bluuebunny/crossref_metadata_2025_split huggingface dataset:
An artificial intelligence-enabled electrocardiogram model outperforms the revised cardiac risk index score for predicting major adverse cardiovascular events in patients undergoing noncardiac surgery
Authors: Sheffeh M , Estarda-Magana A , Medina-Inojosa J , Mangold K , Siontis K , Sher-Lu P , Jacob A , Chini E , Ortega Aviles L , Liu K , Noseworthy N , Attia Z , Lopez-Jiminez F 
Published: October 2024

Abstract

Background
Existing clinical preoperative risk assessment tools such as the Revised Cardiac Risk Index (RCRI) have been used extensively to predict perioperative major adverse cardiovascular events (MACE). Electrocardiogram (ECG) analysis using Deep Learning can identify hidden features that human eyes cannot identify and might help further risk stratify patients undergoing noncardiac surgery.

Objective
We aimed to develop a Deep Learning model that predicts perioperative MACE in patients undergoing elective noncardiac surgeries.

Methods
We included patients ≥ 18-year-old who underwent elective non-cardiac surgery between 2000-2021. MACE was validated in duplicate and were defined as a composite of in-hospital myocardial infarction, cardiac arrest or mortality. A convolutional neural network was developed using ECGs within 30 days before surgery. We randomly split subjects into training, internal validation, and testing datasets in a 7:1:2 ratio. Performance was evaluated using the area under the receiver operator characteristics curve (AUC) values on the testing dataset. Models trained included AI-ECG models to predict in-hospital MACE and to predict in-hospital mortality, and AI-ECG+RCRI models to predict in-hospital MACE and to predict in-hospital mortality. All models were validated using 30-day validated outcomes (MACE or all-cause mortality) in a community-based cohort and were compared with the Revised Cardiac Risk Index (RCRI) score using AUC values.
Findings: We included 195,214 patients who underwent a total of 241,999 surgeries. 169,214 ECGs were used in the training dataset, 24,234 in the internal validation dataset, and 48,551 in the testing dataset. In the test cohort, the AI-ECG algorithm discriminated risk for in-hospital MACE with an AUC of 0.82 (95% CI 0.79-0.85). The algorithm similarly discriminated 30-day MACE with an AUC value of 0.79 (95% CI 0.77-0.81) surpassing the discrimination of the RCRI score AUC of 0.69 (95% CI 0.66-0.72). All models’ performances are shown in Figure1A. The AI-ECG model to predict in-hospital mortality had an AUC of 0.83 (95% CI 0.80-0.86) and predicted 30-day mortality with an AUC of 0.80 (95% CI 0.77-0.82). Other models’ performances are shown Figure1B. Training the AI-ECG models including RCRI data did not improve the AI-ECG models’ performance.

Conclusion
AI-ECG models with no additional clinical data can improve perioperative risk prediction in non-cardiac surgery, outperforming a conventional and widely used risk stratification tool such as the RCRI.
The added value of device measured physical activity to the prediction of incident cardiovascular disease.
Authors: Sturge A , Harper C , Chan S , Van Duijvenboden S , Casadei B , Doherty A 
Published: October 2024

Abstract

Background
Conventional risk prediction models for Major Adverse Cardio/Cerebrovascular Events (MACCE) primarily rely on non-modifiable factors such as age, sex, and physiological measurements, including LDL/HDL cholesterol and blood pressure readings, that are prone to variations across medical practices and are often unavailable in many parts of the world. In this study, we investigate the potential of inexpensive wearable measurements of physical activity to enhance MACCE prediction when physiological measurements are unavailable.

Purpose
To assess the added value of physical activity measurements, collected via wrist-worn accelerometers, to established cardiovascular risk prediction models. Additionally, we seek to investigate whether physical activity measurements can substitute for common physiological measurements in established cardiovascular risk models.

Methods
We obtained accelerometer-measured physical activity over 7 days between 2013 and 2015 from 69,898 UK Biobank (UKB) participants without a prior history of MACCE, defined as death or hospitalisation due to (1) myocardial infarction, (2) heart failure, or (3) Stroke/transient ischaemic attack. We assessed the added value of daily step counts, sleep duration, and a deep learning-derived activity health score (Fig 1), calculated from complete 7-day accelerometer recordings, to established clinical risk models (SCORE2, QRISK3 and AHA) before and after excluding measurements of cholesterol and systolic blood pressure (SBP). The primary outcome was the first recorded MACCE within 6 years. Risk scores were developed in men and women using Cox proportional hazards models. We assessed predictive performance using Harrel’s C-index, net reclassification index (NRI), and net benefit at the recommended treatment threshold for each model.

