bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of inpatient cardiac rehabilitation on hospital-associated disability for older patients undergoing transcatheter aortic valve implantation: insight from the JROAD/JROAD-DPC database
Authors: Katano S , Kono Y , Yano T , Kanaoka K , Sawamura A , Motokawa T , Miyamoto Y , Ohya Y , Miura S , Fukuma N , Makita S , Izawa H 
Published: October 2024

Abstract

Background
Hospital-associated disability (HAD), a decline in the ability to perform activities of daily living (ADL) during hospitalization, is a modifiable target in the integrated care for older adults. Inpatient rehabilitation during the perioperative period is a part of a comprehensive cardiac rehabilitation (CR) program and plays a pivotal role in functional recovery and smooth reintegration into the home, community, and workplace after surgery. Prior research, including meta-analyses, have shown that outpatient CR participation after discharge of patients who underwent transcatheter aortic valve implantation (TAVI) and surgical aortic valve replacement is associated with a significant improvement in exercise capacity and indexes of ADL. However, the impact of inpatient CR on the occurrence of HAD remains unexplored.

Purpose
Using a large real-world nationwide database in Japan, the aim of this study was to determine the prevalence of HAD and the impact of inpatient CR participation on the development of HAD in older patients undergoing TAVI.

Methods
Older patients undergoing TAVI were extracted from the Japanese nationwide database (JROAD-DPC) from April 2014 to March 2021 and were divided into patients receiving inpatient CR (CR group) and non-CR group. HAD was defined as a decrease in the Barthel Index score ≥ 5 points at discharge relative to admission.

Results
Of 19,789 eligible patients, 17,066 (86%) underwent inpatient CR (average age, 84.3 ± 4.9 years; Woman, 65%). The overall prevalence of HAD was 9.6%, with a lower incidence in the CR group than those in the non-CR group (8.8% vs. 14.2%, p &lt; 0.01). Participation in inpatient CR was associated with a significantly lower odds ratio (OR) of HAD in multivariate logistic regression analysis (adjusted OR, 0.62; 95% confidence interval [CI], 0.54–0.70; p &lt; 0.01) and in inverse probability of treatment weighting-adjusted logistic regression analyses (adjusted OR, 0.82; 95% CI, 0.77–0.88; p &lt; 0.01; Table). Analyses in subgroups of interest showed heterogeneity of the effects of inpatient CR with possible loss in patients with a body mass index of less than 18.5 kg/m² or BI score of less than 60 points at admission (Figure).

Conclusions
A substantial number of older patients undergoing TAVI developed HAD during perioperative period. Participation in inpatient CR was associated with a lower likelihood of HAD in TAVI recipients, but the benefit of inpatient CR for development of HAD was possibly lost in patients with a low BMI and low a BI score at admission, indicating the necessity of HAD-targeted multidisciplinary CR for lean patients with ADL disability before TAVI.Figure
Tell me and I forget, teach me and I may remember, involve me and I learn: impact of simulation training on performance in electrophysiology
Authors: Kozlik M , Kosiuk J , Cogiel M , Kost J , Hemmerling D , Staniszewski M , Szczesna A , Wojakowski W , Jadczyk T 
Published: October 2024

Abstract

Background
Interpretation of intracardiac electrograms plays a key role in electrophysiology (EP) training. However, there had been a substantial gap between theoretical knowledge and practical skills acquired in the clinical setting. With the development of an online life-like EP-simulator, interactive training has never been so easily accessible.

Purpose
The aim of this study is to assess the efficiency of simulation-based training in electrophysiology, with the help of EP-simulator, by comparing it to traditional-learning methods.

Methods
340 participants attended a 3-hour, online video-lecture on basic electrophysiology. Then, they were randomised 1:1 into a traditional-learning and simulation-learning groups. Both groups received presentation slides from the lecture and two textbook recommendations. In addition, simulation-learning group obtained access to online EP-simulator. This was followed by one week of self-learning. At the end, their knowledge was assessed using an online multiple-choice test.

Results
Eventually, 91 participants from the traditional-learning group and 67 participants from the simulation-learning group took the final test. The simulation-learning group, which obtained additionally an access to EP-simulator, scored significantly better than the traditional-learning group (mean score=15.10 vs 13.16, p=0.022).

