Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 286
Filtrar
2.
JACC Case Rep ; 29(13): 102396, 2024 Jul 03.
Artigo em Inglês | MEDLINE | ID: mdl-38948493

RESUMO

Single coronary artery, giant coronary artery aneurysm, and coronary cameral fistula are rare congenital anomalies, and can cause a range of presentations. To our knowledge, this is the first reported case of all 3 entities occurring simultaneously in 1 patient, with largely unknown implications. Multimodal imaging was essential in prompt diagnosis and management.

3.
Can J Cardiol ; 2024 May 30.
Artigo em Inglês | MEDLINE | ID: mdl-38823633

RESUMO

Large language models (LLMs) are a unique form of machine learning that facilitates inputs of unstructured text/numerical information for meaningful interpretation and prediction. Recently, LLMs have become commercialized, allowing the average person to access these incredibly powerful tools. Early adopters focused on LLM use in performing logical tasks, including but not limited to generating titles, identifying keywords, summarizing text, initial editing of scientific work, improving statistical protocols, and performing statistical analysis. More recently, LLMs have been expanded to clinical practice and academia to perform higher cognitive and creative tasks. LLMs provide personalized assistance in learning, facilitate the management of electronic medical records, and offer valuable insights into clinical decision-making in cardiology. They enhance patient education by explaining intricate medical conditions in lay terms, have a vast library of knowledge to help clinicians expedite administrative tasks, provide useful feedback regarding content of scientific writing, and assist in the peer review process. Despite their impressive capabilities, LLMs are not without limitations. They are susceptible to generating incorrect or plagiarized content, face challenges in handling tasks without detailed prompts and lack originality. These limitations underscore the importance of human oversight in utilizing LLMs in medical science and clinical practice. As LLMs continue to evolve, addressing these challenges will be crucial in maximizing their potential benefits while mitigating risks. This review explores the functions, opportunities, and constraints of LLMs, with a focus on their impact on cardiology, illustrating both the transformative power and the boundaries of current technology in medicine.

4.
Sci Rep ; 14(1): 10672, 2024 05 09.
Artigo em Inglês | MEDLINE | ID: mdl-38724564

RESUMO

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the "pattern" of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.


Assuntos
Ecocardiografia , Aprendizado de Máquina , Humanos , Masculino , Ecocardiografia/métodos , Feminino , Pessoa de Meia-Idade , Doenças Raras/diagnóstico por imagem , Pericardite Constritiva/diagnóstico por imagem , Pericardite Constritiva/diagnóstico , Cardiomiopatia Restritiva/diagnóstico por imagem , Estudos Retrospectivos , Idoso , Ventrículos do Coração/diagnóstico por imagem , Ventrículos do Coração/fisiopatologia , Insuficiência Cardíaca/diagnóstico por imagem , Adulto
6.
Lancet ; 403(10436): 1590-1602, 2024 Apr 20.
Artigo em Inglês | MEDLINE | ID: mdl-38554727

RESUMO

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challenges in diagnosis and management. This two-part Series offers a comprehensive review of changing concepts in VHD, covering diagnosis, intervention timing, novel management strategies, and the current state of research. The first paper highlights the remarkable progress made in imaging and transcatheter techniques, effectively addressing the treatment paradox wherein populations at the highest risk of VHD often receive the least treatment. These advances have attracted the attention of clinicians, researchers, engineers, device manufacturers, and investors, leading to the exploration and proposal of treatment approaches grounded in pathophysiology and multidisciplinary strategies for VHD management. This Series paper focuses on innovations involving computational, pharmacological, and bioengineering approaches that are transforming the diagnosis and management of patients with VHD. Artificial intelligence and digital methods are enhancing screening, diagnosis, and planning procedures, and the integration of imaging and clinical data is improving the classification of VHD severity. The emergence of artificial intelligence techniques, including so-called digital twins-eg, computer-generated replicas of the heart-is aiding the development of new strategies for enhanced risk stratification, prognostication, and individualised therapeutic targeting. Various new molecular targets and novel pharmacological strategies are being developed, including multiomics-ie, analytical methods used to integrate complex biological big data to find novel pathways to halt the progression of VHD. In addition, efforts have been undertaken to engineer heart valve tissue and provide a living valve conduit capable of growth and biological integration. Overall, these advances emphasise the importance of early detection, personalised management, and cutting-edge interventions to optimise outcomes amid the evolving landscape of VHD. Although several challenges must be overcome, these breakthroughs represent opportunities to advance patient-centred investigations.


