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The Journal of International Medical... Jun 2024The gold standard therapy for end-stage heart failure is cardiac transplantation. However, in the face of a donor shortage, a mechanical assist device such as the left...
The gold standard therapy for end-stage heart failure is cardiac transplantation. However, in the face of a donor shortage, a mechanical assist device such as the left ventricular assist device HeartMate 3 (Abbott Laboratories, Abbott Park, IL, USA) serves as bridging therapy to transplantation and/or destination therapy. Current guidelines recommend anticoagulation with a vitamin K antagonist in combination with low-dose aspirin. We herein report a challenging anticoagulation regimen in a patient with a HeartMate 3 in whom systemic anticoagulation with warfarin was not feasible for 4 years because of low compatibility and a rare X-factor deficiency. This is a rare hematological disorder, estimated to affect approximately 1 in every 500,000 to 1,000,000 people in the general population. The patient finally received a modified anticoagulation regimen involving the combination of rivaroxaban and clopidogrel without warfarin. Under this regimen, the patient remained free of thromboembolic complications for 4 years with placement of the left ventricular assist device. This case illustrates that under specific circumstances, long-term absence of warfarin therapy is feasible in patients with a HeartMate 3.
Topics: Humans; Heart-Assist Devices; Warfarin; Thromboembolism; Anticoagulants; Male; Heart Failure; Middle Aged; Clopidogrel; Rivaroxaban; Withholding Treatment
PubMed: 38901839
DOI: 10.1177/03000605241258474 -
The Canadian Journal of Cardiology Jun 2024This manuscript reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of... (Review)
Review
This manuscript reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The paper examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac CT, and MRI and discusses the regulatory landscape for AI in healthcare, categorizes AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalizability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.
PubMed: 38901544
DOI: 10.1016/j.cjca.2024.06.011 -
Scientific Reports Jun 2024Mechanical circulatory support (MCS) devices, including veno-arterial extracorporeal membrane oxygenation (VA-ECMO) and Impella, have been widely used for patients with...
Mechanical circulatory support (MCS) devices, including veno-arterial extracorporeal membrane oxygenation (VA-ECMO) and Impella, have been widely used for patients with cardiogenic shock (CS). However, hemodynamics with each device and combination therapy is not thoroughly understood. We aimed to elucidate the hemodynamics with MCS using a pulsatile flow model. Hemodynamics with Impella CP, VA-ECMO, and a combination of Impella CP and VA-ECMO were assessed based on the pressure and flow under support with each device and the pressure-volume loop of the ventricle model. The Impella CP device with CS status resulted in an increase in aortic pressure and a decrease in end-diastolic volume and end-diastolic pressure (EDP). VA-ECMO support resulted in increased afterload, leading to a significant increase in aortic pressure with an increase in end-systolic volume and EDP and decreasing venous reservoir pressure. The combination of Impella CP and VA-ECMO led to left ventricular unloading, regardless of increase in afterload. Hemodynamic support with Impella and VA-ECMO should be a promising combination for patients with severe CS.
Topics: Shock, Cardiogenic; Hemodynamics; Extracorporeal Membrane Oxygenation; Heart-Assist Devices; Humans; Models, Cardiovascular; Pulsatile Flow
PubMed: 38898087
DOI: 10.1038/s41598-024-64721-1 -
Sensors (Basel, Switzerland) May 2024Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed...
Monitoring heart conditions through electrocardiography (ECG) has been the cornerstone of identifying cardiac irregularities. Cardiologists often rely on a detailed analysis of ECG recordings to pinpoint deviations that are indicative of heart anomalies. This traditional method, while effective, demands significant expertise and is susceptible to inaccuracies due to its manual nature. In the realm of computational analysis, Artificial Neural Networks (ANNs) have gained prominence across various domains, which can be attributed to their superior analytical capabilities. Conversely, Spiking Neural Networks (SNNs), which mimic the neural activity of the brain more closely through impulse-based processing, have not seen widespread adoption. The challenge lies primarily in the complexity of their training methodologies. Despite this, SNNs offer a promising avenue for energy-efficient computational models capable of displaying a high-level performance. This paper introduces an innovative approach employing SNNs augmented with an attention mechanism to enhance feature recognition in ECG signals. By leveraging the inherent efficiency of SNNs, coupled with the precision of attention modules, this model aims to refine the analysis of cardiac signals. The novel aspect of our methodology involves adapting the learned parameters from ANNs to SNNs using leaky integrate-and-fire (LIF) neurons. This transfer learning strategy not only capitalizes on the strengths of both neural network models but also addresses the training challenges associated with SNNs. The proposed method is evaluated through extensive experiments on two publicly available benchmark ECG datasets. The results show that our model achieves an overall accuracy of 93.8% on the MIT-BIH Arrhythmia dataset and 85.8% on the 2017 PhysioNet Challenge dataset. This advancement underscores the potential of SNNs in the field of medical diagnostics, offering a path towards more accurate, efficient, and less resource-intensive analyses of heart diseases.
