bluuebunny/crossref_metadata_2025 huggingface dataset:
Healthcare professionals' experiences of the usefulness and ease of using a digital medical translation tool in communicating with minority language-speaking hospitalized heart surgery patients
Authors: Lie I , Rokeness S , Skredsvig K , Johannsen N , Lindsjorn K E , Hasle C H , Nes L S
Published: October 2024

Background/Introduction

Healthcare professionals are required to deliver high-quality care while adhering to the human right to equity for all patients. Language barriers often result in miscommunication between healthcare professionals and patients, leading to decreased patient satisfaction, compromised patient safety (such as increased adverse events), and diminished healthcare quality. Studies focusing on implementing digital medical translation tools and measuring their usefulness during patients’ hospitalization are highly warranted.

Purpose

To survey and explore healthcare professionals' experiences of the usefulness and ease of using a digital medical translation tool in communicating with minority language-speaking heart surgery patients during hospitalization.

Methods

Participants completed an internationally published six-item, study-specific questionnaire on health care professionals' experiences regarding perceived usefulness and ease of use from October 2022 to February 2023. The first three items, namely: (1) 'The digital tool was easy to use,' (2) 'The languages were easy to understand,' and (3) 'The digital tool was useful,' were measured on a 5-point Likert scale ranging from 1 ('totally agree') to 5 ('totally disagree'). The last three items were open-ended: (4) 'What they liked best,' (5) 'What they liked the least,' and (6) 'Suggestions for improvement'.

Results

Health care professionals (i.e., registered nurses, intensive care nurses, operating room nurses, anaesthesia nurses and physiotherapists) (n=53) aged, mean 35 (SD, min-max 9.7, 25-58). They reported the digital medical translation tool was easy to use ("totally agree/agree" 15/33, 91%), easy to understand ("totally agree/agree" 30/22, 97%), and useful ("totally agree/agree" 24/25, 92%) as illustrated in figure 1. Illustrative citations from the open responses: What health care professionals liked best: "Enabling communication with minority language-speaking patients feels safe in terms of patient safety" and "The opportunity to form a clinical picture of the patient at the beginning of the shift gives me confidence. The possibility to meet the patient's psychosocial needs and the opportunity to more easily clarify situations and pains and discomforts." They liked the least that it "Took some time to find my way around between questions at the beginning of using the tool" and suggestions for improvement were cited as "Split up the phrases in the tool in several categories (icons) e.g., intubation phase" and "Add more phrases specific for our heart surgery patients".

Conclusions

Healthcare professionals found a digital medical translation tool to be useful and easy to use in meeting the needs of patients and improving patient safety during communication with minority-speaking heart surgery patients.
NICE guidance in the rapid access chest pain clinic: does it work?
Authors: Pottle A , Harrison S
Published: October 2024

Background

Rapid Access Chest Pain Clinics (RACPC) were developed following the publication of the National Service Framework for Coronary Heart Disease in 2000(1). The National Institute for Health and Care Excellence (NICE) produces guidelines for the management of patients with recent onset chest pain of suspected cardiac origin(2). In 2016 the guidance was updated and recommended the use of CT coronary angiography (CTCA) as the initial investigation for patients with chest pain which was typical or atypical of angina. Adherence to the guideline may be dependent upon factors such as local availability of CT scanners and clinician preference. The RACPC in this tertiary centre is nurse-run with no medical involvement. There is good engagement with the CT department and in July 2017 the NICE guidance was adopted in the clinic.

Purpose

To evaluate the use of CTCA as the first line investigation for patients with typical or atypical chest pain.

Methods

Data from all patients reviewed in the clinic between 01/07/2017 and 31/12/2023 were prospectively collected, entered into a database, and then retrospectively analysed. The data were organised into 3 groups: patients with typical chest pain; atypical pain and those with non-cardiac sounding chest pain. The number of patients in each group who underwent a CTCA, and the outcome of the scan were evaluated. The CT scans were categorised as showing significant coronary artery disease (CAD), non-obstructive CAD, no CAD, or as inconclusive. The outcome of patients who had a positive CTCA was also recorded.