Results
Of the 69,898 UKB participants in the study, 3,386 (4.8%) experienced a MACCE over a 6-year follow-up period. In place of HDL ratio and SBP, the addition of deep-learned activity health scores modestly improved performance in the best clinical baseline, QRISK3 for Female participants ΔC-index women:0.003 (95% CI 0.002-0.004); Δnet benefit 0.02(0.01-0.03); NRI 0.04, (0.02-0.07), and outperformed manually extracted steps and sleep measurements. We report no significant improvements for men. We find a greater increase in model performance when both HDL ratio and SBP, plus activity health scores, are included in the model. ΔC-index women:0.008, (0.007-0.009); men: 0.003 (0.002-0.004) (Fig 2).

Conclusion
Our findings indicate that in the absence of cholesterol and systolic blood pressure, the addition of device-measured physical activity modestly improves the performance of clinical risk scores among individuals without prior cardiovascular disease (CVD). These findings could further refine intervention strategies for targeted prevention of CVD, particularly in settings where physiological measurements may be unavailable.
Biventricular modelling for investigating ventricular arrhythmias in silico
Authors: Cheng F , Rauseo E , Misghina S , Barrera C , Vigmond E , Slabaugh G , Aung N , Petersen S , Roney C 
Published: October 2024

Abstract

Background
Ventricular arrhythmias have a significant incidence rate worldwide and may potentially lead to sudden cardiac death in severe cases. Despite therapeutic advancements, the intricate mechanisms dictating these arrhythmias remain elusive. Biventricular modelling and deep learning provide a way to investigate the complex electrophysiological substrates underlying cardiac arrhythmogenesis.

Purpose
This study aims to develop and validate a patient-specific biventricular computational modelling pipeline for simulating ventricular arrhythmias in silico. We provide a comprehensive platform for understanding the initiation, propagation, and treatment response of various ventricular arrhythmias.

Methods
To facilitate large-scale computational experiments and personalised model predictions within clinical timeframes, it is imperative to establish a rapid and reproducible modelling framework. First, we constructed biventricular volumetric models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) data from the Cardiac Atlas Project (2018 Atria Segmentation Challenge), and then we calculate biventricular coordinates to facilitate data registration between measures, and fibre and fibrosis inclusion. We also apply the pipeline to cardiac magnetic resonance (CMR) data and electrocardiogram recordings from UK Biobank.

Results
We have successfully developed a pipeline for constructing biventricular volumetric models and efficiently processed biventricular coordinates (Figure). By systematically exploring interactions between ventricular regions, the model may identify critical factors contributing to arrhythmia susceptibility and guide personalised therapeutic strategies. Furthermore, the model may provide insights into the efficacy of antiarrhythmic interventions, ablation techniques, and device therapies, facilitating the development of precision medicine approaches.

Conclusions
We demonstrate a pipeline for constructing patient-specific biventricular computational models at scale from imaging data and apply this to LGE-MRI data and UK Biobank CMR data. These biventricular models enable investigation of the complex mechanisms of ventricular arrhythmias, providing novel insights for clinical decision-making and treatment strategies.
Initial experience with a 5G cellular network and robotic arm-based remote echocardiographic system in an all-comers setting
Authors: Liu Y , Meng F , Zhou N , Wu Y , Hu C , Fang X , Chen H , Pan C , Shu X , Ge J 
Published: October 2024

Abstract

Background
Echocardiography is the test-of-choice for the initial evaluation of many cardiac diseases and requires expertise that is often limited in rural area. Robotic arm-assisted remote echocardigram has been attampted for teleconsultation but was limited to heart failure patients(ref), primarily due to the network delay in telecommunication and the subsequent incoordination in robotic arm control.

Purpose
We aim to assess the feasibility and diagnostic accuracy of a 5G cellular network and robotic arm-based remote echocardiographic system in an all-comers setting.

