bluuebunny/crossref_metadata_2025 huggingface dataset:
Patient engagement in pulmonary embolism research: Insights form the Attend-PE study
Authors: Lindegaard S , Rolving N , Hojen A
Published: October 2024

Background

There is growing recognition of the significance of a close collaboration between patients, clinicians, and researchers to ensure that research and health service delivery effectively meets the needs of end users [1-3]. However, there is limited understanding of best practices for patient engagement in research among patients with venous thromboembolism, and there is sparse knowledge about challenges to and facilitators of this process [4, 5].

Purpose

To describe and explore the process and evaluate the impact of patient engagement in the development of a structured nurse-led post pulmonary embolism care model (the Attend-PE model).

Method

This prospective embedded case study of patient engagement was conducted within the research project - A structured Integrated post Pulmonary Embolism care model (Attend-PE). The study was based on documents from patient journey mapping (PJM), documents and field notes from workshops, and semi-structured interviews with two patients with pulmonary embolism, two clinicians working with pulmonary embolism patients and the Attend-PE researchers. Data were analyzed based on research questions guided by the United Kingdom six Standards for Public Involvement; Inclusive opportunities, Working together, Support & learning, Governance, Communications and Impact [6].

Result

Patients played a crucial role throughout the entire Attend-PE study, contributing to research questions, design, execution, and dissemination, as illustrated in Figure 1. The patients were involved at a higher engagement level in the co-creation of the Attend-PE model and co-creation of patient education and information material. Comparatively the engagement level was lower in decisions regarding outcomes, feasibility testing and implementation of the Attend-PE model, as shown in Figure 2. Notably, despite lower engagement level, the continuous involvement in the research process was invaluable to the patients. This ensured that they maintained insight in the research process, even though their contributions varied in the different phases.

Important facilitators of patient engagement within the Attend-PE were related to "Inclusive opportunities" and "Support and learning". Patients highlighted the importance of being a part of the process, working together as a team, and being prepared for the work. Important challenges to patient involvement related to "Communication" and "Governance", including lack of expectation alignment and communication regarding clinical elements where clinicians often dominated the discussions.

Conclusion

Patient involvement has been invaluable in the development of a structured nurse-led post pulmonary embolism care model. Further, patients will be involved in evaluation of the national implementation process and dissemination of study results.Figure 1Figure 2
Beyond telehealth in heart failure, how a nurse-led mobile team unit improves management and reduces re-admission in elderly patients living in a rural area
Authors: Soussi O , Lemiere P , Millot S , Preda D , Devemy F , Querion L , Mansuy E , Saunier E , Tardivat F , Reynier M , Pradier E , Semeria F , Quilici J
Published: October 2024

Introduction

Telehealth plays a crucial role in improving access to healthcare for elderly patients with heart failure (HF), particularly those who live in under-resourced geographic areas, have physical disabilities, or have difficulties with transportation.

However, extending and improving telehealth faces significant obstacles, primarily due to its inadequacy to fully assess a patient's clinical status. While some components of HF can be measured remotely, others, such as cardiac and lung examinations, require in-person clinical evaluation. This leads to imprecise assessments of HF severity, excessive medical consultations, and inappropriate emergency transfers or hospitalization.

We conducted an evaluation of a specialized mobile team unit (MTU) for HF care in a rural area. The team consisted of nurses trained in performing electrocardiograms (ECGs) and point-of-care ultrasound (POCUS) to extend telemonitoring services to elderly patients with HF in the comfort of their homes. This approach offers a direct and optimal evaluation of the patient's condition.

Purpose

This study aimed to evaluate the added value of an MTU in assessing and optimizing treatment for HF in elderly patients, ultimately leading to decreased hospitalization or medical request rates.

Materials and methods

This retrospective analysis examined data from elderly patients diagnosed with HF, who participated in a 6-month telemonitoring program, and received MTU visits. MTU was activated by the telemonitoring team, in patients presenting signs of decompensation, with an imprecise assessment of severity based on digital data only.

Results

During six months, 165 elderly patients were included in the telemonitoring platform after index HF hospitalization. The mobile team traveled over 3,040 kilometers in a rural area with limited cardiological care. They visited 84 patients (50.9%), offering personalized education and physical assessments to all of the patients, 45% received additional imaging (EKG, POCUS). This resulted in 30% therapeutic adjustments, 90% fewer General practitioner (GP) /Cardiologist consultations and 54% avoided readmissions: 20% of hospitalizations were avoided immediately and 34% likely avoided in the near future, demonstrating the intervention's 80% effectiveness.

Conclusion

Monitoring heart failure (HF) in elderly patients can be challenging, especially in rural areas with limited access to care, few caregivers, cognitive impairments, or difficulty using e-health platforms. This study emphasizes the benefits of a nurse-led mobile team unit in providing a reliable direct assessment of HF severity. This home intervention provides education, strengthens the decision-making capacity of the remote monitoring platform, assists with patient management, and significantly reduces the readmission rate.
Safe transition home after transcatheter aortic valve implantation feasibility (START-F) study: Very early self-reported function and recovery status after early discharge
Authors: Lauck S , Yu M , Polderman J , Low C , Smith A , Hyde E , Borregaard B
Published: October 2024

Background

Despite significant excellent outcomes and significant improvements in rapid recovery protocols and early discharge, rates of 30-day readmission after transcatheter aortic valve implantation (TAVI) remain stubbornly high. There can be numerous drivers for readmission in older people with valvular heart disease; the time of transition home is the period of greatest risk when patients are most vulnerable to repeat hospitalization. We sought to assess patients’ early recovery (< post-procedure day 3) to inform the evaluation of a nurse-led telephone intervention to mitigate the risks of 30-day readmission after early (1 day) discharge TAVI.