Topics: Neural Networks, Computer; Electrocardiography; Humans; Neurons; Algorithms; Signal Processing, Computer-Assisted
PubMed: 38894215
DOI: 10.3390/s24113426 -
Cancers May 2024This study aimed to predict stress in patients using artificial intelligence (AI) from biological signals and verify the effect of stress on respiratory irregularity. We...
This study aimed to predict stress in patients using artificial intelligence (AI) from biological signals and verify the effect of stress on respiratory irregularity. We measured 123 cases in 41 patients and calculated stress scores with seven stress-related features derived from heart-rate variability. The distribution and trends of stress scores across the treatment period were analyzed. Before-treatment information was used to predict the stress features during treatment. AI models included both non-pretrained (decision tree, random forest, support vector machine, long short-term memory (LSTM), and transformer) and pretrained (ChatGPT) models. Performance was evaluated using 10-fold cross-validation, exact match ratio, accuracy, recall, precision, and F1 score. Respiratory irregularities were calculated in phase and amplitude and analyzed for correlation with stress score. Over 90% of the patients experienced stress during radiation therapy. LSTM and prompt engineering GPT4.0 had the highest accuracy (feature classification, LSTM: 0.703, GPT4.0: 0.659; stress classification, LSTM: 0.846, GPT4.0: 0.769). A 10% increase in stress score was associated with a 0.286 higher phase irregularity ( < 0.025). Our research pioneers the use of AI and biological signals for stress prediction in patients undergoing radiation therapy, potentially identifying those needing psychological support and suggesting methods to improve radiotherapy effectiveness through stress management.
PubMed: 38893087
DOI: 10.3390/cancers16111964 -
Healthcare (Basel, Switzerland) May 2024Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration...
Efficient management of hospital resources is essential for providing high-quality healthcare while ensuring sustainability. Length of stay (LOS), measuring the duration from admission to discharge, directly impacts patient outcomes and resource utilization. Accurate LOS prediction offers numerous benefits, including reducing re-admissions, ensuring appropriate staffing, and facilitating informed discharge planning. While conventional methods rely on statistical models and clinical expertise, recent advances in machine learning (ML) present promising avenues for enhancing LOS prediction. This research focuses on developing an ML-based LOS prediction model trained on a comprehensive real-world dataset and discussing the important factors towards practical deployment of trained ML models in clinical settings. This research involves the development of a comprehensive adult cardiac patient dataset (SaudiCardioStay (SCS)) from the King Faisal Specialist Hospital & Research Centre (KFSH&RC) hospital in Saudi Arabia, comprising 4930 patient encounters for 3611 unique patients collected from 2019 to 2022 (excluding 2020). A diverse range of classical ML models (i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), artificial neural networks (ANNs), Average Voting Regression (AvgVotReg)) are implemented for the SCS dataset to explore the potential of existing ML models in LOS prediction. In addition, this study introduces a novel approach for LOS prediction by incorporating a dedicated LOS classifier within a sophisticated ensemble methodology (i.e., Two-Level Sequential Cascade Generalization (2LSCG), Three-Level Sequential Cascade Generalization (3LSCG), Parallel Cascade Generalization (PCG)), aiming to enhance prediction accuracy and capture nuanced patterns in healthcare data. The experimental results indicate the best mean absolute error (MAE) of 0.1700 for the 3LSCG model. Relatively comparable performance was observed for the AvgVotReg model, with a MAE of 0.1703. In the end, a detailed analysis of the practical implications, limitations, and recommendations concerning the deployment of ML approaches in actual clinical settings is presented.
PubMed: 38891185
DOI: 10.3390/healthcare12111110 -
Scientific Reports Jun 2024The sinus node (SN) serves as the primary pacemaker of the heart and is the first component of the cardiac conduction system. Due to its anatomical properties and sample...
The sinus node (SN) serves as the primary pacemaker of the heart and is the first component of the cardiac conduction system. Due to its anatomical properties and sample scarcity, the cellular composition of the human SN has been historically challenging to study. Here, we employed a novel deep learning deconvolution method, namely Bulk2space, to characterise the cellular heterogeneity of the human SN using existing single-cell datasets of non-human species. As a proof of principle, we used Bulk2Space to profile the cells of the bulk human right atrium using publicly available mouse scRNA-Seq data as a reference. 18 human cell populations were identified, with cardiac myocytes being the most abundant. Each identified cell population correlated to its published experimental counterpart. Subsequently, we applied the deconvolution to the bulk transcriptome of the human SN and identified 11 cell populations, including a population of pacemaker cardiomyocytes expressing pacemaking ion channels (HCN1, HCN4, CACNA1D) and transcription factors (SHOX2 and TBX3). The connective tissue of the SN was characterised by adipocyte and fibroblast populations, as well as key immune cells. Our work unravelled the unique single cell composition of the human SN by leveraging the power of a novel machine learning method.
