors below 2% and 3% for pressure and flow rate, respectively, provided there is adequate training data. As a result, our method exhibits superior performance compared to physics-based one-dimensional models, while maintaining high efficiency at inference time.

Yagya Raj Pandeya, Samin Karki, Ishan Dangol et al.

We have developed a comprehensive computer system to assist farmers who practice traditional farming methods and have limited access to agricultural experts for addressing crop diseases. Our system utilizes artificial intelligence (AI) to identify and provide remedies for vegetable diseases. To ensure ease of use, we have created a mobile application that offers a user-friendly interface, allowing farmers to inquire about vegetable diseases and receive suitable solutions in their local language. The developed system can be utilized by any farmer with a basic understanding of a smartphone. Specifically, we have designed an AI-enabled mobile application for identifying and suggesting remedies for vegetable diseases, focusing on tomato diseases to benefit the local farming community in Nepal. Our system employs state-of-the-art object detection methodology, namely You Only Look Once (YOLO), to detect tomato diseases. The detected information is then relayed to the mobile application, which provides remedy suggestions guided by domain experts. In order to train our system effectively, we curated a dataset consisting of ten classes of tomato diseases. We utilized various data augmentation methods to address overfitting and trained a YOLOv5 object detector. The proposed method achieved a mean average precision of 0.76 and offers an efficient mobile interface for interacting with the AI system. While our system is currently in the development phase, we are actively working towards enhancing its robustness and real-time usability by accumulating more training samples.

Ralf Martz Sulague, Nina Nicole M. Suan, Michael F. Mendoza et al.

E. A. Zhidkova, E. M. Gutor, V. B. Pankova et al.

Corporate health programs are a common measure for the primary and secondary prevention of chronic non-communicable diseases. Aim . To study the first implementation results of a Targeted comprehensive program to reduce morbidity and prevent mortality from circulatory system diseases and early cancer detection in employees of JSC "Russian Railways" for the period from 2019 to 2023. Material and methods. The study used a survey of employees of locomotive crews (RLC), which was conducted twice: in the summer of 2018 and February-March 2021. The survey was conducted using a specially designed questionnaire that takes into account the health status of drivers and their assistants, production, and non-production risk factors. In 2018, 10476 questionnaires were collected (>7% of employees), and in 2021 — 14403 questionnaires (>10% of employees). The age structure of railways has not changed, which made it possible to analyze the frequency of occurrence of risk factors in dynamics. Results. In general, the mention of the RLC of the interfering effect of the noise factor, uncomfortable temperature, and undesirable odors in the driver’s cabin decreased for JSC "Russian Railways". The number of smokers on the South-Eastern Railway significantly increased during the study period. The number of people consuming insufficient amounts of vegetables and fruits has increased on the Far Eastern, West Siberian, Krasnoyarsk, and Volga railways. The frequency of workers’ meals at fast food restaurants has increased on the Southeastern Railway. The number of people with a good commitment to the basic principles of a healthy lifestyle has increased on the East Siberian, Trans-Baikal, West Siberian, Kuibyshev, Oktyabrskaya, Sverdlovsk, North Caucasian, and South Ural railways. Conclusion. The conducted research has shown the effectiveness of the initial stage of the implementation of the corporate program to reduce morbidity and prevent mortality from diseases of the circulatory system in RLC. The heterogeneity of the results for different railways was revealed.

Timothy L. Kline

The analysis of biological networks encompasses a wide variety of fields from genomic research of protein-protein interaction networks, to the physiological study of biologically optimized tree-like vascular networks. It is certain that different biological networks have different optimization criteria and we are interested in those networks optimized for fluid transport within the circulatory system. Many theories currently exist. For instance, distributive vascular geometry data is typically consistent with a theoretical model that requires simultaneous minimization of both the power loss of laminar flow and a cost function proportional to the total volume of material needed to maintain the system (Murray's law). However, how this optimized system breaks down (or is altered) due to disease has yet to be characterized in detail in terms of branching geometry and geometric interrelationships. This is important for understanding how vasculature remodels under changes of functional demands. For instance, in polycystic kidney disease (PKD), drastic cyst development may lead to a significant alteration of the vascular geometry (or vascular changes may be a preceding event). Understanding these changes could lead to a better understanding of early disease as well as development and characterization of treatment interventions. We have developed an optimal transport network model which simulates distributive vascular systems in health as well as disease in order to better understand changes that may occur due to PKD. We found that reduced perfusion territories, dilated distributive vasculature, and vessel rarefaction are all consequences of cyst development derived from this theoretical model and are a direct result of the increased heterogeneity of local renal tissue perfusion demands.

