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Journal of the American Heart... Jun 2024Enhanced detection of large vessel occlusion (LVO) through machine learning (ML) for acute ischemic stroke appears promising. This systematic review explored the... (Meta-Analysis)
Meta-Analysis
Systematic Review and Meta-Analysis of Prehospital Machine Learning Scores as Screening Tools for Early Detection of Large Vessel Occlusion in Patients With Suspected Stroke.
BACKGROUND
Enhanced detection of large vessel occlusion (LVO) through machine learning (ML) for acute ischemic stroke appears promising. This systematic review explored the capabilities of ML models compared with prehospital stroke scales for LVO prediction.
METHODS AND RESULTS
Six bibliographic databases were searched from inception until October 10, 2023. Meta-analyses pooled the model performance using area under the curve (AUC), sensitivity, specificity, and summary receiver operating characteristic curve. Of 1544 studies screened, 8 retrospective studies were eligible, including 32 prehospital stroke scales and 21 ML models. Of the 9 prehospital scales meta-analyzed, the Rapid Arterial Occlusion Evaluation had the highest pooled AUC (0.82 [95% CI, 0.79-0.84]). Support Vector Machine achieved the highest AUC of 9 ML models included (pooled AUC, 0.89 [95% CI, 0.88-0.89]). Six prehospital stroke scales and 10 ML models were eligible for summary receiver operating characteristic analysis. Pooled sensitivity and specificity for any prehospital stroke scale were 0.72 (95% CI, 0.68-0.75) and 0.77 (95% CI, 0.72-0.81), respectively; summary receiver operating characteristic curve AUC was 0.80 (95% CI, 0.76-0.83). Pooled sensitivity for any ML model for LVO was 0.73 (95% CI, 0.64-0.79), specificity was 0.85 (95% CI, 0.80-0.89), and summary receiver operating characteristic curve AUC was 0.87 (95% CI, 0.83-0.89).
CONCLUSIONS
Both prehospital stroke scales and ML models demonstrated varying accuracies in predicting LVO. Despite ML potential for improved LVO detection in the prehospital setting, application remains limited by the absence of prospective external validation, limited sample sizes, and lack of real-world performance data in a prehospital setting.
Topics: Humans; Machine Learning; Emergency Medical Services; Early Diagnosis; Stroke; Ischemic Stroke; Predictive Value of Tests
PubMed: 38874054
DOI: 10.1161/JAHA.123.033298 -
Clinical Cardiology Jun 2024Elevated serum uric acid (sUA) is associated with heart failure (HF).
BACKGROUND
Elevated serum uric acid (sUA) is associated with heart failure (HF).
HYPOTHESIS
Urate-lowering therapy (ULT) in HF is associated with lower risk of HF hospitalization (hHF) and mortality.
METHODS
Data on patients with HF and gout or hyperuricemia in the Clinical Practice Research Datalink database linked to the Hospital Episode Statistics and the Office for National Statistics in the United Kingdom were analyzed. Risks of hHF and all-cause mortality or cardiovascular-related mortality by ULT exposure (ULT initiated within ≤6 months of gout or hyperuricemia diagnosis) were analyzed in a propensity score-matched cohort using adjusted Cox proportional hazards regression models.
RESULTS
Of 2174 propensity score-matched pairs, patients were predominantly male, aged >70 years, with mean ± standard deviation sUA 9.3 ± 1.8 (ULT-exposed) and 9.4 ± 1.9 mg/dL (ULT-unexposed). At 5 years, ULT-exposed patients had a 43% lower risk of hHF or all-cause mortality (adjusted hazard ratio [HR]: 0.57; 95% confidence interval [CI]: 0.51-0.65) and a 19% lower risk of hHF or cardiovascular-related mortality (adjusted HR: 0.81; 95% CI: 0.71-0.92) versus no ULT exposure.
CONCLUSION
ULT was associated with reduced risk of adverse clinical outcomes in patients with HF and gout or hyperuricemia over 5 years.
Topics: Humans; Hyperuricemia; Male; Heart Failure; Female; Aged; United Kingdom; Retrospective Studies; Uric Acid; Gout Suppressants; Risk Factors; Middle Aged; Biomarkers; Treatment Outcome; Gout; Time Factors; Databases, Factual; Follow-Up Studies
PubMed: 38873862
DOI: 10.1002/clc.24297 -
ESC Heart Failure Jun 2024Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF...
AIMS
Heart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD-code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients.
METHODS AND RESULTS
The model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP-HF) and non-HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF-related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP-HF as cases (PPV, 0.86; sensitivity, 0.86). From the 'HF ICD-code universe', we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54).