Methods
51 pts were enrolled from the outpatient cardiology clinic of our institute. All pts underwent standard comprehensive echocardiography on a 5G cellular network and robotic arm-based remote echocardiographic system (the robotic part was deployed at our hospital and the control part was located at another campus 20 kilometers away) (Figure 1)and a conventional echocardiographic platform (at our hospital) successively. The order in which patients were examined on the remote and conventional instruments was randomly determined. There was no interval between the two examinations, and examinations of the same patient were performed by experienced but different cardiologists, who were blinded to each other’s diagnosis. The doctor who used the remote system was also randomly allocated and had not been previously specifically trained. The exams were real-time and diagnoses were made right after the exams.

Results
Of the 51 pts, the image quality was sufficient for diagnosis in 50 patients (48% female, 40±18 years). 1 pt was excluded because some key views could not be obtained using the remote system (98% of exams successful). 17 pts had positive findings, including 10 with a primary diagnosis of valvulopathy (1 Barlow's syndrome, 1 bicuspid aortic valve and 8 less-than-moderate regurgitation), 2 cardiac surgery follow-ups (1 case of aortic valve replacement and septal myectomy, and 1 case of mitral valve replacement and tricuspid annuloplasty), and 2 hypertrophic cardiomyopathy (including 1 case of obstruction at papillary muscle level), 2 with abnormal left ventricular wall motion(including 1 case of apical mural thrombus) (Figure 2), and 1 with congenital heart disease (secumdum atrial septal defect). The main diagnosis was consistent in 98% of cases (papillary muscle level obstruction was missed in one case), using conventional echocardiographic findings as the standard. Time for image acquision using remote echocardiography was significantly longer than conventional one (24 minutes 36 seconds ± 4 minutes 52 seconds vs. 16 minutes 15 seconds ± 1 minute 54 seconds, p&lt;0.0001, paired t-test).

Conclusions
Comprehensive echocardiographic exam with a 5G cellular network and robotic arm-based remote system is feasible with relatively good diagnostic accuracy.Figure 1Figure 2
The cost-effectiveness of a digitally enabled telerehabilitation program delivered nationally to patients in Australia
Authors: Braver J , Salim A , Oldenburg B , Carrington M J , Marwick T 
Published: October 2024

Abstract

Introduction
Traditionally delivered cardiac rehabilitation (CR) programs have considerable benefits, yet access and participation remains low. Emerging digital health models offer promise in healthcare delivery, but their benefits and impact on healthcare utilization and cost-effectiveness are uncertain. We conducted a cost-effectiveness analysis of a telehealth program with a CR mobile app for post-hospitalization coronary heart disease (CHD) patients.

Purpose
To assess whether a digitally enabled CR program is cost-effective compared with a propensity matched usual care control group in the Australian health system.

Methods
Patients were recruited nationally, through a health insurer, following hospitalization for CHD. Participants received weekly telehealth consultations for up to 8 weeks, supplemented by use of an mHealth app. Using administrative private health insurance claims data, we assessed differences in hospital costs, mortality rates and bed days at 12-months post discharge. Incremental cost effectiveness ratios (ICERS) were calculated to determine the cost per deaths prevented and cost per bed days saved for the digital-CR group (n=133) compared with usual care (n=266).

Results
Among the cohort, 36% lived in regional/rural areas, 70% were male and the mean age was 68 years. The mean total hospital all cause readmission costs at 12-months were $1,488 AUD less in the digital-CR group ($16,411) compared to the usual care group ($17,899). Digital-CR had fewer total hospital days relative to the usual care group (median of 2 vs 3 days) and patients in the digital-CR group had a significantly lower mortality rate at 12-months post discharge. The ICERs were -$337 per 100 patient deaths prevented and -$3,033 per median bed days saved.

Conclusion
The implementation of a digital-CR program demonstrates cost-effectiveness for patients with CHD when compared to usual care. Thus, advocating for the widespread availability of digital-CR programs as an option for CHD patients following hospital discharge is warranted.
Digital literacy of patients with chronic heart failure using telemedicine in france: a national observational cohort study
Authors: Girerd N , Jourdain P , Hanon O , Jagu A , Seronde M F , Tartiere J M , Amara W , Benchimol H , Albert F , Abdenhader M , Nisse-Durgeat S , Pages N , Picard F 
Published: October 2024

Abstract

Introduction
Remote patient monitoring (RPM) is increasingly considered for chronic care management. However, several challenges exist for its implementation such as funding resources, lack of standardizations and gaps in digital literacy.

