bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract

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&lt;90), Good (70≤PS&lt;80), Medium (60≤PS&lt;70), or Unqualified (&lt;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

Abstract

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

Abstract

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

Abstract

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 &lt; 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

Abstract

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 &lt; 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 &lt; 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

Abstract

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

Abstract

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&lt;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

Abstract

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.