Results

2986 patients were reviewed in the RACPC in the above period. 14.2% of patients presented with typical pain, 47.4% with atypical pain, and 30.6% had non-cardiac pain. The remaining patients presented with other symptoms such as breathlessness. CTCA was used as the first investigation in the majority of patients with typical or atypical pain, unless alternative investigations or management were deemed to be more clinically appropriate. In 59.5% of typical and 78.5% of atypical presentations, the CT scan showed no CAD, or non-significant CAD. There was a low incidence of positive scans in both groups, however patients with typical pain were more likely to have significant CAD (Table).

All patients who had a CTCA indicating significant CAD, were referred for invasive angiography. 88.0% of typical presentations, and 64.0% of those with atypical pain, underwent revascularisation. No patient who underwent invasive angiography was found to have normal coronary arteries.

Conclusion

Atypical pain is the most frequent presentation in the RACPC, however the majority of these patients, and most of those with typical pain, do not have significant CAD. Careful assessment and the use of CTCA as the initial investigation, enable the identification of those patients who need invasive angiography. This is a cost-effective strategy which also reduces risk and radiation exposure for patients.
A nurse-led intervention improves quality of life and reduces arrhythmic recurrences in patients undergoing atrial fibrillation ablation. Results of the NURSECAT-AF randomized clinical trial
Authors: Cano Valls A , Mont L L , Carro E , Venturas M , Lajara M A , Martinez-Monblan M A , Guasch E
Published: October 2024

Background/Introduction

Atrial fibrillation (AF) is a prevalent condition that can lead to a decrease in quality of life and frequent re-hospitalizations. Some data suggest that integrated care nurse-led may blunt these consequences in unselected patients with AF, but not all studies support this. While AF ablation is becoming a cornerstone treatment for symptomatic patients with AF, it is unclear what impact a nurse-led intervention would have on these, quite likely, more receptive patients.

Purpose

To assess the impact of nurse-led intervention on quality of life among AF patients undergoing ablation.

Methods

We conducted a single-centre prospective randomised clinical trial to test the effect of an expert nurse-based intervention consisting of an educational programme on AF, support for the patient undergoing cardiac ablation to ensure its success, and management of risk factors associated with this arrhythmia. The control group underwent standard medical follow-up. Patient selection was based on chronological order. Only patients who underwent their first ablation and did not have heart failure were included. The primary endpoint was quality of life at 12 months post-ablation using the Arrhythmia-Specific Scale in Tachycardia and Arrhythmia (ASTA) questionnaire (higher values denote impaired quality of life). Secondary endpoints were AF knowledge and satisfaction at 15 days post-ablation using validated instruments, symptoms using the ASTA questionnaire as well, and readmissions rates, emergency room visits, and AF recurrence rates over a one-year period.

Results

116 patients were screened (50 were excluded mostly because were re do). A total of 66 patients were eventually included and randomized (33 per arm; mean age [mean±SD] 63±10 years; 44 men [67%]). At 12 months, quality of life was significantly better in the intervention group than in the control group (4.5 ± 6.1 vs 0.57 ± 2.3, respectively, p=0.001)(Figure). Furthermore, satisfaction levels (17 ± 4.1 vs 12.7 ± 5.4, p>0.001) and disease knowledge (20.5 ± 5.1 vs 15.9 ± 4.5, p<0.001) were higher in the intervention group. Patients in the control group experienced a higher symptom burden during the first year (0.6 ± 1.5 vs 3.8 ± 4.5, p<0.001. Relevant clinical endpoints such as readmissions (3% vs 18.2%, p=0.046), emergency room visits (21.2% vs 57.5%, p=0.021), and arrhythmia recurrences (15.2% vs 45.5%, p=0.02) were all significantly lower in the intervention group.

Conclusion(s)

Nurse-led integrated care for patients undergoing AF ablation yields substantial improvements in quality of life and symptomatic relief at one year post-procedure. Additionally, it enhances patient knowledge of AF and satisfaction levels, and reduces readmissions, emergency room visits, and arrhythmia recurrences.
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

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 < 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 < 0.01) and in inverse probability of treatment weighting-adjusted logistic regression analyses (adjusted OR, 0.82; 95% CI, 0.77–0.88; p < 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

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

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

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

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

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

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

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

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<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.
Smartphone application for improving self-awareness of adherence to edoxaban treatment: a randomised controlled trial (ADHERE-APP)
Authors: Yoon M J , Kim I C , Lee J H , Kim M , Kim H L , Lee S , Kim I J , Choi S , Park S J , Hussain M , Lee S , Choi D J
Published: October 2024

Background

Adherence to oral anticoagulants is essential for stroke prevention in patients with atrial fibrillation (AF). This study aims to test the feasibility and effectiveness of the smartphone app-based intervention to improve drug adherence in patients with AF.