Methods

We conducted a prospective observational cohort feasibility study in five Canadian TAVI programs to ascertain acceptability, compliance, delivery of the intervention, recruitment and retention. In this preliminary report, we assessed key indicators of patients’ status at the time of discharge and functional status in their early recovery.

Results

We recruited 188 participants, mean age 81.6 years (SD=7.0), 41.5% women with baseline mean KKCQ 53.4 (SD=20.1). Patients were admitted on the day of the procedure, and underwent TAVI with a balloon-expandable (n=146, 77.6%) or self-expanding (n=30, 16.0%) device, with local anaesthesia +/- light sedation (n=171, 90.1%) or general anaesthesia (n=17, 9.0%). There was 1 in-hospital death (0.5%) and 182 (97.4%) were discharged home. At the time of the 3-day telephone follow-up, 168 (95.5%) reported that an individual had stayed with them during the first 24 hours [spouse: 85 (48.9%); friend: 77 (44.3%)], and 165 (93.3%; missing: 11) were able to complete all activities of daily living, including cooking their own meals (n=80, 45.5%). Self-reported energy was low/very low for 49 participants (26.1%), moderate for 93 (52.8%), and high/very high for 34 (18.1%), with 104 (55.3%) describing feeling somewhat or quite a bit better than before TAVI, and 24 (12.8%) somewhat or quite a bit worse. Bruising was present in 120 participants (68.6%) and 137 (78.8%) had removed their bandage as instructed. 18 participants were readmitted within 30 days, including 14 cardiac-related events.

Conclusion

This is one of the first reports of TAVI patients’ self-reported function following next-day discharge, providing novel evidence that most patients describe safe self-care and very early improved functional status. Research is needed to identify and support patients who are at higher risk of poorer trajectories of recovery and readmission, and report the feasibility of a nurse-led intervention to mitigate these risks.
Advancing care in type 2 diabetes and atherosclerosis: assessing pharmacist-led strategies in England's primary care - a retrospective observational study
Authors: Sharma R , Hasan S S , Conway B R , Ghori M U
Published: October 2024

Background

Type 2 diabetes (T2D) mellitus is a complex multi-system disorder with a tendency to develop complications. ‘Persistence to therapy’ plans affect almost 50% of all people with T2D, leading to suboptimal glycaemic and cardiovascular (CVD) risk factor control. The interplay between T2D and established atherosclerotic cardiovascular disease (ASCVD) underscores the need for a comprehensive, multidisciplinary approach to care that addresses both glycaemic control and CVD risk factors.

Purpose

This study retrospectively analysed pharmacist-led interventions in patients living with T2D and established ASCVD in general practices in a large UK city.

Methods

A retrospective pre-post observational study was conducted using electronic health record data from men and women diagnosed with T2D and ASCVD. Participants attended an independent prescribing pharmacist-led clinic within 21 general practices. The practices operated linked and comprehensive electronic health records systems, facilitating access to high-quality longitudinal data that align with study objectives.

Results

A total of 380 patients received pharmacist-led interventions. Comparisons of pre-intervention and post-intervention data revealed a significant decrease in HbA1c levels (73.7 ± 10.7 mmol/mol vs 67.2 ± 8.5 mmol/mol, p < 0.01), systolic blood pressure (132 ± 10.2 mmHg vs 125.9 ± 9.9 mmHg, p < 0.01), and diastolic blood pressure (76.8 ± 5.9 mmHg vs 72.4 ± 5.0 mmHg, p < 0.001). Additionally, total cholesterol levels significantly reduced from 4.4 ± 0.7 mmol/L to 3.9 ± 0.7 mmol/L (p < 0.001). The proportion of patients achieving the triple target of HbA1c (≤58 mmol/mol), BP (≤140/80 mmHg), and total cholesterol (≤5 mmol/L) increased significantly from 2.4% before the intervention to 23.4% afterward (p < 0.001). The initiation of the sodium-glucose co-transporter 2 inhibitors (SGLT2i) therapies was the most common prescribing intervention (67%).

Conclusions

Pharmacist-led interventions in a general practice setting significantly improved several critical diabetes and CVD risk related outcomes. These findings underscore the potential benefits of integrating pharmacist-led care models into the management of T2D, particularly for patients with concurrent ASCVD and adopting an individualised, evidence-based approach to prescribing interventions.
A novel nurse-led, multidisciplinary, and guideline-directed disease intensive management program improves long-term survival of pulmonary hypertension patients
Authors: Guo J , Wang J Q , He J , Chen Y C
Published: October 2024

Background

Although the overall survival of pulmonary hypertension (PH) patients improved in the current era, better management strategy for PH patients still needs further exploration. Here, we proposed a novel nurse-led, multidisciplinary, and guideline-directed disease intensive management program and hypothesized this strategy would improve the overall survival of PH patients.