Assuntos
Inteligência Artificial , Doenças das Valvas Cardíacas , Humanos , Doenças das Valvas Cardíacas/diagnóstico , Doenças das Valvas Cardíacas/terapia
8.
JACC Case Rep ; 29(4): 102162, 2024 Feb 21.
Artigo em Inglês | MEDLINE | ID: mdl-38379653

RESUMO

We describe a rare complication of intraprocedural spontaneous thrombus formation on a transcatheter edge-to-edge repair (MitraClip; Abbott Laboratories) device in a hypercoagulable yet adequately anticoagulated patient. We also outline the novel use of a vacuum (Penumbra) aspiration system, which resulted in rapid and effective thrombus elimination.

9.
Eur Heart J Cardiovasc Imaging ; 25(7): 937-946, 2024 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-38315669

RESUMO

AIMS: Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal left ventricular (LV) diastolic dysfunction (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF). METHODS AND RESULTS: A previously developed DeepNN was tested on 5596 older participants (66-90 years; 57% female; 20% Black) from the Atherosclerosis Risk in Communities Study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4054) and Stage C/D (n = 1542) subgroups was assessed. The DeepNN-predicted high-risk compared with the low-risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank P < 0.0001 for all). In multi-variable analyses, the high-risk phenogroup remained an independent predictor of HF and death in both Stages A/B {adjusted hazard ratio [95% confidence interval (CI)] 6.52 [4.20-10.13] and 2.21 [1.68-2.91], both P < 0.0001} and Stage C/D [6.51 (4.06-10.44) and 1.03 (1.00-1.06), both P < 0.0001], respectively. In addition, DeepNN showed incremental value over the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) guidelines [net re-classification index, 0.5 (CI 0.4-0.6), P < 0.001; C-statistic improvement, DeepNN (0.76) vs. ASE/EACVI (0.70), P < 0.001] overall and maintained across stage groups. CONCLUSION: Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.


Assuntos
Disfunção Ventricular Esquerda , Humanos , Feminino , Idoso , Masculino , Idoso de 80 Anos ou mais , Disfunção Ventricular Esquerda/diagnóstico por imagem , Disfunção Ventricular Esquerda/fisiopatologia , Aprendizado Profundo , Medição de Risco , Insuficiência Cardíaca/diagnóstico por imagem , Ecocardiografia/métodos , Estados Unidos , Estudos de Coortes , Redes Neurais de Computação , Diástole , Fatores Etários
10.
Curr Cardiol Rep ; 25(12): 1897-1907, 2023 12.
Artigo em Inglês | MEDLINE | ID: mdl-38091196

RESUMO

PURPOSE OF REVIEW: In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action. RECENT FINDINGS: Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.


Assuntos
Cardiologia , Sistemas de Apoio a Decisões Clínicas , Humanos , Inteligência Artificial , Ecocardiografia , Aprendizado de Máquina
13.
JACC Cardiovasc Imaging ; 16(9): 1209-1223, 2023 09.
Artigo em Inglês | MEDLINE | ID: mdl-37480904

RESUMO

Artificial intelligence (AI) promises to revolutionize many fields, but its clinical implementation in cardiovascular imaging is still rare despite increasing research. We sought to facilitate discussion across several fields and across the lifecycle of research, development, validation, and implementation to identify challenges and opportunities to further translation of AI in cardiovascular imaging. Furthermore, it seemed apparent that a multidisciplinary effort across institutions would be essential to overcome these challenges. This paper summarizes the proceedings of the National Heart, Lung, and Blood Institute-led workshop, creating consensus around needs and opportunities for institutions at several levels to support and advance research in this field and support future translation.