CONCLUSIONS
These findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.
PubMed: 38873749
DOI: 10.1002/ehf2.14787 -
Frontiers in Robotics and AI 2024Flow state, the optimal experience resulting from the equilibrium between perceived challenge and skill level, has been extensively studied in various domains. However,...
Flow state, the optimal experience resulting from the equilibrium between perceived challenge and skill level, has been extensively studied in various domains. However, its occurrence in industrial settings has remained relatively unexplored. Notably, the literature predominantly focuses on Flow within mentally demanding tasks, which differ significantly from industrial tasks. Consequently, our understanding of emotional and physiological responses to varying challenge levels, specifically in the context of industry-like tasks, remains limited. To bridge this gap, we investigate how facial emotion estimation (valence, arousal) and Heart Rate Variability (HRV) features vary with the perceived challenge levels during industrial assembly tasks. Our study involves an assembly scenario that simulates an industrial human-robot collaboration task with three distinct challenge levels. As part of our study, we collected video, electrocardiogram (ECG), and NASA-TLX questionnaire data from 37 participants. Our results demonstrate a significant difference in mean arousal and heart rate between the low-challenge (Boredom) condition and the other conditions. We also found a noticeable trend-level difference in mean heart rate between the adaptive (Flow) and high-challenge (Anxiety) conditions. Similar differences were also observed in a few other temporal HRV features like Mean NN and Triangular index. Considering the characteristics of typical industrial assembly tasks, we aim to facilitate Flow by detecting and balancing the perceived challenge levels. Leveraging our analysis results, we developed an HRV-based machine learning model for discerning perceived challenge levels, distinguishing between low and higher-challenge conditions. This work deepens our understanding of emotional and physiological responses to perceived challenge levels in industrial contexts and provides valuable insights for the design of adaptive work environments.
PubMed: 38873120
DOI: 10.3389/frobt.2024.1393795 -
Indian Pacing and Electrophysiology... Jun 2024The risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult...
INTRODUCTION
The risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult congenital heart disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate shock rates-mostly caused by T wave oversensing (TWO)- are observed in this population. We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening.
METHODS
Adult patients with ACHD and a control group of normal subjects were fitted with a 24-h Holters to record their S-ICD vectors. Their T:R ratio was analysed utilising phase space reconstruction matrices and a deep learning-based model to provide an in-depth description of the T: R variation plot for each vector. T: R variation was compared statistically using t-test.
RESULTS
13 patients (age 37.4 ± 7.89 years, 61.5 % male, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the two groups (p < 0.001). There was also a significant difference in the standard deviation of T: R between both groups (p = 0.04).
CONCLUSIONS
T:R ratio, a main determinant for S-ICD eligibility, is significantly higher with more tendency to fluctuate in ACHD patients when compared to a population with normal hearts. We hypothesise that our novel model could be used to select S-ICD eligible patients by better characterisation of T:R ratio, reducing the risk of TWO and inappropriate shocks in the ACHD patient cohort.
PubMed: 38871179
DOI: 10.1016/j.ipej.2024.06.003 -
International Journal of Cardiology.... Aug 2024
PubMed: 38868318
DOI: 10.1016/j.ijcha.2024.101433 -
Heliyon Jun 2024Models of the arterial network are useful in studying mechanical cardiac assist devices as well as complex pathological states that are difficult to investigate in-vivo...
Models of the arterial network are useful in studying mechanical cardiac assist devices as well as complex pathological states that are difficult to investigate in-vivo otherwise. Earlier work of artificial arterial tree (AAT) have been constructed to include some of the major arteries and their branches for in-vitro experiments which focused on the aorta, using dipping or painting techniques, which resulted in inaccuracies and inconsistent wall thickness. Therefore, the aim of this work is to use 3D printing for manufacturing AAT based on physiologically correct dimensions of the largest 45 segments of the human arterial tree. A volume ratio mix of silicone rubber (98 %) and a catalyst (2 %) was used to create the walls of the AAT. To validate, the AAT was connected at its inlet to a piston pump that mimicked the heart and capillary tubes at the outlets that mimicked arterial resistances. The capillary tubes were connected to a reservoir that collected the water which was the fluid used in testing the closed-loop hydraulic system. Young's modulus of the AAT walls was determined using tensile testing of different segments of various wall thickness. The developed AAT produced pressure, diameter and flow rate waveforms that are similar to those observed in-vivo. The technique described here is low cost, may be used for producing arterial trees to facilitate testing mechanical cardiac assist devices and studying hemodynamic investigations.