Methods

Patients were prospectively enrolled and divided into the intensive management group (IDM) and the conventional disease care (CDC) group voluntarily. Intensive management was nurse-led and delivered by multidisciplinary teams, including patient education, symptoms monitoring, and patient adherence improvement with careful outpatient or inpatient assessment every 3 months and medicine prescription whenever needed. Patients with conventional care were treated by primary cardiologists’ consultants and telephone follow-ups every year.

Results

Overall, 413 PH patients were finally included. Both total survival (p<0.001) and event-free survival (p=0.008) of the IDM group were significantly higher than the CDC group. After adjustment of age and sex, intensive management was also an independent protective predictor for both primary [all-cause mortality, HR 0.378, 95% CI (0.214-0.668), p<0.001] and composite endpoints [all-cause mortality and re-hospitalization, HR 0.648, 95% CI (0.454-0.927), p=0.017]. In subgroup analysis, IDM was beneficial in prolonging the overall survival of patients in high-risk situations (HR 0.283, 95% CI 0.125-0.641, p=0.002) and with advanced RV dysfunction (HR 0.250, 95% CI 0.123-0.510, p<0.001).

Conclusion

Nurse-led, multidisciplinary, and guideline-directed disease intensive management program represented an ideal method to strengthen the overall and event-free survival of PH patients, especially for those in advanced stages of PH.Novel management program
The effectiveness of various intensity interval training compared to continuous training in patients with heart failure: a systematic review and network meta-analysis
Authors: Okamura M , Shimizu M , Yamamoto S , Nishie K , Konishi M
Published: October 2024

Background/Introduction

Cardiac rehabilitation including exercise therapy is beneficial for improving exercise tolerance, physical function, quality of life (QOL), and life expectancy in patients with heart failure. High-intensity interval training (HIIT) is increasingly reported as a new training modality to improve exercise tolerance more effectively than conventional moderate-intensity continuous aerobic training (MCT). However, the optimal intensity of HIIT for achieving the greatest effects in patients with heart failure remain controversial.

Purpose

This systematic review and network meta-analysis aimed to examine the effectiveness of HIIT compared to MCT on exercise tolerance in patients with heart failure.

Methods

We searched for studies published up to 30 January 2024 in Embase, MEDLINE via Ovid SP, PubMed, and the Cochrane Library via Wiley Online Library with no limitations on data, language, or publication status. We included randomised controlled trials comparing the efficacy of HIIT and MCT on peak oxygen uptake (VO2), as a measure of exercise tolerance, aerobic threshold (AT) VO2, ventilatory equivalent of carbon dioxide (VE/VCO2) slope, and QOL. We categorized HIIT into three different intensities [> 80% heart rate max (HRM), 60-80% HRM, < 60% HRM] and compared the outcomes with those of MCT.

Results

We identified 15 randomised controlled trials with 557 patients from a total of 27,931 records. Our network meta-analysis revealed that the following results of peak VO2 from the comparisons among the three intensities of HIIT and MCT: > 80% HRM (10 studies, mean difference 1.27 ml/min/kg, 95% confidence interval -0.20 to 2.73), 60-80% HRM (3 studies, 1.47, -0.89 to 3.83), < 60% HRM (2 studies, 2.10, -1.22 to 5.43) (Fig. 1). Regarding AT VO2, HIIT at 60-80% HRM demonstrated significant improvements compared to MCT: > 80% HRM (8 studies, 0.18, -0.83 to 1.18), 60-80% HRM (2 studies, 2.50, 0.72 to 4.28), < 60% HRM (2 studies, 1.14, -0.93 to 3.20) (Fig. 2). Regarding VE/VCO2 slope, HIIT at 60-80% HRM also demonstrated significant improvements compared to MCT: > 80% HRM (7 studies, 0.29, -1.89 to 2.47), 60-80% HRM (2 studies, -3.26, -6.18 to -0.35), < 60% HRM (no study). The network meta-analysis did not reveal any significant differences in QOL between the various intensities of HIIT and MCT.

Conclusions

Our systematic review and network meta-analysis revealed that HIIT prescribed at a sub-maximal intensity, 60-80% heart rate max, is most effective in improving exercise tolerance. Higher intensity does not necessarily yield better outcomes, and future research on interval training should include detailed methodologies that account for the training intensity.Fig.1 Network meta-analysis on peak VO2Fig.2 Network meta-analysis on AT VO2
Quality evaluation of field safety notices of medical devices, across EU and non-EU countries
Authors: Ren Y , Fraser A G , Caiani E G
Published: October 2024

Introduction

The EU Medical Device Regulation 2017/745 (MDR; article 87) requires device manufacturers to report to the relevant authorities any serious incident and any corrective action undertaken, through Field Safety Notices (FSN) whose content must be consistent in all Member States. Before MDR, due to the lack of harmonized global standards for reporting, FSNs were published independently by each country with different formats, styles, nomenclatures and languages. This heterogeneity makes it challenging to use such historical data for trend analysis in post-market surveillance (PMS).

Purpose

1) To assess the quality of the FSNs issued by EU or non-EU national authorities for analysing historical trends. 2) To group countries based on such quality, and test for differences between EU and non-EU countries.