Assuntos
Inteligência Artificial , Sistema Cardiovascular , Estados Unidos , Humanos , National Heart, Lung, and Blood Institute (U.S.) , Valor Preditivo dos Testes , Assistência ao Paciente
14.
Eur Heart J Digit Health ; 4(3): 145-154, 2023 May.
Artigo em Inglês | MEDLINE | ID: mdl-37265867

RESUMO

Aims: Clinical differentiation of acute myocardial infarction (MI) from unstable angina and other presentations mimicking acute coronary syndromes (ACS) is critical for implementing time-sensitive interventions and optimizing outcomes. However, the diagnostic steps are dependent on blood draws and laboratory turnaround times. We tested the clinical feasibility of a wrist-worn transdermal infrared spectrophotometric sensor (transdermal-ISS) in clinical practice and assessed the performance of a machine learning algorithm for identifying elevated high-sensitivity cardiac troponin-I (hs-cTnI) levels in patients hospitalized with ACS. Methods and results: We enrolled 238 patients hospitalized with ACS at five sites. The final diagnosis of MI (with or without ST elevation) and unstable angina was adjudicated using electrocardiography (ECG), cardiac troponin (cTn) test, echocardiography (regional wall motion abnormality), or coronary angiography. A transdermal-ISS-derived deep learning model was trained (three sites) and externally validated with hs-cTnI (one site) and echocardiography and angiography (two sites), respectively. The transdermal-ISS model predicted elevated hs-cTnI levels with areas under the receiver operator characteristics of 0.90 [95% confidence interval (CI), 0.84-0.94; sensitivity, 0.86; and specificity, 0.82] and 0.92 (95% CI, 0.80-0.98; sensitivity, 0.94; and specificity, 0.64), for internal and external validation cohorts, respectively. In addition, the model predictions were associated with regional wall motion abnormalities [odds ratio (OR), 3.37; CI, 1.02-11.15; P = 0.046] and significant coronary stenosis (OR, 4.69; CI, 1.27-17.26; P = 0.019). Conclusion: A wrist-worn transdermal-ISS is clinically feasible for rapid, bloodless prediction of elevated hs-cTnI levels in real-world settings. It may have a role in establishing a point-of-care biomarker diagnosis of MI and impact triaging patients with suspected ACS.

17.
JACC Cardiovasc Imaging ; 16(10): 1253-1267, 2023 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-37178071

RESUMO

BACKGROUND: Primary mitral regurgitation (MR) is a heterogeneous clinical disease requiring integration of echocardiographic parameters using guideline-driven recommendations to identify severe disease. OBJECTIVES: The purpose of this preliminary study was to explore novel data-driven approaches to delineate phenotypes of MR severity that benefit from surgery. METHODS: The authors used unsupervised and supervised machine learning and explainable artificial intelligence (AI) to integrate 24 echocardiographic parameters in 400 primary MR subjects from France (n = 243; development cohort) and Canada (n = 157; validation cohort) followed up during a median time of 3.2 years (IQR: 1.3-5.3 years) and 6.8 (IQR: 4.0-8.5 years), respectively. The authors compared the phenogroups' incremental prognostic value over conventional MR profiles and for the primary endpoint of all-cause mortality incorporating time-to-mitral valve repair/replacement surgery as a covariate for survival analysis (time-dependent exposure). RESULTS: High-severity (HS) phenogroups from the French cohort (HS: n = 117; low-severity [LS]: n = 126) and the Canadian cohort (HS: n = 87; LS: n = 70) showed improved event-free survival in surgical HS subjects over nonsurgical subjects (P = 0.047 and P = 0.020, respectively). A similar benefit of surgery was not seen in the LS phenogroup in both cohorts (P = 0.70 and P = 0.50, respectively). Phenogrouping showed incremental prognostic value in conventionally severe or moderate-severe MR subjects (Harrell C statistic improvement; P = 0.480; and categorical net reclassification improvement; P = 0.002). Explainable AI specified how each echocardiographic parameter contributed to phenogroup distribution. CONCLUSIONS: Novel data-driven phenogrouping and explainable AI aided in improved integration of echocardiographic data to identify patients with primary MR and improved event-free survival after mitral valve repair/replacement surgery.

