bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ultra-short heart rate variability over 15 seconds is associated with increased long-term risk of cardiovascular disease
Authors: Orini M , Van Duijvenboden S , Young W J , Ramirez J , Jones A R , Tinker A , Munroe P B , Lambiase P D 
Published: November 2023

Abstract

Background
Heart rate variability (HRV) is an established cardiac autonomic marker with prognostic value, whose application at scale is limited by the fact it requires relatively long ECG recordings (≥ 5-min). Ultra-short HRV (usHRV) is derived from much shorter recordings and can be measured at scale using standard and wearable ECGs, but its association with cardiovascular events is undetermined.

Purpose
(1) To validate usHRV measured using 15-second ECGs and (2) To investigate association with multiple outcomes in individuals without cardiovascular disease.

Methods
Data from the National Survey for Health and Development (N=1,337) was used to assess the agreement between usHRV and standard HRV (which requires ≥5-minute ECGs). Participants from the first visit of the UK Biobank study (2009-2013), without cardiovascular disease and with an ECG (N=51,628, 64% male, median age 58) were recruited to assess association of usHRV with outcomes. Exposures included usHRV time-domain indices RMSSD and SDSD, which measure fast heart rate variations related to cardiac vagal modulation, and usHRV spectral index PHF, which is related to respiratory sinus arrhythmia. Outcomes included incident atrial fibrillation (AF), major adverse cardiovascular events (MACE), stroke and mortality.

Results
HRV indices RMSSD and SDSD measured in 15-second ECGs showed excellent agreement (median correlation coefficient ≥0.84 and narrow limits of agreement in Bland-Altman analyses, Fig. 1) with indices measured from 6-minute ECGs, while PHF showed good agreement (median correlation coefficient 0.70 and small positive bias). In the UK Biobank, after a median follow-up of 11.5 (11.4-11.7) years, incidence of AF, MACE, stroke, and mortality was 3.8%, 3.9%, 1.7% and 4.3%, respectively. An inverse linear association was found between usHRV and AF and stroke, with hazard ratios between 1.07 and 1.13 for a standard deviation decrease in RMSSD, SDSD and PHF, p&lt;0.05. Non-linear cox regressions (Fig. 2) showed that the association of RMSSD (and SDSD) with MACE and mortality was U-shaped with individuals in the bottom quintiles of usHRV indices showing increased risk with respect to those in the third quintile (hazard ratios between 1.16 and 1.29, p&lt;0.05).

Conclusions
Cardiac autonomic dysfunction measured with usHRV from 15-second ECGs is associated with increased risk of AF, MACE, stroke, and mortality in individuals without cardiovascular disease. usHRV can be measured from both standard clinical ECGs and novel wearable devices and may have a role to monitor autonomic function at scale and improve understanding and prevention of cardiovascular disease.Fig1: 6-min vs 15-sec HRVFig2: usHRV association with outcomes
Usefulness of CineECG method in identification of P-wave morphology variants in an elderly epidemiologic cohort
Authors: Baturova M , Van Dam P , Johnson L S B , Smith G , Platonov P 
Published: November 2023

Abstract

Background
Biphasic P waves in inferior leads categorizes interatrial block (IAB) into partial or advanced subtypes. P-wave morphology is highly variable, and visual assessment of P-wave morphology is subjective, especially in case of low-amplitude P waves. CineECG is a non-subjective approach which is not sensitive to signal amplitude, and which can also calculate terminal P-wave angle.

Purpose
To assess the relationship between P-wave morphology in inferior ECG leads, categorization of the IAB subtypes and terminal P-wave angle using CineECG in an elderly epidemiologic cohort.

Methods
We included participants with sinus rhythm on an ECG collected 2002-2006 in the general-population based Malmö Preventative Project (n=983, age 70±5 years, 38% females). ECGs were digitally processed using the Glasgow algorithm. IAB was defined as a P-wave ≥120 ms and classified regarding as partial (pIAB, P waves affected in none [pIAB-0] or only 1 inferior lead III [p-IAB-1]) or advanced (aIAB, P waves affected in leads III and aVF [aIAB-2] or all 3 inferior leads [aIAB-3, typical aIAB]). All ECGs were also processed using CineECG. The vectorcardiogram was computed from the 12 lead ECG and a generic model of the atria and torso, with average body build and heart orientation. We used the terminal P-wave angle as the variable which describes the direction of the terminal part of P wave in the frontal plane (Figure).