Methods

As part of the CORE-MD project coordinated by the ESC, all FSN information publicly available as HTML text (excluding linked PDFs) was retrieved automatically from national authorities’ websites, using the CORE-MD PMS tool [1]. For each FSN, a score was assigned to each field of interest (1 or 2, according to importance for building trends) (see Figure 1), and a Percentage Score (PS) computed as the % of the ratio between the scores’ sum and the maximum possible score (10). Based on PS, FSNs were categorized as Excellent (≥90), Very Good (80≤PS<90), Good (70≤PS<80), Medium (60≤PS<70), or Unqualified (<60). For each country, the distribution of FSN categories was computed. Hierarchical Agglomerative Clustering (HAC) was used to group countries based on their similarities, and differences between EU and non-EU countries were assessed by Mann-Whitney U test.

Results

126,405 FSNs published before 31/12/2022 were retrieved and analyzed. The % of FSN in which each field was available is shown by country in Figure 1. Manufacturer and Device were clearly described, but more detailed device information was often absent. The Description, with the reason for publishing the FSN, and the Device Category, allowing nomenclature categorization, were provided only by a few countries. The % of FSNs in each quality category is reported in Figure 2: only Italy provided some FSNs (31%) classified as Excellent. The HAC analysis identified three clusters: "high-quality FSNs" (Czechia, Denmark, Italy, Sweden, the Netherlands, and the USA), "moderate-quality FSNs" (Australia, Canada, Greece, Ireland, Latvia, Slovenia, Spain, and the UK), and "low-quality FSNs" (Croatia, Estonia, France, Germany, Poland, and Portugal). There was no difference between EU and non-EU countries.

Conclusions

A significant discrepancy was observed in the quality of FSNs retrieved from different countries, highlighting the difficulty in using such data to analyse trends, for example in reports of cardiovascular devices. This study reinforces the need for a global minimum reporting standard, to facilitate more effective PMS.Figure 1Figure 2
Validation of diagnostic wearable ECG data from high-risk patients and integration into the atrial fibrillation catheter ablation pathway
Authors: Ahluwalia N , Saengkrajang G , Maclean E , Abbass H , Hussain A , Butcher C , Schilling R J
Published: October 2024

Background

Wearables like the Apple Watch (AW) offer non-invasive, long-term monitoring and patient-led, single-lead ECG recordings. Although validated for Atrial Fibrillation (AF) detection in the general population, the accuracy and clinical utility of the single-lead ECG recording feature for the diagnosis of AF has not been evaluated in patients at high risk of atrial arrhythmias.(1) An evaluation of the accuracy of the Wearable-derived labels is important to determine the feasibility of integrating AW-based remote monitoring into clinical pathways.

Objective

To evaluate the accuracy and data burden from AW-based, patient-led ECG monitoring in patients undergoing AF catheter ablation.

Methods

Patients referred for first-time AF catheter ablation to the Study Institution between January 2022 and March 2023 were invited to enrol in the AF Follow-up with Apple Watch (AFFU-AW) Trial. Patients randomised to the active arm of the study received 12 months of post-ablation follow-up using a loaned Apple Watch. Participants underwent an education session and were asked to perform daily ECGs; sending through any labelled as AF, inconclusive, or in the context of symptoms. The accuracy of the automated ECG labelling was determined against blinded, independent labelling by two experienced Electrophysiologists.

Results

1093 remote transmissions were emailed in for evaluation from 52 patients to the Study team. This was a median of 6 [2, 17] per patient and a receiver burden of 2 [1, 4] ECGs per day. 475 were categorised by the AW as AF with a sensitivity of 0.71 and specificity of 0.96 compared to expert labelling. 342 (31%) were not categorised by the wearable, with an ‘inconclusive’ interpretation (161 [47%]) or ‘high heart rate’ (108 [32%]) as the most cited reasons. 178 (55%) of the uncategorised ECGs were subsequently overread as AF. Accompanying symptoms were reported with 324 (30%) ECGs, most commonly palpitations.

Conclusion

This is the first reported evaluation of the AF diagnostic feature based on AW ECG recordings as part of a remote patient rhythm surveillance pathway after catheter ablation. The high positive predictive value of ‘Sinus Rhythm’ and ‘AF’ labels and the high specificity of the ‘AF’ label are comparable to diagnostic algorithms in implantable loop recorders.(2) The source code to automate data curation and structured databasing of wearable ECGs has been made available for care providers to integrate the service into device monitoring workflows.(3)
Deciphering postoperative hemodynamics in pediatric intensive care: insights from patient-specific computational fluid dynamics models for congenital heart disease
Authors: Sughimoto K , Koda D , Maeda N , Miki T , Fujiwara T , Sakurai T , Sakurai H , Liu H
Published: October 2024

Purpose

Precise hemodynamic control is imperative for optimizing outcomes in children undergoing cardiac surgery, particularly in the pediatric intensive care unit (PICU). Computational fluid dynamic (CFD) models tailored to individual pediatric patients following definitive repairs for congenital heart disease hold the potential to discern and analyze the intricacies of their hemodynamic systems. This study aims to explore the applicability of patient-specific CFD models in determining optimal treatment strategies to enhance patient hemodynamics.