PubMed: 38867983
DOI: 10.1016/j.heliyon.2024.e31764 -
European Heart Journal Supplements :... Apr 2024In the ESC 2023 guidelines, cardiomyopathies are conservatively defined as 'myocardial disorders in which the heart muscle is structurally and functionally abnormal, in...
In the ESC 2023 guidelines, cardiomyopathies are conservatively defined as 'myocardial disorders in which the heart muscle is structurally and functionally abnormal, in the absence of coronary artery disease, hypertension, valvular disease, and congenital heart disease sufficient to cause the observed myocardial abnormality'. They are morpho-functionally classified as hypertrophic, dilated, restrictive, and arrhythmogenic right ventricular cardiomyopathy with the addition of the left ventricular non-dilated cardiomyopathy that describes intermediate phenotypes not fulfilling standard disease definitions despite the presence of myocardial disease on cardiac imaging or tissue analysis. The new ESC guidelines provide 'a guide to the diagnostic approach to cardiomyopathies, highlight general evaluation and management issues, and signpost the reader to the relevant evidence base for the recommendations'. The recommendations and suggestions included in the document provide the tools to build up pathways tailored to specific cardiomyopathy (phenotype and cause) and define therapeutic indications, including target therapies where possible. The impact is on clinical cardiology, where disease-specific care paths can be assisted by the guidelines, and on genetics, both clinics and testing, where deep phenotyping and participated multi-disciplinary evaluation provide a unique tool for validating the pathogenicity of variants. The role of endomyocardial biopsy remains underexploited and confined to particular forms of restrictive cardiomyopathy, myocarditis, and amyloidosis. New research and development will be needed to cover the gaps between science and clinics. Finally, the opening up to disciplines such as bioinformatics, bioengineering, mathematics, and physics will support clinical cardiologists in the best governance of the novel artificial intelligence-assisted resources.
PubMed: 38867869
DOI: 10.1093/eurheartjsupp/suae002 -
European Heart Journal Supplements :... Apr 2024Patients with advanced heart failure, due to the instability of their clinical conditions, need close surveillance to avoid dangerous exacerbations or sudden events....
Patients with advanced heart failure, due to the instability of their clinical conditions, need close surveillance to avoid dangerous exacerbations or sudden events. Digital technology can be of great help in this contest, thanks to remote monitoring, made possible with the use of wearable or implantable instruments. The latter are currently generally inserted inside defibrillators or resynchronization systems, or inserted inside the pulmonary circulation for monitoring pulmonary pressure. Parameters such as thoracic impedance, physical activity, heart rate variability, atrial and ventricular arrhythmias, blood pressure, and O saturation can be controlled remotely. The data relating to the actual benefit in terms of avoidable events (death and hospitalizations) are not definitive, but certainly from an organizational point of view, the benefit is evident, both on the part of the patient and of the organization of care. The latter, provided in the form of televisits, requires a re-modulation of the system, making use of trained personnel, a well-structured network, and digital technologies (platforms, electronic health records) that are not yet perfectly developed. The evolution of the solutions offered by artificial intelligence guarantees a rapid and progressive refinement of telemedicine in this sector.
PubMed: 38867862
DOI: 10.1093/eurheartjsupp/suae026 -
European Heart Journal Supplements :... Apr 2024Arterial hypertension represents the most important cardiovascular risk factor with a direct responsibility for a large share of cardiovascular mortality and morbidity...
Arterial hypertension represents the most important cardiovascular risk factor with a direct responsibility for a large share of cardiovascular mortality and morbidity in the world. Despite the wide availability of antihypertensive therapies with documented effectiveness, blood pressure control still remains largely unsatisfactory in large segments of the population. Guidelines for the management of arterial hypertension suggest the preferential use of five classes of drugs-angiotensin-converting enzyme inhibitors, angiotensin II type I receptor inhibitors, calcium channel blockers, thiazide/thiazide-like diuretics, and beta-blockers-recommending the use of combination therapy, preferably in pre-established combinations, for the majority of hypertensive patients. The evidence of a non-negligible heterogeneity in the response to different antihypertensive drugs in different patients suggests the opportunity for personalization of treatment. The notable phenotypic heterogeneity of the population of hypertensive patients in terms of genetic structure, behavioural aspects, exposure to environmental factors, and disease history imposes the need to consider all the potential determinants of the response to a specific pharmacological treatment. The progressive digitalization of healthcare systems is making enormous quantities of data available for machine learning systems which will allow the development of management algorithms for truly personalized antihypertensive therapy in the near future.
PubMed: 38867857
DOI: 10.1093/eurheartjsupp/suae019