Methods

This prospective, randomised, open-label, multicentre trial (ADHERE-APP) enrolled a total of 502 patients at 9 university hospitals in South Korea from November 2021 to January 2023. Patients with AF aged ≥19 years requiring oral anticoagulation,and were treated with edoxaban were enrolled. Patients were randomized in a 1:1 ratio to the conventional treatment group and the app conditional feedback group. Patients in the app conditional feedback group used a smartphone application that pushed alarms for taking medication and measuring blood pressure (BP) and heart rate (HR) at specific times of the day. In addition to medication alarms, the habit of measuring one’s BP and HR could improve drug adherence by increasing self-awareness of the disease. The automatic BP machine was connected to the smartphone via Bluetooth and the measured BP and HR were automatically updated on the smartphone app. The primary endpoint was adherence to edoxaban as measured by pill count at 12 and 24 weeks. In addition, we evaluated the differential effect of the smartphone application by age on the primary endpoint.

Results

Among the 502 patients, the mean age was 66.0±10.0 years and 67.7% were male. Of the 372 patients who completed 12-weeks trial, edoxaban adherence at 12 weeks was high in both arms, with no difference in the primary outcome (96.5% [92.5%-100.0%] vs. 98.0% [93.2%-99.5%], P = 0.488). or in secondary outcomes, including the proportion of patients achieving adherence ≥ 80% or 95%. In patients aged >65 years, adherence to edoxaban was higher in the app conditional feedback group than in the conventional treatment group (95.8% [93.0%-99.5%] vs. 98.0% [95.8%-99.5%], P = 0.049).

Conclusions

Overall adherence to edoxaban was much higher than expected in patients with AF. There was no difference in edoxaban adherence between the app conditional feedback group and the conventional treatment group, however, in older patients, app feedback increased medication adherence.
Effect of a cardiovascular post discharge sms support program on healthcare utilisation - the hearthealth program
Authors: Sheahen B , Marschner S , Minn H , Thiagalingam A , Sivagangabalan G , Kovoor P , Chow C
Published: October 2024

Background

Cardiovascular disease (CVD) is a leading cause of mortality worldwide[1]. Targeting modifiable risk factors is key to primary and secondary prevention of CVD but many barriers to good risk factor control exist[2]. SMS message programs have shown to be an efficient modality to deliver information to improve knowledge and risk factor profiles for CVD patients[3]; however, their impact on healthcare utilisation has not been thoroughly examined. The HeartHealth program is a text message post-discharge support program delivered to CVD patients. It comprised regular semi-personalised text messages providing CVD information, advice, and support to patients over a 6-month period.

Purpose

This study aimed to examine the impact of the Heart Health program on healthcare service utilisation.

Methods

Patients ≥18 years old who recently attended cardiology clinics or were discharged from a cardiology unit, between April 2020 and April 2022 were invited to the program. We compared healthcare utilisation rates in patients who enrolled into the HeartHealth text program (intervention group) to contemporaneous patients who did not enrol in the program (control group). The primary outcome was the incidence rate of all-cause healthcare utilisation events (composite of ED presentations and/or hospital admissions for any medical cause) in the 6 months post-discharge. The secondary outcomes were incidence rates of CVD events and unplanned CVD events. All analyses were adjusted for demographic and co-morbidity variables.

Results

11542 patients were included in analysis (intervention: 4324, control: 7218), mean age 58.8 years, 60.7% were male, and 78.3% with English as their preferred language. HeartHealth Intervention participants had lower rates of the primary outcome of healthcare service events compared to control participants (mean number of events 0.43 intervention vs 0.52 control, adjusted rate ratio [RR], 0.91 [95% CI, 0.83-0.99], p=0.033, Table 1). The intervention was significantly more effective in older patients compared to younger patients (RR, 0.82, [95% CI, 0.72-0.93] in patients aged ≥65 years, compared to patients <65 years, RR, 0.98, [95% CI, 0.87-1.10]; p-value for interaction =0.043, Figure 1). The subset of CVD events and unplanned CVD events were numerically lowering in the intervention group compared to the control, but the risk ratio not statistically significantly different in adjusted models (Table 1).