Results
IAB was documented in 413 (42%) subjects: 344 (35%) had pIAB (199 (20%) with pIAB-0; 145 (15%) with pIAB-1) and 60 (6.5%) had aIAB (45 (5%) with aIAB-2 and 15 (1.5%) with aIAB-3 or typical aIAB). P wave morphology could not be categorized by these definitions in 9 subjects. Terminal P wave angle correlated with P wave morphology (Spearman’s r=0.127, p&lt;0.001) and progressively increased with increase of P wave morphology class from pIAB-0 to aIAB-3 (Figure, Table).

Conclusion
Morphology variants of IAB can be classified depending on the degree of the terminal P wave angle which correlates with the order and number of bipolar inferior leads. CineECG provides an instrument for 3D quantification of the terminal atrial depolarization direction which can be used to detect IAB in an earlier stage.Figure.Atrial depolarisarion and IAB.
Comparison of historical criterion and artificial intelligence in patients with left ventricular hypertrophy
Authors: Shimizu M , Misu Y , Tsunoda T , Miyazaki H , Tateishi R , Yamaguchi M , Yamakami Y , Kato N , Shimada H , Isshiki A , Kimura S , Fujii H , Suzuki M , Nishizaki M , Sasano T 
Published: November 2023

Abstract

Backgrounds
Diagnostic criterion of left ventricular hypertrophy (LVH) on 12-leads electrocardiogram (ECG) were historically established. However, there was no study to evaluate the criterion comparing with machine learning methods.

Purpose
To verify the historical criterion of LVH on ECG, and to compare them with diagnosis by machine learning.

Methods
First, consecutive 60 patients with LVH were recruited, and one to one matching with age and sex to patients with normal cardiac function was performed. Finally, 120 patients (69.6 ± 12.6years, 38men per group) were enrolled. LVH was defined as at least one LV wall (septum, posterior wall, apex) showed thickness over 15mm on ultrasound echocardiography. No sinus rhythm, and wide QRS cases were excluded. The accuracy of historical criterion and ECG predictors were calculated by an assessment of whether a predictor was higher/lower than the cut-off value of receivor operating characteristics curve analysis (ROC). Ten machine learning methods were built using the significant predictors of logistic regression with 10-times cross validation.

Results
By logistic regression analysis, 77 significant predictors were extracted, and 22 predictors showed their area under ROC (AUROC) over 0.700. Among historical criterion, Cornell voltage showed high accuracy (0.783) and AUROC (0.808). Conversely, among AI methods, light gradient boosting machine demonstrated higher accuracy (0.843) and random forest method higher AUROC (0.882). Shapley additive explanation method (SHAP) demonstrated that V2/V2 S-wave amplitude and I/V5 T-wave amplitude played essential roles to build the AI models.

Conclusions
AI diagnosis on ECG for LVH showed powerful diagnostic performance comparing historical criterion.
A deep-learning-based retinal cardiovascular disease biomarker and risk of stroke, myocardial infarction, atrial fibrillation, and heart failure in the UK Biobank
Authors: Lee C J , Rim T H , Kang H G , Lee G , Yu M , Tham Y C , Wong T Y , Cheng C Y , Kim S S , Kang S M , Park S 
Published: November 2023

Abstract

Background
Retinal photographs allow a non-invasive way to see the human vasculature and provide insights into cardiovascular disease (CVD). In our previous study, we developed the Reti-CVD, a deep-learning algorithm to predict the future CVD events from retinal photographs.

Purpose
In this study, we extend the application of Reti-CVD by investigating the association between the Reti-CVD score and the occurrence of individual cardiovascular events, including stroke, myocardial infarction (MI), atrial fibrillation (AF), and heart failure (HF).