Methods

Ten postoperative pediatric patients (6 females and 4 males, median age 4.5±3.6 months, median height 62.5±5.3 cm, and median weight 6.2±1.3 kg) diagnosed with ventricular septal defect (VSD, n=5), atrial septal defect (ASD, n=2), and atrioventricular septal defect (AVSD, n=3) were enrolled. Continuous radial arterial waveforms and sporadic arterial blood draws, including lactate levels, were recorded in the PICU postoperatively. Fluid dynamics equations were employed to model major and peripheral blood vessels, utilizing a 0–1-dimensional hemodynamic model validated in previous studies and refined for this investigation. Sensitivity analysis based on correlation coefficients identified significant parameters, adjusted for replicating pressure waveforms. Comparison and re-approximation was made with actual radial artery pressure waveforms from 78 arterial waveforms and blood draws. Updated parameters were then correlated with postoperative time and lactate levels in the ICU.

Results

Replicated waveforms demonstrated high agreement with original arterial waveforms, with mean, maximal, and minimal blood pressure percentages of 96.7±1.8%, 96.7±2.4%, and 95.8±2.4%, respectively. Notably, systemic vascular resistance and compliance exhibited a substantial relationship with time elapsed after admission to the PICU, with correlation coefficients (R) of 0.26 and 0.43, respectively. Lactate levels demonstrated a decrease with reduced peripheral vascular resistance and an increase in vascular compliance, with R of 0.23 and 0.32, respectively, aligning with expected clinical improvement and stability.

Conclusion

This study successfully attained precise replication of radial artery blood pressures and intricate pressure waveforms using patient-specific CFD models in pediatric patients following congenital heart disease repairs. The insights gained shed light on the pivotal hemodynamic parameters that impact both stability and instability. This research sets the stage for theoretically informed treatments by amalgamating real-time biological information with patient-specific CFD models.Re-approximation of arterial waveformCorrelation of hemodynamic parameters
Evaluating the feasibility of upper arm ECG for cardiac monitoring
Authors: Diven J , Adair R , Rowny J , Birmingham A , Jardine D
Published: October 2024

Background

Ambulatory electrocardiogram (ECG) monitoring is essential for detecting paroxysmal cardiac events and tracking long-term physiological parameters. However, traditional Holter devices are cumbersome, restrict movement, and can cause skin irritation, resulting in sub-optimal patient compliance during extended monitoring periods. Wearable devices utilizing non-conventional form factors are emerging as promising non-intrusive alternatives for ECG monitoring.

Purpose

To determine the optimal location for single-lead ECG acquisition on the upper arm and assess the feasibility of using dry electrode armbands for cardiac monitoring when coupled with robust ECG processing software.

Methods

Stationary ECGs from 20 healthy adult participants were recorded across a two-phase data collection protocol and processed with clinically validated ECG software. In the first phase, the average signal amplitudes of wet-electrode ECGs obtained from 30 different configurations across the mid-brachial and proximal-brachial regions of the upper arm were assessed. In the second, the performance of two proprietary dry-electrode armbands, featuring either a single-point or three-point electrode strap, were evaluated on the optimal upper arm configuration using QRS detection and heart rate (HR) metrics. Comparative analysis was performed throughout using data simultaneously acquired on a gold standard Holter device in modified lead III (MLIII) configuration. Performance metrics were calculated relative to manually generated signal annotations.

Results

Wet-electrode-acquired ECGs from the proximal-brachial region exhibited significantly higher average signal amplitudes (0.12 ± 0.06 mV) compared to those from the mid-brachial region (0.05 ± 0.04 mV) of the upper arm (p < 0.05). The optimal configuration (A-F), positioned at the lateral and medial extents of the proximal-brachial region, demonstrated comparable average signal amplitudes (0.16 ± 0.07 mV, 0.16 ± 0.06 mV, 0.19 ± 0.08 mV) for single-point dry, three-point dry, and wet-electrode setups, respectively. Upon processing with ECG software, data from optimal dry-electrode configurations showed excellent average QRS sensitivity and specificity of 98.2% and 98.9% for the one-point strap, and 98.9% and 99.8% for the three-point strap, respectively. HR metrics from the optimized dry-electrode setups were comparable in accuracy to those derived from the reference Holter device, with the average HR error being similar across the three-point dry electrode strap (0.57% ± 0.28%), one-point dry electrode strap (1.16% ± 1.46%), and Holter device (0.74% ± 1.50%).

Conclusion(s)

Optimized upper arm single-lead ECG with dry electrodes can yield signals with sufficient amplitude and quality for automated ECG analysis. Coupled with robust ECG signal processing software, the cardiac metrics obtained from the optimized upper arm setups exhibited comparable performance to those derived from medical Holter data.
A novel 12-lead standard ECG derived scoring system for risk stratification in the emergency department
Authors: Theurl F , Schreinlechner M , Dolejsi T , Hofer F , Massmann C , Reinstadler S J , Toifl M , Schmidt G , Hnatkova K , Malik M , Bauer A
Published: October 2024

Background/Aims

The ECG represents a routinely used diagnostic tool in the assessment of patients in the emergency department. Beside specific alterations within the ECG indicative of underlying pathologies, such as ST-elevation in myocardial infarction, several other parameters have been developed over the past decades that proofed themselves as strong and independent predictors of worsened outcome independent of a specific underlying pathology. These include basic metrics such as heart rate and QRS duration, but also more advanced biomarkers such as QRS microfragmentation (QRSµf) [1] and total cosine R to T (TCRT) [2] . In this work, we aimed to summarize all these markers derived from 12-lead standard ECGs into one novel risk score and evaluate its performance as mortality predictor in the emergency department.