18.
Life (Basel) ; 13(4)2023 Apr 17.
Artigo em Inglês | MEDLINE | ID: mdl-37109558

RESUMO

Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam-a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.

19.
Am J Physiol Heart Circ Physiol ; 324(5): H624-H629, 2023 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-36897746

RESUMO

Left ventricular vortex formation optimizes the effective transport of blood volume while minimizing energy loss (EL). Vector flow mapping (VFM)-derived EL patterns have not been described in children, especially in those less than 1 yr of age. A prospective cohort of 66 (0 days-22 yr, 14 patients ≤ 2 mo) cardiovascularly normal children was used to determine left ventricular (LV) vortex number, size (mm2), strength (m2/s), and energy loss (mW/m/m2) in systole and diastole and compared across age groups. One early diastolic (ED) vortex at the anterior mitral leaflet and one late diastolic (LD) vortex at the LV outflow tract (LVOT) were seen in all newborns ≤ 2 mo. At >2 mo, two ED vortices and one LD vortex were seen, with 95% of subjects > 2 yr demonstrating this vortex pattern. Peak and average diastolic EL acutely increased in the same 2 mo-2-yr period and then decreased within the adolescent and young adult age groups. Overall, these findings suggest that the growing heart undergoes a transition to adult vortex flow patterns over the first 2 yr of life with a corresponding acute increase in diastolic EL. These findings offer an initial insight into the dynamic changes of LV flow patterns in pediatric patients and can serve to expand our understanding of cardiac efficiency and physiology in children.NEW & NOTEWORTHY This research article demonstrates, for the first time, echocardiographic evidence of a transition in left ventricular vortex patterns from the newborn to the adult period, with an associated change in cardiac efficiency, marked by increased energy loss, during infancy.


Assuntos
Ecocardiografia , Ventrículos do Coração , Recém-Nascido , Adulto Jovem , Adolescente , Humanos , Criança , Estudos Prospectivos , Velocidade do Fluxo Sanguíneo/fisiologia , Diástole/fisiologia , Ventrículos do Coração/diagnóstico por imagem , Função Ventricular Esquerda/fisiologia
20.
Ann Card Anaesth ; 26(1): 29-35, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36722585

RESUMO

Background: General anesthesia has traditionally been used in transcatheter aortic valve replacement; however, there has been increasing interest and momentum in alternative anesthetic techniques. Aims: To perform a descriptive study of anesthetic management options in transcatheter aortic valve replacements in the United States, comparing trends in use of monitored anesthesia care versus general anesthesia. Settings and Design: Data evaluated from the American Society of Anesthesiologists' (ASA) Anesthesia Quality Institute's National Anesthesia Clinical Outcomes Registry. Materials and Methods: Multivariable logistic regression was used to identify predictors associated with use of monitored anesthesia care compared to general anesthesia. Results: The use of monitored anesthesia care has increased from 1.8% of cases in 2013 to 25.2% in 2017 (p = 0.0001). Patients were more likely ages 80+ (66% vs. 61%; p = 0.0001), male (54% vs. 52%; p = 0.0001), ASA physical status > III (86% vs. 80%; p = 0.0001), cared for in the Northeast (38% vs. 22%; p = 0.0001), and residents in zip codes with higher median income ($63,382 vs. $55,311; p = 0.0001). Multivariable analysis revealed each one-year increase in age, every 50 procedures performed annually at a practice, and being male were associated with 3% (p = 0.0001), 33% (p = 0.012), and 16% (p = 0.026) increased odds of monitored anesthesia care, respectively. Centers in the Northeast were more likely to use monitored anesthesia care (all p < 0.005). Patients who underwent approaches other than percutaneous femoral arterial were less likely to receive monitored anesthesia care (adjusted odds ratios all < 0.51; all p = 0.0001). Conclusion: Anesthetic type for transcatheter aortic valve replacements in the United States varies with age, sex, geography, volume of cases performed at a center, and procedural approach.


Assuntos
Anestesiologia , Anestésicos , Substituição da Valva Aórtica Transcateter , Humanos , Masculino , Idoso de 80 Anos ou mais , Feminino , Anestesia Geral , Sistema de Registros
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA