bluuebunny/crossref_metadata_2025_split huggingface dataset:
Role of the cardiology pharmacist in improving quality of care for patients with acute myocardial infarction based on ESC-ACCA quality indicators
Authors: Al Farhan H , Yaseen I F 
Published: October 2024

Abstract

Background
The European Society of Cardiology-Acute Cardiovascular Care Association (ESC-ACCA) suggested the use of quality indicators (QIs) to improve quality of care for patients with acute myocardial infarction (AMI). Among these QIs are prescribing of angiotensin-converting enzyme inhibitor (ACEi) and beta blocker (BB) if left ventricular ejection fraction (LVEF) was ≤40%, and prescribing high intensity statin and dual antiplatelet therapy (DAPT) before discharging patients from the coronary care unit (CCU).

Purpose
To identify the role of cardiology clinical pharmacy specialist in improving quality of care for patients with AMI based on ESC-ACCA QIs.

Methods
A cross-sectional study was conducted at a single CCU in Iraq between January 2021 and August 2023. Patients with AMI with or without LVEF ≤40% were included in the study. Patients' profiles were reviewed to collect data about suggestions by the cardiology clinical pharmacy specialist to initiate high intensity statin, addition of a second antiplatelet therapy to keep patients on DAPT, or addition of ACEi and/or BB if LVEF ≤40%.

Results
Total number of patients included was 326 of whom 119 (37%) patients had LVEF ≤40%. Drug interventions by the cardiology clinical pharmacy specialist with the initiation of statin and addition of a second antiplatelet therapy to optimize DAPT for patients with AMI and initiation of ACEi and/or BB for patients with AMI and LVEF ≤40% are shown in table 1 and figure 1. Drug interventions were based on the updated ESC guidelines and agreed by the interventional cardiologists.

Conclusions
Role of the cardiology clinical pharmacy specialist was important in bridging gaps in the medical therapy and improving quality of care for patients with AMI and/or LVEF ≤40% by optimizing patients medications particularly for the initiation of ACEi. ACEi was initiated in about one-quarter of the patients with AMI and LVEF ≤40% based on the suggestion of the cardiology clinical pharmacy specialist and approved by the interventional cardiologists.
Association of 5-times sit-to-stand test with muscle weakness and prognosis in older patients with cardiovascular disease
Authors: Tsubaki Y , Kamiya K , Hamazaki N , Nozaki K , Ichikawa T , Uchida S , Noda T , Ueno K , Miki T , Ogura K , Hotta K , Maekawa E , Yamaoka-Tojo M , Matsunaga A , Ako J 
Published: October 2024

Abstract

Background/Introduction
In the elderly with cardiovascular disease (CVD), maintaining muscle strength and physical function is essential for preventing adverse outcomes like falls and hospitalizations. The decline in lower limb strength signals increased morbidity and mortality risks. The 5-times sit-to-stand test (5STS) is a practical method for assessing lower limb strength, yet its predictive value for muscle weakness and prognosis in older CVD patients is underexplored.

Purpose
This study aims to elucidate the 5STS's role in identifying muscle weakness and its impact on the prognosis of older patients with CVD, offering insights for targeted interventions.

Methods
We included 3585 patients aged ≥ 65 years with CVD (mean age, 75.8 ± 6.6 years, 64.5% male) who underwent assessment of the 5STS before discharge. Maximal quadriceps isometric strength (QIS) was evaluated as a marker of lower limb strength and expressed relative to body weight (% body weight: BW). The primary outcome was all-cause mortality. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for each sex to calculate the cut-off values of the 5STS for low QIS (male &lt; 45%BW, female &lt; 35%BW), which have been reported to be associated with prognosis. In addition, we performed Kaplan-Meier curves and Cox regression analyses in five groups defined by 5STS time quartile and 5STS inability to assess the prognostic significance of 5STS. Cox regression analysis was performed as a multivariate model including the following variables; age, sex, body mass index, acute coronary syndrome, heart failure, hypertension, diabetes, dyslipidemia, current smoker, prior myocardial infarction, prior heart failure, prior chronic obstructive pulmonary disease, prior dementia, albumin, hemoglobin, estimated glomerular filtration rate, C-reactive protein.

Result
Over a median follow-up of 2.23 years (interquartile range 0.87-4.52 years), 657 (18.3%) patients died. Kaplan-Meier analysis indicated that both slow 5STS performance and inability to execute the 5STS were significantly linked with poorer prognosis (log-rank p &lt; 0.001, Figure 1). After comprehensive adjustment, the slowest quartile and inability to perform the 5STS were associated with significantly higher mortality rates compared to the fastest quartile (HR 1.34; 95% CI: 1.05-1.72; p = 0.021; HR: 2.28; 95% CI: 1.60-3.24; p &lt; 0.001, Figure 2). The ROC AUC for 5STS in identifying low QIS was 0.75 (95% CI: 0.73-0.77) for males and 0.75 (95% CI: 0.72-0.77) for females, with cut-off times established at 9.71s for males and 9.48s for females.

Conclusion
The 5STS, assessable in diverse settings without specialized equipment and reflecting key clinical outcomes, could serve as a standard tool for evaluating lower limb function.
ECGWiz: a unified and generalizable AI model for comprehensive ECG diagnosis
Authors: Vatsaraj I , Unde I , Loeffler S , Mohsen Y , Aronis K , Carrick R , Wu K , Chrispin J , Trayanova N 
Published: October 2024

Abstract

Background
Deep learning has demonstrated diagnostic performance in electrocardiogram (ECG) analysis comparable to that of clinicians. However, existing models primarily excel in diagnosing common conditions such as arrhythmias and heart blocks, which are easily identifiable using standard predictor variables. Conversely, models aiming to uncover novel insights, such as detecting structural changes or the presence of myocardial scar, often lack generalizability due to insufficient data, model architecture limitations, and other factors. Moreover, these models frequently encounter challenges, such as a decline in performance when applied to external datasets. To address these issues comprehensively, we propose the ECGWiz — a unified model capable of tackling diverse ECG-related problems with consistently high performance.

Objective
To develop a generalizable Foundation AI model for ECG diagnosis capable of detecting diverse cardiac abnormalities and diseases using few training samples.

Methods
We utilized publicly available datasets like PTB-XL, CPSC, etc. To build our model, totaling approximately 80,000 ECGs. These 12 lead ECGs are scaled and filtered before using them for training. The AI model is pretrained using Self Supervised Learning and Contrastive Learning strategies. Further, the model undergoes supervised training using a hierarchy of labels that increase in granularity, encompassing arrhythmias, heart blocks, other cardiac abnormalities, heart failure, sudden cardiac death and more. Lastly, the model is prepared for Few-Shot Learning — where the model learns from a minimal set of samples (5 to 10 ECGs) of the target disease. For testing the model, we are using unique datasets like PROSE which consists of non-ischemic cardiomyopathy patients with and without myocardial scaring detected using cardiac MRI imaging at the Johns Hopkins University.

Results
Our model, trained on a dataset of only 5 ECG recordings, demonstrated a 74% accuracy in identifying myocardial scarring as detected by LGE MRI, utilizing a standard 12-lead ECG. This performance is comparable to that of conventional deep learning methods, which necessitate datasets containing upwards of 120 ECG recordings.

Conclusion
Our model represents a promising stride in creating a unified and adaptable foundation AI model for comprehensive ECG-based diagnosis. Its capacity to learn from a minimal set of examples signifies a robust framework that can evolve and adapt to new challenges in ECG diagnosis. Ongoing improvements, particularly through dataset expansion, underscore its potential to enhance cardiovascular diagnostics, offering precise and efficient patient care.
Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis
Authors: Sangha V , Oikonomou E K , Krumholz H M , Miller E J , Khera R 
Published: October 2024

Abstract

Background
Transthyretin amyloid cardiomyopathy (ATTR-CM) remains largely under-recognized, under-diagnosed, and under-treated. We hypothesized that the myocardial remodeling seen in ATTR-CM may be detectable through artificial intelligence (AI) applied to 12-lead electrocardiographic (ECG) images.

Purpose
To develop an AI-ECG algorithm that can detect ATTR-CM from images of 12-lead electrocardiograms.