Conclusion

This analysis demonstrates that a text message post-discharge support program for CVD patients decreased the need for healthcare service utilisation for all medical causes. Future randomised controlled trials are required to further support these findings.Figure 1:Adjusted interaction analysisTable 1:Adjusted Incidence of events
Geospatial clustering of auto-antibodies against apolipoprotein A-1, heavy metals, cardiovascular and liver diseases in the general population
Authors: Vuilleumier N , Joost S , Fellay N , Pagano S , Marques-Vidal P , Vollenweider P , Stringhini S , De Ridder D , Vaucher J , Perrais M , Thomas A , Somm E , Jornayvaz F , Guessous I
Published: October 2024

Aims

Heavy metals (HM) are combustion-related environmental factors predisposing to cardiovascular (CVD), autoimmune and liver diseases, even below regulatory concentrations. Pro-atherogenic and pro-steatotic auto-antibodies against apolipoprotein A1 (AAA1) are prevalent in the general population for unclear reasons. We explored a putative interplay between HM exposure, AAA1, CVD risk factors and a non-alcoholic fatty liver disease (NAFLD) risk score, using a geospatial clustering approach at the general population level.

Methods

Getis-Ord, Moran I2 clustering and geographically weighted regression (GWR) geosptatial analyses were carried out on 6361 individuals recruited in the CoLaus/PsyCoLaus general population cohort. Geospatial dependences between serum AAA1, urinary HM (cadmium, arsenic, cobalt) concentrations, SCORE2 CVD risk, and fatty liver index (FLI) were assessed.

Results

Cadmium and cobalt were found to be associated with higher SCORE2 risk (p<0.04). AAA1 were associated with higher cadmium, cobalt, and cigarette consumption, independently of SCORE2 (p<0.04). AAA1 hotspots overlapped significantly with those of cadmium and FLI, independently of SCORE2. According to GWR, correlations between cadmium and AAA1 were the highest in railways proximity regions, increasing up to 10-fold compared to global associations. AAA1 were not associated with other HM.

Conclusions

These geospatial footprints highlight independent associations between Cadmium, AAA1, and NAFLD risk score in the general population. We hypothesize that environmental cadmium exposure may facilitate the occurrence of AAA1 in humans. Ongoing experimental studies will confirm/refute a possible causal link. Whether AAA1 could represent an early biological signature of cadmium exposure and subsequent health hazards remains to be shown.
The association between the dietary salt intake and glucose intolerance in the non-diabetic populations: from the Korean National Health and Nutritional Examination Survey (KNHANES)
Authors: Rha S , Choi B G , Choi S Y , Cha J , Hyun S , Kang J , Chu W , Park S , Choi C U , Park C G
Published: October 2024

Background

Diabetes is known to be a strong risk factor for cardiovascular disease (CVD), and controlling salt intake in patients with type 2 diabetes may be a strategy to reduce the risk of CVD. However, the clear interaction between salt intake and glucose homeostasis has yet to be investigated. Our study evaluated the frequency and risk of glucose intolerance (pre-diabetes) according to dietary salt intake in the non-diabetic population in Korea.

Methods

The Korean National Health and Nutritional Examination Survey (KNHANES) database was utilized for this study (https://knhanes.cdc.go.kr). A total of 72,751 subjects were enrolled in the KNHANES database from 2010 to 2018. The subjects were categorized into the following: normal fasting glucose (i.e., no pre-diabetes; FPG < 100 mg/dL), stage 1 pre-diabetes (FPG 100–109 mg/dL), stage 2 pre-diabetes (FPG 110–125 mg/dL). All subjects were divided into 8-equal groups according to their sodium intake: Group 1: ≤ 3.5 g, Group 2: 3.5 to 5.0 g, Group 3: 5.0 to 6.3 g, Group 4: 6.3 to 7.8 g, Group 5: 7.8 to 9.5 g, Group 6: 9.5 to 11.8 g, Group 7: 11.8 to 15.5 g, Group 8: > 15.5 g. This study is a retrospective cross-sectional study, so future risks cannot be assessed, but only the disease status assessment at the same time.