Conclusion
EP-simulator can possibly contribute to better learning results. Including simulation in training programs may have an impact on educational outcomes.Course of the studyEP-simulator interface
Mobile single-lead ECG atrial fibrillation prediction enhancement integrated by standard ECG algorithm with Deep learning model
Authors: Lee K H , Lee M E , Choi H W , Lee Y H , Kim Y R , Yoon N S , Park H W 
Published: October 2024

Abstract

Background
Artificial intelligence (AI) using electrocardiogram (ECG) enabled to predict atrial fibrillation (AF) in patients without documented AF. Mobile single-lead ECG is more convenient to surveil cardiac rhythm with simple measurement. However, AI-enabled arrhythmia predictability by mobile ECG is limited due to single channel utilization and longer duration for arrhythmia diagnosis. We aimed to enhance mobile single-lead ECG AF prediction AI algorithm integrated with 12-lead ECG using deep learning model.

Method
Based on 552,372 12-lead ECG data of 318,321 patients, a statistical AF prediction model employing a deep-learning approach was constituted. Out of single-lead 13,509 ECGs from a total of 6,719 patients, we utilized a total of 10,287 normal sinus rhythm ECGs from 5,170 patients. Resnet structure was utilized to distinguish subtle changes of the vicinity of P-wave. Single-lead mobile ECGs were adjusted with noise filtering and segmented every 10 seconds. A random under-sampling was applied to reduce bias from data imbalance. Both 12-lead ECG and single-lead ECG were allocated to training, validation, testing datasets in a 6:2:2 ratio. Then, we conducted transfer learning using the standard 12-lead ECG’s deep learning model to improve performance of single-lead mobile ECG deep learning model.

Results
AF was annotated in 26,541 (4.8%) with 12-lead ECG whereas 1,443 (21.2%) with single-lead mobile ECG. The area under the curve (AUC) value for predicting AF was 0.910 with 12-lead ECG, and 0.742 with mobile ECG. The predictive performance of mobile ECG was 67.5% in accuracy, 64.2% in sensitivity and 66.8% in F1-score. The AUC value of mobile ECG after applying transfer learning based on 12-lead ECG for AF prediction was increased to 0.790 with accuracy of 73.8%, sensitivity of 65.5% and F1-score of 71.0%.

Conclusion
Integration with deep learning algorithm of standard 12-lead ECG significantly improved the model performance of single-lead ECG AF prediction model compared to single-lead mobile ECG only based model. Easy application of mobile ECG with enhanced AF predictability might serve a more convenient method as a pre-emptive assistive tool to provide probabilistic prediction for PAF screening rather than 12-lead ECG.
Machine learning approach for detection of valvular heart disease through digital cardiac auscultation: results from multi-center prospective cross-sectional study
Authors: Jung I H , Bae S A , Kim M K , Moon I K , Seo H S , Chang H J 
Published: October 2024

Abstract

Background
Cardiac auscultation, an essential bedside diagnostic tool, often identifies significant valvular heart disease through murmurs. However, its accuracy is contingent upon the clinician's expertise, leading to a growing dependence on expensive imaging techniques. This study aimed to develop and validate a machine learning model for detecting valvular heart disease from cardiac auscultation.

Methods
The digital cardiac auscultation and associated echocardiograms were prospectively collected from three sites between May 1, 2022, and Dec 31, 2022. We trained, validated, and externally tested a machine learning based model to classify aortic stenosis, mitral stenosis, aortic regurgitation, mitral regurgitation, tricuspid regurgitation, oveall valvular heart disease from cardiac auscultation, age, sex, and comorbidity. We condcted training, and internal testing using datasets for eXtreme Gradient Boosting (XGBoost) machine learning model from hospital one and hospital two. The dataset from hospital three was employed for external testing. We evaluated the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity.