Methods
Across 5 hospitals of a large U.S.-based hospital system, we identified patients with ATTR-CM, defined by the presence of a positive bone scintigraphy scan or pharmacotherapy with an approved transthyretin stabilizer between 2015 and the first half of 2023. The development cohort consisted of 1,011 ECGs from 234 patients (age 79 [IQR:70-85], n=176 (17.4%) women), who were age- and sex-matched in a 10:1 ratio to 10,110 ECGs from 10,110 controls (age 79 [IQR:70-85] years, n=1,800 [17.7%] female). A convolutional neural network (CNN) pre-trained using a bio-contrastive pretext on ECGs before 2015 was fine-tuned for ATTR-CM using 5-fold cross-validation and subsequently tested in an independent set of cases (139 ECGs in 47 patients; age 80 [75-86] years, n=44 (31.7% women)) and matched controls (1390 ECGs and patients) from the second half of 2023.

Results
The AUROC (area under the receiver operating characteristic curve) of the AI-ECG model for discriminating ATTR-CM in the cross-validation set was 0.90 [95%CI: 0.89-0.91], and in the leave-out, temporally distinct dataset was 0.915 [95%CI: 0.89-0.94] (A), with a sensitivity of 0.85 [95%CI: 0.79-0.91] and specificity 0.80 [95%CI 0.78-0.82]. Representative saliency maps are shown in panel B. In simulations, the positive predictive value ranged from 0.12 [95%CI: 0.10-0.13] at a 3% prevalence level to 0.59 [95%CI: 0.56-0.62] at 25% prevalence.

Conclusions
We demonstrate that AI applied directly to ECG images represents a promising approach for the screening of ATTR-CM.
Smart watch for the detection of exercise-induced transient ST segment changes
Authors: Spaccarotella C , Polimeni Alberto P A , Ferraro Daniel F D , Franzone Anna A F , Piccolo Raffaele P R , Curcio Antonio C A , Indolfi Ciro C I , Giovanni Esposito E G 
Published: October 2024

Abstract

Background
Smartwatches are largely used and allowed to perform electrocardiogram one lead, usually a modified DI, used to diagnose atrial fibrillation. It is unknown whether or not smartwatches could be used to identify electrocardiographic transient ST changes during exercise-induced myocardial ischemia. The present study assessed the feasibility and reliability of using an Apple watch to record V5 in subjects scheduled for standard ECG stress tests.

Methods
One hundred eighty-nine subjects were included in this study. The mean age was 61+9 years, and 21.6 % were women (41 subjects). A single thoracic lead V5 was obtained with an Apple watch, positioned at the fifth intercostal space anterior axillary line, by creating a circuit between the back of the watch and the right index resting on the crown for 30 seconds. Four subjects were excluded because of the poor quality of the Apple watch signal. Forty-nine patients (25,92%) had a positive stress test with standard 12 lead ECG, 136 (71,96 %) negative stress test, and 4 (2,12%) the ECGs were non-diagnostic. The mean difference or bias between the Apple Watch and standard exercise-induced ST changes was quantified as 0.054, suggesting a generally minimal discrepancy in their readings. This consistency is critically affirmed by Cohen's Kappa (k) coefficient of 0.665. 9-1.0)

Results
A total of 189 subjects were included in the study; the mean age was 61 years (SD 9), and 21.6 % were women (41 subjects). Insightful outcomes were gleaned from the Bland-Altman analysis, which probed the concordance between the smartwatch ECG and the conventional standard ECG in measuring ST-segment deviations (Bias 0,054; SD 0.31, from -0.56 to 0.67, 95% limits of agreement), as vividly depicted in Figure. The dashed blue lines in Figure demarcate the 95% limits of agreement, which span from -0.56 to 0.67, offering a tangible visualization of the range within which most individual differences resided. A notable harmony was unveiled in the intricate exploration of the concordance between the two diagnostic methodologies, substantiated by an agreement of 87.6%. This demonstrates that, in most instances, the methodologies provided analogous results, exhibiting a commendable consistency between them in a substantial proportion of cases.
Assuming the results of standard ECGs as the reference values, Apple Watch ST deviation showed a sensitivity of 69% and specificity of 94 %. The positive predictive value was 0.80952381, and the negative predictive value was 0,8951049.