Results

Of the total 57,165 subjects without diabetes, 29.5% (16,874 subjects) were pre-diabetic, of which 22.0% (12,577 patients) were stage I pre-diabetic, and 7.5% (4,297 subjects) were stage II pre-diabetic. From the lowest salt intake group (group 1, salt intake <3.5g) to the highest salt intake group (group 8, salt intake>.5g), the pre-diabetes prevalence continuously increased from 22.9% to 33.3%. The range of stage 1 pre-diabetes was 16.4% to 26.4%, and the range of stage 2 diabetes was 6.5% to 8.7%. The odds ratio (OR) of pre-diabetic risk according to salt intake in the non-diabetic population, compared with group 1, group 2 was 1.16 (95% confidence interval [CI]: 1.07-1.25), group 3 was 1.32 (95% CI: 1.23 -1.43), group 4 was 1.46 (95% CI: 1.35-1.57), group 5 was 1.51 (95% CI: 1.40-1.63), group 6 was 1.57 (95% CI: 1.46-1.69), group 7 was 1.65 (95% CI: 1.53-1.78), group 8 was 1.68 (95% CI: 1.56-1.81).

Conclusions

High salt intake is significantly associated with pre-diabetes prevalence in the non-diabetic Korean population.
Rheumatic heart disease: an ongoing public health concern in vulnerable communities
Authors: Rajaratnam D , Boddupalli S , Sacca R , Slattery A , Hall J , Rajaratnam R
Published: October 2024

Background

Acute rheumatic fever (ARF) results from an autoimmune response triggered by group A streptococcus (GAS) infection (1). Severe or recurrent ARF episodes can cause permanent heart valve damage, leading to complications including heart failure, stroke, arrhythmias, carditis, and decompensation during pregnancy (2).

Purpose

Rheumatic fever (RF) remains a public health concern in Australia, particularly among Indigenous, migrant populations and socioeconomically disadvantaged communities. Europe and various regions worldwide have experienced significant migration from areas with a high prevalence of RHD (3).

Amongst the more than 1 million asylum seekers who arrived in Europe in 2015, an estimated 1% could potentially be affected by RHD (3). This migration influx underscores the pressing need for the implementation of strategies in health policy and practice. We aimed to outline the attributes of our RF-RHD cohort to aid in the formulation of a Model of Care (MoC).

Methods

Cases were identified on a quarterly basis either through the NSW Public Health Rapid, Emergency, Disease, and Syndromic Surveillance (PHREDSS) system or via clinical diagnoses reported to the Public Health Unit (PHU) from January 2015 to June 2023. Demographic profiles, clinical particulars, and patient outcomes were collected from patients in the South Western Sydney Local Health District (SWSLHD), NSW, Australia.

Results

A total of 45 cases were reported (Table 1). This consisted predominantly of females with an average age at diagnosis of 14 years. A majority were obese (69%).

Most were in the Maori and Pacific Islander populations (62%), while a smaller portion of patients were of Aboriginal or Torres Strait Islander descent (9%) (Australian Indigenous community). Notably, 84% of patients diagnosed with ARF subsequently progressed to RHD.

Among the reported cases, 16 patients underwent valvular surgery, 2 patients progressed to end-stage heart failure, and there was 1 mortality. 39% (11) of female patients were pregnant during the study period, with 6 (55%) of these patients decompensating during their pregnancy (Table 2).

Conclusion

Rheumatic fever persists as a public health concern on a global scale. Our findings emphasize the need for a high index of clinical awareness in considering RF/RHD in socioeconomically disadvantaged communities. In our setting, there is a disproportionate burden of disease among the Pacific Islander population. The high occurrence of cardiac decompensation during pregnancy, underscores the need for close monitoring in this demographic.

Despite the prevalence of rheumatic fever predominantly affecting young patients in vulnerable groups, we found a significant proportion of individuals lost to follow-up or not connected with Cardiology services. Addressing rheumatic fever requires a comprehensive approach that encompasses improved healthcare accessibility, targeted health education initiatives, and socioeconomic interventions.