Methods

We retrospectively collected 10- second 12- lead ECG recordings unselected patients presenting to the emergency department of a large university hospital between September 2019 and June 2022. For each patient a risk score was calculated from the first retrieved ECG using the criteria displayed in Figure 1. Calculation of all parameters was done as previously published. Patients with a total score of 0-1 points, 2-3 points, 4-6 points and 7 points were considered as low, intermediate, high and very high risk, respectively. The performance of the risk score was evaluated using Cox regression analysis and Kaplan-Meier curves with all-cause mortality within one year as primary endpoint.

Results

In total, 10 781 patients were included in our analyses. Median age was 69 (IQR 54-80) years, 5 588 (52%) were male and 5 193 (48%) were female. 6 966 (65%) patients were considered as low risk, 2 656 (25%) patients as intermediate risk, 1 098 (10%) patients as high risk and 61 (1%) patients as very high risk according to the ECG risk score with one year mortality rates of 8%, 17%, 26% and 43%, respectively (p < 0.001 for difference). In cox regression analysis the novel ECG risk score was a strong and independent predictor of one-year all-cause mortality with a hazard ratio of 1.35 (95% CI 1.31-1.39, p < 0.001). Kaplan-Meier survival curves for different risk categories are displayed in Figure 2.

Conclusion

In an unselected population of patients presenting to the emergency department of a large university hospital a novel risk score calculated from standard 12-lead ECGs recorded during clinical routine was a strong and independent mortality predictor. Further studies are needed to evaluate how incorporation of the novel score into existing triage systems can improve patient care.Figure 1:Calculation of ECG risk scoreFigure 2:Kaplan-Meier survival curves
Detection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms
Authors: Sangha V , Oikonomou E K , Khunte A , Miller E J , Khera R
Published: October 2024

Background

Amyloid cardiomyopathy (ATTR-CM) is often underdiagnosed, with very few diagnosed before the onset of symptoms, leading to delayed use of disease-modifying therapies that could have substantial prognostic implications early in the disease course. In the US, the disease has a substantially higher prevalence among Black adults, who are further challenged by lower access to healthcare services and lower preventative care, further reducing the ability for clinician-led diagnosis. We sought to develop an approach for identifying ATTR-CM on noisy single-lead ECGs obtainable on portable and wearable ECG devices, allowing broad use in community screening.

Purpose

To develop a portable/wearable device-adapted AI-ECG algorithm capable of detecting ATTR-CM on noisy single-lead ECGs.

Methods

We identified patients with ATTR-CM from 2015 through June 2023 across 5 hospitals of a large U.S.-based hospital system using positive bone scintigraphy scans or pharmacotherapy with an approved transthyretin stabilizer. In the development cohort, we used lead I of 12 lead ECGs, commonly captured by portable and wearable ECG devices, to train a novel noise-adapted model explicitly designed to infer information against simulated real-world noises. For this, ECG signals were augmented using random Gaussian real-world noise within four frequency ranges at multiple signal-to-noise ratios. We tested the model in an independent set of cases and matched controls drawn from July through December 2023.

Results

The development cohort consisted of 1,011 ECGs from 234 patients (median age 79 years [IQR:70-85], 17% women), with 10:1 age- and sex-matched 10,110 controls with 10,110 ECGs (age 79 [IQR:70-85] years 17.7% female). In the independent test set of 139 ECGs from 47 patients (age 80 [75-86] years, 32% women) and matched controls (1390 ECGs and patients) from July through December 2023, the AUROC (area under the receiver operating characteristic curve) of the AI-ECG model for ATTR-CM was 0.90 [95%CI: 0.88-0.92] (A). The AUPRC was 0.44 [0.36-0.53]. The sensitivity and specificity of the model were 0.85 and 0.80. The positive predictive value ranged from 0.12 at a 3% prevalence level to 0.59 at a 25% prevalence (B).

Conclusions

A novel portable and wearable-devices adapted model demonstrates promising performance for detecting ATTR-CM on single-lead ECGs, representing a more accessible method for screening in community-dwelling adults.
Performance evaluation of ChatGPT 4.0 on cardiovascular questions from the medical knowledge self-assessment program
Authors: Malkani K , Zhang R , Zhao A , Jain R , Collins G P , Parker M , Maizes D , Zhang R , Kini V
Published: October 2024

Background

Medical trainees are increasingly using online chat-based artificial intelligence (AI) platforms as supplementary resources for board exam preparation and clinical decision support. Prior studies have evaluated the performance of AI chatbots like ChatGPT on various general standardized tests such as the United States Medical Licensing Examination (USMLE), but little is known about their performance on subspecialty-focused exam questions, particularly related to clinical management and treatment.

Objective

This study aims to evaluate the performance of ChatGPT version 4.0 on the cardiovascular questions from the Medical Knowledge Self-Assessment Program (MKSAP) 19, a widely used resource for board exam preparation in the United States.

Methods

We submitted all cardiovascular questions from MKSAP 19 to ChatGPT 4.0, covering a broad range of cardiology topics in a multiple-choice format. Performance was gauged against both the official MKSAP answer key and average trainee scores obtained from the MKSAP website. Out of 129 questions, 4 were invalidated due to post-publication data, and 18 were excluded due to reliance on visual aids, leaving 107 questions for analysis.