Objective
The aim of this study was to describe patients using RPM to manage chronic heart failure (CHF) according to their digital literacy at a national scale in France.

Methods
A national retrospective study was conducted using a remote monitoring system for the management and therapeutic guidance of patients with CHF. Patients were categorized according to their digital literacy (digitally literate vs poor digital literacy). The latter were identified as requiring nurse assistance in medical data entry. Data collection included baseline characteristics, comorbidities, RPM status, heart failure-related hospitalizations and mortality.

Results
Of the 12,072 patients with CHF enrolled for RPM follow-ups (male: 65.4%, mean age: 74.2±13.1 years), 43.3% had poor digital literacy. These patients were older (79.4±10.2 vs 70.2±13.6 years) and were less among males (39.7% vs 60.3%). Overall, digitally literate patients had more comorbidities, including diabetes (61.8%) and obesity (69.4%). Over a third of all patients withdrew from RPM (37.0%). Continued use of RPM was higher in digitally literate patients (60.1% vs 39.9%, p&lt;0.001). Mortality was higher in patients with poor digital literacy (OR: 0.71, 95%CI: 0.66 to 0.78, p&lt;0.0001). Poor digital literacy was linked to increased occurrences of HF episodes in contrast to digitally literate patients (34.1% vs 25.8%) who had an uHR of 0.66 (95%CI: 0.62 to 0.71, p&lt;0.0001), and an aHR of 0.68 (95%CI: 0.64 to 0.73, p&lt;0.0001).

Conclusion
Patients with poor digital literacy form a sizable subgroup in heart failure management and may require specialized tools tailored to their needs. Prevalence of poor digital literacy may increase the risk of HF hospitalization and mortality and reduce RPM adherence.
Effect of remote monitoring on the management of heart failure patients with cardiac amyloidosis
Authors: Mouhat B , Seronde M F , Jagu A , Esser R , Becle C , Nisse-Durgeat S , Pages N , Picard F 
Published: October 2024

Abstract

Objective
The primary objective was to assess the characteristics of heart failure (HF) patients with cardiac amyloidosis (CA) monitored remotely with our program. The secondary objective was to evaluate the discrimination of alerts by remote monitoring for HF in this specific population.

Methods
The entire multicentre cohort of HF patients monitored remotely with our program from August 2018 to December 2023 were included in the analysis.

Results
A total of 13,933 patients, including 346 (2.5%) with CA and 13,587 (97.5%) without cardiac amyloidosis (no-CA), had a median follow-up of 14 months. CA patients were more often male (78.6% vs 63.8%), over 80 years of age (69.9% vs 40.4%), with left ventricular ejection fraction &gt;40% (78.6% vs 56.5%), more often NYHA class III-IV (26.6% vs 16.2%), more atrial fibrillation (54.0% vs 40.9%) but less myocardial infarction (8.4% vs 27.9%), diabetes (16.5% vs 28.1%), smoking (2.9% vs 13.4%), p&lt;0.001 for all. There was no difference in the rate of renal failure (21.7% vs. 20.3%), obstructive sleep apnea (15.9% vs. 14.9%) or stroke (10.7% vs. 9.4%). Remote monitoring identified a total of 57,624 alerts, including 1240 in the CA group vs 56,384 in the no-CA group (i.e. 3.58 alerts per CA patient vs 4.15 alerts per no-CA patient). Of the alert resolutions in the CA group vs no-CA group, 22.4% vs 38.9% had a low probability of cardiac decompensation, 34.0% vs 35.4% had a moderate probability, 43.6% vs 25.7% had a high probability of cardiac decompensation (global Chi-square p&lt;0.001).

Conclusion
Despite an elderly population with different comorbidities, remote monitoring with this program in CA patients identifies correct relevance for discrimination of cardiac decompensation.
Measurement inaccuracy reported via the Manufacturer and User Facility Device Experience database for an implantable pulmonary artery pressure sensor: recalibration direction and magnitude results
Authors: Wetterling F , Kittipibul V , Fudim M 
Published: October 2024

Abstract

Introduction
Remote monitoring using an implantable pulmonary artery pressure (PAP) sensor (CardioMEMS HF system) in patients with heart failure (HF) has been consistently shown to reduce HF hospitalizations [1]. The success of this approach relies on the accurate measurement of diastolic PAP (dPAP) over time to allow early interventions such as diuretic intensification [2]. However, it remains unclear how the measurement accuracy changes over time after the initial calibration at the time of device implantation.