Topics: Humans; Sinoatrial Node; Myocytes, Cardiac; Single-Cell Analysis; Mice; Animals; Artificial Intelligence; Transcriptome; Heart Atria; Deep Learning
PubMed: 38890395
DOI: 10.1038/s41598-024-63542-6 -
Open Heart Jun 2024Neurocardiogenic syncope is a common condition with significant associated psychological and physical morbidity. The effectiveness of therapeutic options for... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Neurocardiogenic syncope is a common condition with significant associated psychological and physical morbidity. The effectiveness of therapeutic options for neurocardiogenic syncope beyond placebo remains uncertain.
METHODS
The primary endpoint was the risk ratio (RR) of spontaneously recurring syncope following any therapeutic intervention. We also examined the effect of blinding on treatment efficacy. We identified all randomised trials which evaluated the effect of any pharmacological, device-based or supportive intervention on patients with a history of syncope. A systematic search was conducted on Medline, Embase, PubMed databases and Cochrane Central Register for Controlled Trials from 1950 to 25 April 2023. Event rates, their RRs and 95% CIs were calculated, and a random-effects meta-analysis was conducted for each intervention. Data analysis was performed in R using RStudio.
RESULTS
We identified 47 eligible trials randomising 3518 patients. Blinded trials assessing syncope recurrence were neutral for beta blockers, fludrocortisone and conventional dual-chamber pacing but were favourable for selective serotonin reuptake inhibitors (SSRIs) (RR 0.40, 95% CI 0.26 to 0.63, p<0.001), midodrine (RR 0.70, 95% CI 0.53 to 0.94, p=0.016) and closed-loop stimulation (CLS) pacing (RR 0.15, 95% CI 0.07 to 0.35, p<0.001). Unblinded trials reported significant benefits for all therapy categories other than beta blockers and consistently showed larger benefits than blinded trials.
CONCLUSIONS
Under blinded conditions, SSRIs, midodrine and CLS pacing significantly reduced syncope recurrence. Future trials for syncope should be blinded to avoid overestimating treatment effects.
PROSPERO REGISTRATION NUMBER
CRD42022330148.
Topics: Humans; Syncope, Vasovagal; Randomized Controlled Trials as Topic; Treatment Outcome; Recurrence
PubMed: 38890128
DOI: 10.1136/openhrt-2024-002669 -
Cell Reports Methods Jun 2024Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of...
Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principles for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs. We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We clustered and refined the cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium transient rising time in distinguishing organoids based on geometric patterns and clustering results. This integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.
Topics: Organoids; Machine Learning; Tissue Engineering; Humans; Animals; Heart; Myocardium
PubMed: 38889687
DOI: 10.1016/j.crmeth.2024.100798 -
JMIRx Med Jun 2024The Society of Thoracic Surgeons and European System for Cardiac Operative Risk Evaluation (EuroSCORE) II risk scores are the most commonly used risk prediction models...
BACKGROUND
The Society of Thoracic Surgeons and European System for Cardiac Operative Risk Evaluation (EuroSCORE) II risk scores are the most commonly used risk prediction models for in-hospital mortality after adult cardiac surgery. However, they are prone to miscalibration over time and poor generalization across data sets; thus, their use remains controversial. Despite increased interest, a gap in understanding the effect of data set drift on the performance of machine learning (ML) over time remains a barrier to its wider use in clinical practice. Data set drift occurs when an ML system underperforms because of a mismatch between the data it was developed from and the data on which it is deployed.
OBJECTIVE
In this study, we analyzed the extent of performance drift using models built on a large UK cardiac surgery database. The objectives were to (1) rank and assess the extent of performance drift in cardiac surgery risk ML models over time and (2) investigate any potential influence of data set drift and variable importance drift on performance drift.
METHODS
We conducted a retrospective analysis of prospectively, routinely gathered data on adult patients undergoing cardiac surgery in the United Kingdom between 2012 and 2019. We temporally split the data 70:30 into a training and validation set and a holdout set. Five novel ML mortality prediction models were developed and assessed, along with EuroSCORE II, for relationships between and within variable importance drift, performance drift, and actual data set drift. Performance was assessed using a consensus metric.
RESULTS
A total of 227,087 adults underwent cardiac surgery during the study period, with a mortality rate of 2.76% (n=6258). There was strong evidence of a decrease in overall performance across all models (P<.0001). Extreme gradient boosting (clinical effectiveness metric [CEM] 0.728, 95% CI 0.728-0.729) and random forest (CEM 0.727, 95% CI 0.727-0.728) were the overall best-performing models, both temporally and nontemporally. EuroSCORE II performed the worst across all comparisons. Sharp changes in variable importance and data set drift from October to December 2017, from June to July 2018, and from December 2018 to February 2019 mirrored the effects of performance decrease across models.
CONCLUSIONS
All models show a decrease in at least 3 of the 5 individual metrics. CEM and variable importance drift detection demonstrate the limitation of logistic regression methods used for cardiac surgery risk prediction and the effects of data set drift. Future work will be required to determine the interplay between ML models and whether ensemble models could improve on their respective performance advantages.
PubMed: 38889069
DOI: 10.2196/45973