Conclusions
In conclusion, this study demonstrated that the Apple Watch could detect transient ST changes with a sensitivity of 69% and a specificity of 94 %. Further prospective studies should be performed to assess the potential role of detecting transient myocardial ischemia with a smartwatch in a general population at risk.
Deep learning for quantification of myocardial scar and microvascular obstruction in late gadolinium enhancement cardiovascular magnetic resonance images
Authors: Leng S , Yang X L , Tan R S , Zong D X , Cheng J , Sia C H , Low A S , Chan M , Hausenloy D J , Zhong L 
Published: October 2024

Abstract

Objectives
Detecting and measuring myocardial scar and microvascular obstruction (MVO) accurately following reperfusion therapy is clinically vital for patients with acute myocardial infarction (AMI). Current clinical practice relies on manual delineation of endocardial borders as well as hyper- and hypo-enhanced regions on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) images for scar and MVO quantification, respectively. However, this approach is subjective, labor-intensive, and time-consuming. The objective of this study is to develop and assess a convolutional neural network (CNN)-based method for the automated quantification of LGE scar and MVO in AMI patients.

Methods
A total of 1836 LGE images from 252 patients with AMI were analyzed, with the cohort comprising 232 males and an average age of 56±9 years. Our approach, utilizing a combined architecture of cascaded YOLOv8 (You Only Look Once version 8) and nnU-Net, was implemented for the simultaneous segmentation of the left ventricular (LV) myocardium, scar, and MVO in LGE images. Independent datasets for training, validation, and testing were maintained in an 8:1:1 ratio. Performance evaluation was conducted by comparing the results to manual scar and MVO quantification, utilizing metrics such as the Dice similarity coefficient (DSC) and Bland-Altman analysis.

Results
The automated segmentation process took 0.05 seconds per image. The segmentation accuracy, as assessed by the DSC, yielded values of 0.96±0.04 (per-patient, n=25) and 0.96±0.05 (per-slice, n=184) for scar segmentation, and 0.86±0.17 (per-patient) and 0.89±0.15 (per-slice) for MVO segmentation. In per-patient analysis, the volumes of automatically segmented scar and MVO demonstrated strong agreement with manually derived results, with biases [limits of agreement] of -0.3 [-3.0, 2.4] cm³ and 0.1 [-0.2, 0.3] cm³, respectively. Furthermore, there was strong concordance between automatically and manually derived scar size as a percentage of LV mass (-0.2 [-3.1, 2.7] %) and MVO size as a percentage of scar (1.2 [-5.6, 8.0] %).

Conclusions
The findings demonstrate excellent agreement between our proposed automated deep-learning solution and clinical expert annotation, suggesting its potential as a predictive tool for prognosis and clinical outcomes in patients with AMI.
Machine learning model using heart rate variability for the prediction of vasovagal syncope
Authors: Cho J H 
Published: October 2024

Abstract
Background and Aims:
Vasovagal syncope (VVS) is a reversible condition that can cause serious events, such as cerebral hemorrhage due to head trauma. Its occurrence is difficult to predict; therefore, this study aimed to develop machine learning (ML) models using heart rate variability (HRV) to predict the occurrence of VVS.

Methods and Results
We investigated 80 patients with syncope who underwent Holter monitoring during the head-up tilt test. Twenty-six ML models for the prediction of VVS using HRV parameters were examined. We randomly split the subjects into training and testing sets at a 7:3 ratio (training set: 46 positive and 9 negative subjects; testing set: 21 positive and 4 negative subjects). The XGB classifier achieved the highest performance compared with other algorithms (accuracy, 0.72; area under the curve, 0.801; sensitivity, 0.857; specificity, 0.621). When examining the time taken to predict syncope occurrence using the developed prediction model, it was found that the model could forecast approximately 3 min (equivalent to 193.2 beats) prior to the onset of symptoms.

Conclusion
The ML model utilizing HRV parameters was proficient in preemptively predicting VVS incidents. In practical applications, this approach can effectively enhance VVS prediction by leveraging the HRV, thereby aiding in the prevention of unforeseen accidents.
Identifying heart failure dynamics using multi-point electrocardiograms and deep learning
Authors: Nishihara Y , Nishimori M , Shibata S , Shinohara M , Hirata K , Tanaka H 
Published: October 2024

Abstract

Background
Heart failure (HF) hospitalizations are related to poor survival outcomes, emphasizing the need for early detection and intervention. Recent studies have reported the potential of deep learning algorithms in identifying heart failure through electrocardiogram (ECG) with notable accuracy. However, evidence supporting their utility in the ongoing monitoring and follow-up of HF patients remains uncertain. Advanced diagnostic tools that can effectively track heart failure progression or improvement over time are needed.