Result
We selected the cardiac auscultation and echocardiograms of a total of 4,321 patients, excluding those without valvular heart disease information, congenital heart disease, a history of valvular operation, and cardiac auscultation could not be interpreted. The diagnostic performance of heart murmurs for valvular heart disease across the three datasets generally demonstrated high sensitivity and PPV. Specifically, the sensitivity for aortic stenosis was 89.1%, mitral stenosis 88.8%, aortic regurgitation 88.5%, mitral regurgitation 89.8%, tricuspid regurgitation 90.1%, and for any valvular heart disease, it was 95.0%. However, the NPV was consistently lower, with values of 16.4%, 14.2%, 12.2%, 23.1%, 26.2%, and 65.3% for the respective conditions. In XGBoost machine learning model, the AUROC values for internal testing (N=1,403), along with their 95% confidence intervals, were as follows: aortic stenosis (0.93, 0.88–0.97), mitral stenosis (0.91, 0.81–0.97), aortic regurgitation (0.84, 0.79–0.90), mitral regurgitation (0.94, 0.90–0.98), tricuspid regurgitation (0.81, 0.75–0.87), and overall valvular heart disease (0.86, 0.82–0.89). For external testing (N=814), the AUROC values were: aortic stenosis (0.90, 0.83–0.96), mitral stenosis (0.88, 0.79–0.95), aortic regurgitation (0.94, 0.93–0.96), mitral regurgitation (0.93, 0.92–0.95), tricuspid regurgitation (0.89, 0.87–0.91), and overall valvular heart disease (0.92, 0.90–0.94).

Conclusion
The machine learning based model can accurately classify valvular heart diseases using information from digital cardiac auscultation.Figure 1.Study FlowFigure 2.AUC curve
Multivariate time-series clustering of cardiopulmonary exercise test data for cardiovascular risk stratification
Authors: Ntalianis E , Cauwenberghs N , Claes J , Sabovcik F , Cornelissen V , Kuznetsova T 
Published: October 2024

Abstract

Aims
Current clinical practice assesses cardiorespiratory fitness (CRF) by evaluating summary indexes of cardiopulmonary exercise tests (CPET). Yet, the raw time series recordings may hold additional information with clinical relevance. In this study, we investigated if the interpretation of raw CPET data could identify distinct CRF phenogroups and improve risk stratification.

Methods
In 1399 participants (mean age, 56.4 years; 37.7% women), who underwent maximal CPET by cycle ergometer, we collected baseline clinical characteristics and information on incident cardiovascular (CV) events (n=297) on average 4.3 years later. We employed dynamic time warping combined with k-medoids to identify phenogroups from raw CPET time-series. To evaluate the clinical significance of the derived phenogroups we compared clinical characteristics and incidence of CV events, while an external population cohort (n=171) was used to further validate the trained model.

Results
The optimal number of clusters was 5. We observed significant differences in age, use of medication, spirometry indexes and disease prevalence across all clusters. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and history of CV disease while cluster 1 represented the most favourable risk profile. After adjusting for traditional clinical covariables, clusters 4 (HR: 1.30; 95%CI: 1.02-1.65; p=0.033) and 5 (HR: 1.36; 95%CI: 1.08-1.72; p=0.0088) had significantly higher risk for incident CV events compared to clusters 1 and 2. Comparing clinical characteristics across clusters in the external validation cohort revealed similar patterns. Conclusion – Employing unsupervised machine learning on time series CPET recordings, we identified clinically meaningful CRF phenogroups. An integrative CRF profiling might facilitate CV risk stratification and consequently risk management.
Fully automated artificial intelligence-based cine cardiac magnetic resonance image quantification
Authors: Mohammadi Kazaj P , Baj G , Schutze J , Boscolo Berto M , Stark A , Valenzuela W , Graeni C , Shiri I 
Published: October 2024

Abstract

Background
Analyzing cine cardiac magnetic resonance (CMR) images is a task that demands substantial experience and time, and it is subject to both inter- and intra-observer variability. Existing automated tools in the clinics for image analysis often necessitate substantial editing to enable accurate quantification.

Objective
To tackle this issue, in this study, we developed and validated a fully automated artificial intelligence (AI)-based system for the analysis of cine CMR images, aiming to address these challenges.

Method
In this study, 1,038 patients with CMR images were enrolled. Out of these, 350 CMR images, along with corresponding segmentations for the left ventricle myocardium (LVM) and the left- and right ventricles (LV and RV, respectively), sourced from open access data from 5 centers and databases (with different cardiac diseases), were utilized for model development. The remaining images from two local registries were used to evaluate the model. A 2D U-Net network with deep supervision, enhanced with various image augmentation techniques, was developed for automatic segmentation. The developed model was subsequently utilized to segment the test dataset of 4D (3D+time) short-axis cine CMR images. End-diastolic volumes (EDV) and end-systolic volumes (ESV) were automatically detected from these segmentations and evaluated against the ground truth calculations performed by two experienced physicians using commercially validated software.