Results

ChatGPT 4.0 correctly answered 93 out of 107 questions, reflecting an 87% accuracy rate, compared to a 60% accuracy rate averaged among all human users for the same questions (p<0.0001). ChatGPT accuracy rates for each question category (e.g., heart failure, electrophysiology, etc.) are provided in the figure. On the 14 questions that ChatGPT answered incorrectly, human users averaged a 47% accuracy rate. All 14 incorrectly answered questions were related to clinical management and treatment (as opposed to diagnosis or epidemiology), and the majority (10/14) involved choosing treatment medications or choosing between medications versus interventions.

Conclusion

ChatGPT 4.0 surpassed the typical 70% accuracy threshold required for passing the internal medicine board certification but performed less well in answering questions about clinical management and treatment. This performance suggests that ChatGPT 4.0 could be an effective adjunct tool for cardiology education, but raises some concerns about its use in clinical decision support for trainees.
To compare 24 hour blood pressure values of a conventional 24 hours ambulatory blood pressure device versus a wristband using an artificial intelligence algorithm based on photophletysmography
Authors: Ronner E , Van Vliet M , Monnink S , Constandse J , Hoftijzer H , Schipper J , Hauer A , Van Bronswijk P
Published: October 2024

Background

Ambulatory Blood Pressure Monitoring is of high added value in determining blood pressure, but also highly relevant quantitatively, as hypertension is the single most common medical condition. Unfortunately, practical and qualitative limitations are numerous.

Purpose

To determine the accuracy of a novel wrist-worn cuff-calibrated photoplethysmography (PPG)-based remote patient monitoring device versus ambulatory blood pressure monitoring (ABPM).

Methods

The single-center prospective study included 40 adult patients with standard 24-hour ABPM with a 30-minute measurements. Participants used a wrist-worn PPG device during the ABPM period. Measurements of the average difference and standard deviation (SD) for the full 24-hour period as well as for awake and asleep periods were compared. Differences in awake-asleep BP change between the investigational and reference device were described for all, and per nighttime BP dipper category. And also, investigational BP monitoring accuracy under motion will be visualised.

Results

During the 24-hour period the absolute difference in average SBP and DBP was 3.65 (SD±4.57) and 2.98 (SD±3.91), respectively. Similar results were found for the subsets of day- and nighttime determinations. The absolute difference in average awake-asleep BP change was 2.36 (SD ± 2.40) for SBP and 2.17 (SD ± 2.13) for DBP. Comparable absolute differences were present within each of the nighttime dipper categories. The performance of the investigational device remained stable during motion.

Conclusions

This study demonstrates initial evidence supporting the accuracy of the evaluated wrist-worn PPG-based device in measuring 24-hour as well as day- and nighttime BP. In the average difference and SD remained within ISO-81060-2:2018 cut-off values from recent ESH guidelines. Therefore, the presented method can provide an accurate and more patient friendly alternative to ABPM.
Temporal and geogrpahic trends in female representation within European cardiovascular imaging among 13,436 individuals associated with EACVI
Authors: Berrill M , Santos C , Baltabaeva A
Published: October 2024

Introduction

Women are under-represented in cardiology trial enrolment, specialty training, faculty, scientific authorship and board membership in cardiology journals and societies. An EACVI Task Force on Women in Cardiovascular Imaging aims to promote women’s representation and leadership, but data is lacking in this area.

Purpose

To characterise the representation of women in cardiovascular imaging within Europe and examine temporal and geographic trends.

Methods

We scraped publicly available name data from over 100 online sources including EACVI taskforces, committees, research project members, consensus and position paper authors, board membership and past presidents alongside members of affiliated cardiovascular imaging national societies and working groups. We also included names from EACVI or national accreditation records for cardiac imaging. We recorded data on country location and year of affiliation. We defined gender in a binary manner as either male or female. The previously validated artificial intelligence (AI) system Namsor assigned gender based on name morphology and geolocation data. We pre-set a confidence of gender-name matching of 95%.

Results

The AI was able to assign gender at 95% confidence to 12,643/13,436 (94.1%) names. The overall number of names assigned female gender was 5903/12643 (46.7%), rejecting the null hypothesis that gender was equally distributed (i.e. 50% female) [p<0.0001]. Among the 9712 individuals with year of affiliation we observed female representation to increase over time: 1989-2000 4/18 (22.2%), 2001-2004 107/287 (37.8%) 2005-2009 77/170 (45.3%), 2010-2014 109/203 (53.7%), 2015-2019 1063/2022 (51.3%) and 2020-2024 3352/3064 (52.2%) (Cochran-Armitage trend test 24.196 [p<0.0001]) (Figure 1). Female representation was lower in countries outside of Europe, compared to within Europe; 740/2654 (27.9%) vs. 5163/9989 (51.7%) [p<0.0001]. The lowest rate of female representation was in Saudia Arabia (15.1%), the highest in Lithuania (83.3%). Figure 2 displays a choropleth of gender representation (%) between European countries with n>20.