Purpose
This study aims to identify measurement inaccuracy events and to quantify the magnitude and direction of required device recalibration for CardioMEMS devices as reported in a large-scale regulatory device database.

Methods
We used the publicly accessible Manufacturer and User Facility Device Experience (MAUDE) data maintained by the US Food and Drug Administration (FDA). The summary search was performed for the CardioMEMS pulmonary artery pressure sensor for the entire product life cycle from January 2009 to February 2024 [3,4]. The entries related to the measurement accuracy under ‘Device Problems’ categories were obtained. Tested accuracy was reported to have been within ±10mmHg of a reference pressure measurement throughout simulated use for 10 years [5]. Normal range for dPAP is ~8mmHg to 15mmHg [6].

Results
A total of 2656 entries were obtained. Following categories were identified for the reported entries: device sensing problem (N=999, 38%); incorrect measurement (N=1349, 52%); incorrect measurement, inappropriate waveform (N=279, 11%), and incorrect, inadequate, or imprecise results or readings (N=29, 1%). After excluding 56 entries for missing information, 2600 entries were included for the analysis. A total of 1676 (64%) entries reported the need for recalibration of the CardioMEMS device. A mean PAP difference (defined as the difference of the mean PAP reference value measured at the time of recalibration minus the CardioMEMS reading obtained during the reference measurement) is shown in Figure 1. Compared to the reference pressure values at the time of recalibration, 488 (29%) of CardioMEMS readings were higher and 1188 (71%) were lower. The absolute recalibration pressure difference was 14.5 ± 9.3 mmHg (mean ± standard deviation, range 0.02-59.3 mmHg). The recalibration by more than 10 mmHg was reported in 1040 (40%) entries.

Conclusions
Suspected measurement inaccuracy with CardioMEMS device is not uncommon and often necessitate recalibration. Almost one-half (40%) of the recalibrations required more than 10 mmHg correction. These results should inform clinicians to consider recalibration along the course of CardioMEMS monitoring if there is discordance between clinical assessment and the values from the device.Figure 1.Figure 2
Quality of life in atrial fibrillation patients and its relationship to family member anxiety and depression
Authors: Tao L , Zhang X H , Song H Y , Li S H , Zhang Z H 
Published: October 2024

Abstract

Background
Atrial fibrillation (AF) has been found to decrease quality of life (QOL), and a few studies suggest that family members of AF patients can also experience negative emotions. This study aims to assess the anxiety and depression of the immediate family members of AF patients and to investigate the influence of the QOL of the AF patients on this anxiety and depression.

Method
This cross-sectional study recruited 121 pairs of AF patients and their immediate relatives along with 121 controls who and whose immediate relatives did not have AF. The patients filled out the Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) questionnaire, and the relatives and the controls were required to complete the State-Trait Anxiety Inventory (ST-AI) and Self-Rating Depression Scale (SDS).

Results
The score (mean (SD)) of the State Anxiety Inventory (S-AI, a subscale of ST-AI) in the relatives’ group was 44.9 (10.6), which was significantly higher than that of the control group (38.5 (9.5), p &lt; 0.001). Among the different kinship subgroups, the children of the AF patients had the highest state anxiety score (mean value = 47.0, p=0.075). A negative linear relationship between the patients' overall AFEQT score and the relatives' S-AI score was found (standardized regression coefficient = -0.244, p &lt; 0.001). For those patients who had particularly low AFEQT scores of less than 40, their relatives’ S-AI scores were significantly higher than other members of the relatives’ group (52.1 vs. 42.9, p = 0.001). AF patients in the peri-ablation period (81, 66.9%) had no difference in their AFEQT scores compared to other patients (60.8 vs. 64.4, p= 0.316) and there was also no difference in their family members’ ST-AI or SDS scores.

Conclusion
Study members showed considerable state anxiety if their family members had AF and increases in this anxiety were positively correlated with decreases in the HRQOL of the AF patients.