Results
In the test set, Dice scores of 0.84, 0.90, and 0.93 were achieved for the RV, LV, and LVM, respectively. The mean percentage error for EDV was 0.6% for LV and -6.32% for RV. Regarding ESV, the mean percentage error was 2.95% for LV and -6.32% for RV. A -6.95% error was achieved for LV mass calculation. The stroke volumes for LV and RV were calculated with a mean percent error of -2.52% and -8.43%, respectively. We achieved a mean percent error of -3.22% for LV and -1.64% for RV ejection fraction.

Conclusion
The fully automated AI-based quantitative analysis of cine cardiac MR images developed in this study demonstrated high agreement with the ground truth across large datasets, including those from external centers. This method can significantly reduce the time required for image analysis and enhance the accuracy and consistency of cardiac quantitative assessments in clinical settings.
Digitisation of electrocardiographic images enable artificial intelligence-based mortality prediction
Authors: Patlatzoglou K , Sau A , Zeidaabadi B , Pastika L , Peters N , Kramer D , Waks J , Ng F 
Published: October 2024

Abstract

Background
Artificial intelligence-based electrocardiogram (AI-ECG) models have shown excellent performance in a vast number of diagnostic and prognostic tasks. One of such tasks, which is critical for patient risk stratification and clinical intervention, is the prediction of adverse events, including death. Although a plethora of existing AI-ECG models have shown their accuracy in mortality prediction, their development and utilization currently rely on digital ECG signals for training and application.

Purpose
Many hospitals around the world have a significant number of ECGs stored in image formats or printed as paper ECGs. To address this problem, we developed and tested an ECG image digitisation pipeline to extract digital traces from ECG images and compare the performance of AI-ECG models for mortality prediction, under both natively digital and digitised signal formats.

Methods
A secondary care dataset of over 1 million ECGs (1,163,401 ECGs; 189,539 patients) was used for this analysis, in which both a natively digital signal and a PDF image was available. The ECG digitisation pipeline comprised of coloured PDF image inputs (2200 x 1700), initially processed to extract the ECG traces and grid features. ECG signals were extracted from the traces after cropping/restoration of the images and scaled appropriately based on the detected grid (2.5-sec strips per lead, 0.5 mV per grid block). Subsequently, both digital and digitised signals were identically pre-processed in order to acquire the ECG data fed into the AI model. In particular, a previously successful convolutional neural network architecture with a discrete-time survival loss function was used for mortality prediction. The model was trained under multiple configurations, using either the digital or the digitised ECGs as training/testing datasets (Figure 1). ECGs were split by patient ID to ensure cross-subject generalization with a training/validation/testing split of 50%/10%/40% ratio, respectively. Finally, the models were evaluated by the c-index.

Results
A comparison of the digital and digitised ECGs on the respective 2.5-sec strips available in both formats revealed a mean absolute error of 0.014 (CI: 0.07 – 0.021) and a Pearson’s R of 0.98 (CI: 0.96 – 1.00) between the signals, demonstrating an accurate ECG digitisation (Figure 2 shows overlapping natively digital (black) and digitised (red) traces). The performance of the digitisation was also evident in the performance of the AI-ECG mortality prediction task, with the model achieving c-indices of 0.775 (CI: 0.774 – 0.776) and 0.772 (CI: 0.771 – 0.774) when trained and tested on digital and digitised signals, respectively.