Purpose
To address this challenge, we developed a deep learning model analyzing 12-lead ECG waveforms at two time points, aiming to detect HF deterioration or improvement, potentially revolutionizing HF management.

Methods
This study analyzed 32,045 ECGs from 6,892 unique adult patients (aged 18 years and older) visiting the Department of Cardiology at Kobe University Hospital. The participants were randomly assigned to training (4,284 patients), validation (1,034 patients), and test (1,034 patients) datasets. ElecTransformer was developed to classify HF status into deteriorated, improved, and no change classes based on ECG waveform signals at two different time points and to estimate brain natriuretic peptide (BNP) levels. Performance metrics, such as the area under the receiver operating characteristic curve (AUROC) and accuracy, were calculated, and attention mapping via gradient-weighted class activation mapping was utilized to interpret the model's decision-making ability.

Results
The patients had an average age of 65 years (±15.4 years) and BNP levels averaged 71.5 pg/mL (25.8 to 185.2 pg/mL). For HF status classification, ElecTransformer achieved an AUROC of 0.845 and an accuracy of 0.821, improving to an AUROC of 0.885 and an accuracy of 0.833 with baseline BNP integration. The ElecTransformer accurately estimated BNP levels from ECG data, evidenced by a correlation coefficient of 0.798. Averaged attention mapping showed that the QRS complex was the most important feature for determining model outputs with lesser effects seen for P waves and T waves variance

Conclusions
Harnessing a Transformer-based deep learning model to analyze multiple ECGs markedly improves the accuracy in detecting changes in heart failure status. This method offers a valuable non-invasive option for monitoring heart failure progression, potentially leading to better patient management and earlier intervention in heart failure treatment.Architecture and Performance of ModelsAveraged attention mapping
Personalized prediction model specific for sudden cardiac death in the population: a machine learning study based on EHR
Authors: Chocron R , Laurenceau T , Youssif Y , Bougouin W , Beganton F , Empanan J P , Chopin N , Jouven X 
Published: October 2024

Abstract

Background
Predicting sudden cardiac death (SCD) in the general population remains a significant challenge, particularly at the individual level. SCD and myocardial infarction (MI) share similar pattern of characteristics and risk factors, making their specific prediction highly difficult. As a result, the current prediction models exhibit low specificity.

Methods
To estimate the specific risk of SCD over three months, we trained a machine learning model on electronic health record (EHR) data representing 8,566,229 drug prescriptions and 801,352 of hospital diagnoses up to five years prior to SCD. The data were obtained from a French cohort of 12,338 SCD and from a cohort of 12,338 controls from 2011 to 2015. We then validated the results on two external cohorts: one temporal cohort in the same area between 2016 and 2020 with 11,620 SCD cases and 11,620 controls and one geographical cohort from the USA with 892 SCD cases and 892 controls from 2013 to 2021. In order to address the specificity of our prediction model for SCD, SCD cases collected between 2016 and 2020 were also matched with a group of 35 000 controls who had myocardial infarction, with no SCD, following the same protocol as for the controls issued from the general population.
Findings: The analysis reveals three distinct patterns of distribution for SCD cases, controls, and MI cases. We observed a semi linear increase of SCD cases over the deciles of predicted risk, and our model detected 24% (2,908) of SCD cases with a predicted risk exceeding 90% (highest decile). We observed the opposite among the controls that decreased linearly with increasing deciles of predicted risk: most of the controls were accurately identified in the lowest deciles, and there were only 214 controls (2%) in the highest decile. Interestingly, we observed a relatively stable distribution of MI cases across the deciles, with 10% of MI cases in the highest decile (Figure 1).
Interpretation: We developed and validated a personalized prediction model that is able to identify subjects at high-risk for SCD specifically.
Figure 1: Histogram of predicted risks for sudden cardiac death cases, controls and myocardial infarction cases in the validation cohort.Figure 1