Conclusion(s)

There are significant limitations in this form of analysis, including that we have arbritarily defined gender in a binary format, thereby excluding individuals who do not identify with such a social construct. It is also likely that the excluded names based on the 95% confidence of the AI software disproportionately represents individuals from ethnic minorities. However, our data provides a basic understanding of the representation of women in European cardiovascular imaging over time and between countries. We observed a trend of increasing female participation but with substantial variation between countries both inside and outside of Europe. This data may be useful to refine and direct efforts to improve female representation in areas in which it is needed most.
Detection and risk stratification of cardiac amyloidosis patients by integration of imaging and non-imaging data using a machine learning approach
Authors: Spielvogel C P , Haberl D , Kluge K , Mascherbauer K , Hennenberg J , Yu J , Ning J , Traub-Weidinger T , Calabretta R , Mascherbauer J , Kammerlander A , Hengstenberg C , Hacker M , Nitsche C
Published: October 2024

Background

With the advent of amyloid-targeting therapies, early and reliable diagnosis as well as precise risk estimation of cardiac amyloidosis (CA) have become of substantial importance. While the current diagnostic approach relies on difficult-to-standardize, visual interpretation of 99mTc-scintigraphy, risk assessment is largely based on a combination of blood and imaging parameters.

Purpose

Aim of this study is to assess the possibility of machine learning-based integration of scintigraphy, echocardiography, and non-imaging parameters such as blood parameters and comorbidities for the detection and risk stratification of patients with CA.

Methods

This study included all patients who underwent 99mTc-scintigraphy at a university affiliated tertiary care centre between 2010 and 2023. Two machine learning models were developed, and the relative predictive importance of their parameters were assessed (Figure 1): First, a model to detect patients with CA-suggestive uptake (Perugini grade 2/3) on 99mTc-scintigraphy scans; Second, a model to assess the risk of patients with CA-suggestive uptake for future heart failure hospitalization (HFH). A total of 58 features were extracted from electronic health records including blood, echocardiographic, demographic parameters and comorbidities. Scintigraphy imaging features were extracted from the raw imaging data using a deep learning approach. For the time to HFH prediction, a random survival forest machine learning model was employed.

Results

Overall, 12 380 consecutively enrolled patients were included, 279 (2.3%) of which were affected by CA-suggestive uptake. The machine learning model showed higher accuracy for the detection of CA (AUC 0.96 [95% CI 0.95-0.97], sensitivity 0.78 [95% CI 0.77-0.80] and specificity 0.99 [95% CI 0.99-0.99]. In the outcome analysis, the machine model showed good accuracy in predicting future HFH (C-index 0.71 [95% CI 0.68-0.75]). Parameter importance for the time to event analysis revealed right ventricular diameter, previous diagnosis of chronic heart failure and creatine kinase as the three most important predictors (Figure 2).

Conclusions

Detection of patients with cardiac amyloidosis and their risk estimation for heart failure can be aided using machine learning-based integration of imaging and non-imaging data. Our findings may guide novel approaches for assessing disease progression and treatment response.
Deep learning enables the fully automated quantification of high-resolution cardiac CT images: human in loop for model development
Authors: Mohammadi Kazaj P , Baj G , Schutze J , Stark A , Valenzuela W , Giannopoulos A A , Ralf Buechel R , Graeni C , Shiri I
Published: October 2024

Introduction

Cardiac computed tomography (CCT) images provide vast spatial and temporal information. Segmentation of the structure of interest is necessary to provide quantitative information. However, manual segmentation of cardiac substructures is highly time-consuming.

Objective

This study aims to develop a robust deep-learning segmentation model for cardiac substructure segmentation in 3D CCT images.

Method

We initially developed a 3D U-Net architecture, incorporating extensive augmentation, for the segmentation of the Left Ventricle Myocardium (LVM), the blood cavities of the Left and Right Ventricles (LV, RV), and the Left and Right Atria (LA, RA). This model utilized CT images from 20 patients who had available manual segmentation. Subsequently, we applied this initial model to datasets from two centers, including different generations of scanners. We then visually inspected the segmentation, selected the highest-quality segmentations, and modify it if necessary, and excluded the low-quality data. We continued fine-tuning the model with the new dataset until we reached a count of 456 patients. As there were multiple images from one patient during this model's development, we generated datasets to develop a new model using the entire new dataset to avoid any data leakage in the reported metrics. The model was developed using data from Center 1 (5-fold cross-validation) and then evaluated on an external test set from Center 2 (126 patients) and also 4D images from Center 1. Various metrics were calculated to assess the model's performance.

Results

In the internal test set, utilizing 5-fold CV, the model demonstrated high segmentation accuracy, achieving an overall Dice coefficient of 0.98, with individual scores of 0.96 for LVM, 0.98 for LV, 0.98 for RV, 0.99 for RA, and 0.98 for LA. We assessed the automatic quantification capability of the segmentation model from 256 patients, resulting in an R2 value greater than 0.92 for both LV end-diastolic and systolic volumes. In contrast, the model maintained robust performance on the external dataset, achieving an overall Dice coefficient of 0.96, with individual scores of 0.95 for LVM, 0.98 for LV, 0.97 for RV, 0.98 for RA, and 0.94 for LA.

Conclusion

We provided a large cohort of ground truth segmentations for different cardiac CT substructures using a human-in-the-loop strategy. Subsequently, we developed a deep learning model for highly accurate automated segmentation of these substructures. This model allows for fast and fully automatic segmentation and quantification of 3D and 4D cardiac CT images, from which various quantitative metrics can be extracted from different cardiac substructures.