Conclusion
We described the first digitisation-based AI-ECG model for mortality prediction. Our results demonstrate the potential for clinical settings that lack the digital ECG infrastructure, to develop and deploy AI models using ECG image formats.Figure 1Figure 2
Newcard telemonitoring system improves survival in comparison both with traditional approaches and other French telemonitoring systems in chronic heart failure
Authors: Pathak A , Roubille F , Galinier M , Damy T , De Groote P , Mouquet F , Schmidt A , Perrard L , Perrard G 
Published: October 2024

Abstract

Background
in the NEWTS3 Study, the French National Health Data System (SNDS) was used to comprehensively collect medico economic data for patients between 2017 and 2020. We have shown a decrease in all-cause mortality with the Newcard telemonitoring system (NewTM) in comparison with traditional approaches in Chronic Heart Failure (CHF). This effect seemed to be greater in the group of patients included in the specific year 2020

Purpose
The aim of the NEWTS3EXT study is on one hand to confirm those results by adding 2021 and 2022 data to the follow up of the 2020 population. On the other hand, a new evaluation was performed for NewTM patients matched with patients followed by other telemonitoring French systems (TLM).

Methods
All NewTM patients monitored at least 3 months between 2017 and 2020 were included. For each patient, anonymised data was retrieved from the SNDS. Control Group (CG) was randomly selected from an exhaustive extraction of SNDS of patients with CHF without telemonitoring. This CG was matched in a 1:3 ratio to NewTM patients using a propensity score including baseline characteristics, presence of comorbidities and presence of significative treatments. TLM group was randomly selected from an extraction of SNDS of patients with CHF including a reimbursement of telemonitoring but not followed by Newcard. This TLM group was matched in a 1:1 ratio to NewTM patients using the same propensity score. Binomial negative GEE models were used on matched data sets to assess differences.

Results
53.97 % of the patients were included during the 2020 year (571 patients). During 2020, the COVID first year, the prognosis of patients with HF is known as more positive. Despite that, we show a consistent gain in survival rates for the NewTM group (OR 0.707 p=0.0025) (fig 1)
For the second arm of the study, 919 NewTM patients were matched to 919 TLM patients. We show also an increase statically significative in survival rates in the NewTM group versus TLM group at 48 months (OR 0.832 p=0.0394) (fig 2)

Conclusion
In this part of the NEWTS3 program, we confirm the effect on all-cause mortality of the Newcard telemonitoring system in comparison with a control group even on the specific 2020 population which is known to have a better prognostic. On the other hand, it is one of the first comparison between one specific solution and a group of others. Explaining the positive result on all-cause mortality for the NewTM must be a mix of plenty of specificities especially in a complex process like telemonitoring of HF. To help to find the best place for telemonitoring in the care pathway of HF, we can emphasize on the high level of adherence of the NewTM patients (data collected every 1.4 days) and on the precise process to ensure that every alert is followed by a medical response.
Left atrial strain parameters are able to predict presence of atrial fibrillation
Authors: Plasek J , Valerie Skopelidou V S , Jan Strakos J S , Diana Drienikova D D , Martin Rachela M R , Jiri Pudich J P , Jiri Vrtal J V , Miroslav Homza M H , Jan Vaclavik J V 
Published: October 2024

Abstract

Background
Three-dimensional speckle-tracking echocardiography allows evaluation of left atrial function, left atrial function has been assessed in various diseases for prognostication [1]. Atrial fibrillation (AF) is most common arrhythmia, and one of its manifestations is increased left atrial filling pressure and left atrial dilatation.

Methods
Echocardiography loops were collected between December 2019 and December 2021, only valid atrial loops, and sinus rhythm during examination. Spearman test was used for correlation matrices of left atrial parameters; uni- , multivariate and binary logistic regression to predict presence or absence of atrial fibrillation.

Results
A total of 116 patients (50% females) aged 76.9±11.2 were included in the analysis. Males and females differed in anthropometric and other clinical parameters. Intra- and interobserver variability was close to 1% since most of the measurements are semi-automatized. 2D and 3D left atrial volumes correlated significantly (r=0.8, P&lt;0.0001, Fig.1). Almost all left atrial strain measurement uni- and multivariately predicted the presence of atrial fibrillation, binary logistic regression identified two most significant parameters (left atrial longitudinal strain reservoir and conduit phase, LASr, LAScd, P=0.042, P=0.044, respectively, Fig.2). LASr is significantly greater in patients without history of AF vs patient with AF history, (16±16, 10.5±7.1, respectively, P=0.012). The probability of sinus rhythm increases linearly with the increasing value of LASr.

Conclusion
Left atrial longitudinal strain was able to differentiate patients with paroxysmal atrial fibrillation as compared to patients without history of this arrhythmia.