Haylee K. Fox, Emily J. Callander

G. Perrard

Lisa W. Howley, MD, Janette Strasburger, MD, Joseph J. Maleszewski, MD et al.

Unguarded mitral valve orifice is a rare disease with only 7 described cases in the literature. We describe the first known case of unguarded mitral valve orifice with normal segmental cardiac anatomy, severe left ventricular dilatation and dysfunction, aortic atresia, and atrial flutter. (Level of Difficulty: Advanced.)

Fei F. Chen, Shu F. Jiang, Chang Dong et al.

Background: Primary cardiac angiosarcomas, especially those originating in the pericardium, are extremely rare and aggressive tumors with poor prognosis. These types of malignant tumors have diverse clinical presentations and are often masked by other comorbidities.Case Summary: Our hospital reported a 59-year-old woman who initially presented with pulmonary thromboembolism (PTE) and was subsequently treated with low-molecular-weight heparin. However, she experienced acute pericardial tamponade after anticoagulation therapy, where no obvious mass was primarily identified upon imaging, both in the pericardium or within the heart. Emergency pericardiocentesis and drainage were performed, where a total of 210 mL of bloody effusion was drained. Four months later, she was hospitalized with progressive hemoptysis and dyspnea. A large mixed mass occupying the right pericardium was later identified by coronary computed tomography angiography (CCTA). The mass was consistent with the right atrium, with heterogeneous thickened pericardium and localized moderate pericardial effusion. CCTA and positron emission tomography scans later showed metastases in both lungs and bilateral pleura. Nodules in hilar and mediastinal lymph nodes were also significant. Ultrasound-guided biopsy was performed, and the patient was ultimately diagnosed with an angiosarcoma based on final positive results for both CD31 and CD34 markers. The patient refused chemotherapy and passed away while waiting for her pathology results. The patient survived for 6 months since the first reported episode of PTE.Conclusions: Our case indicates that patients presenting with both embolism and hemorrhage should urgently be channeled to a clinical specialist to confirm any malignant etiology. This would be beneficial to confirm an early diagnosis and lengthen the duration of patient survival. However, the diagnosis of primary cardiac angiosarcoma is still challenging and requires multiple imaging modalities and biopsies in order to assist the accurate diagnosis of disease and achieve effective patient management.

Francisco Eduardo Coral, Giovanna Golin Guarinello, Alice Pavanatto Cavassola et al.

Resumo Contexto A insuficiência venosa crônica é uma doença de alta prevalência mundial, podendo chegar a até 80% da população. Sua incidência aumenta com a idade e é mais frequente no sexo feminino. Das opções terapêuticas, destaca-se a terapia compressiva, sendo a principal o uso de meia elástica de compressão graduada, considerado o tratamento básico para a insuficiência venosa crônica independentemente da classificação clínica do paciente. Na prática clínica, o resultado da terapia é prejudicado pela não adesão ao uso da meia. Objetivos Avaliar a taxa de adesão ao uso da meia elástica de compressão graduada, assim como compreender a problemática da não aderência ao tratamento. Métodos Estudo observacional transversal, realizado entre junho de 2017 até janeiro de 2019, mediante aplicação de questionário aos pacientes em ambulatório de cirurgia vascular do Sistema Único de Saúde (SUS), em um hospital-escola, em Curitiba, no estado do Paraná (PR). Os dados foram analisados com o programa computacional IBM SPSS Statistics v.20.0. Resultados Foram analisados 240 pacientes. A média de idade foi de 57,5±12,9 (22-86); 84,2% eram do sexo feminino. Do total de pacientes analisados, 106 (44,2%) não aderiram ao uso das meias. As justificativas para o não uso foram: questão financeira, dor, desconhecimento da necessidade, calor e outras. Conclusões A taxa de adesão encontrada no presente estudo foi de 55,8%, e o principal motivo para o não uso foi a questão financeira.