Methods
The Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. The cumulative incidence of cardiovascular events (stroke, MI, AF, and all-cause HF each) rate was evaluated across the three groups (low, moderate, and high risk) defined by the Reti-CVD score. Cox proportional hazards model was used to estimate the adjusted hazard ratios (aHRs), trends in HRs, and respective p-values were examined by fitting a linear model for the three categories after adjustment of age, gender, diabetes, hypertension, hyperlipidemia, and smoking. C-statistics was used to assess the prognostic value of the Reti-CVD score.

Results
A total of 44,677 participants were included at baseline and tracked for up to 7 years. There were 277 (0.62%) strokes, 506 (1.13%) MIs, 1053 (2.32%) AFs, and 431 (0.94%) HFs. An increase in Reti-CVD score was significantly associated with increased risk of stroke (adjusted hazard ratio [aHR] trends, 1.40; 95% confidence interval [CI], 1.09-1.80, p=0.008), MI (aHR trends, 1.22; 95% CI, 1.01-1.08, p&lt;0.001), and AF (aHR trends, 1.33; 95% CI, 1.17-1.52, p&lt;0.001). However, the association of Reti-CVD score-based three-risk groups and the occurrence of HF showed a trend for increased risk without statistical significance (aHR trends, 1.17; 95% CI, 0.96-1.43, p=0.123). C statistics based on Reti-CVD alone were 0.65 (0.63-0.68) for stroke, 0.64 (0.62-0.66) for MI, 0.66 (0.65-0.67) for AF, and 0.65 (0.60-0.70) for HF.

Conclusion
A deep-learning-based retinal CVD biomarker, the Reti-CVD has the potential to identify individuals with a risk of stroke, MI, and AF, who are likely to benefit from earlier preventative CVD interventions. For the relationship between the Reti-CVD and HF, it seems necessary to analyze by HF subtypes.
ChatGPT takes on the european exam in core cardiology: an AI success story
Authors: Skalidis I , Cagnina A , Luangphiphat O , Muller O , Abbe E , Fournier S 
Published: November 2023

Abstract

Background
ChatGPT, the trending novel artificial intelligence has triggered ongoing debate regarding its capabilities.Recently, preliminary reports showed that answered correctly in the majority of questions of the United States Medical License Examinations (USMLE). However, its ability to succeed a more precise, challenging and high-stakes post-graduate test, such as the final exam for the completion of medical residency , like the European Exam in Core Cardiology (EECC), is not known yet.

Purpose
We sought to evaluate the performance of ChatGPT on EECC, to test its capability on a more demanding, high-stakes post-graduate exam in Cardiology training.

Methods
A total of 488 publicly-available single-answer multiple choice questions (MCQs) were randomly obtained from three different MCQs sources that are traditionally used for the preparation for the EECC: 88 from the sample exam questions released since 2018 from the official ESC website, 200 from the 2022 edition of StudyPRN and 200 from the Braunwald's Heart Disease Review and Assessment (BHDRA). Questions containing audio or visual assets were excluded. After filtering, 362 MCQ items (ESC sample: 68, BHDRA:150, StudyPRN: 144) were included and considered as input source. False responses and indeterminate responses were considered as not correct.

Results
ChatGPT answered to 340 questions out of 362, with 22 indeterminate answers in total. The overall accuracy was 58.8% across all the question sources. More specifically, it demonstrated an accuracy for the ESC sample, BHDRA and StudyPRN of 61.7%, 52.6%, 63.8% respectively. It answered correctly 42/68 (4 indeterminate) of ESC sample questions, 79/150 (11 indeterminate) of the BHDRA and 92/144 (7 indeterminate) of the StudyPRN.

Conclusion
ChatGPT manages to correctly answer the majority of EECC’s questions and perform within the passing threshold range. Although it cannot yet process visual content, it can provide rational and correct answers to text-based inputs in most scenarios. The model may be able to efficiently handle a massive amount of acquired medical knowlededge, but the current approach may not substitue for critical thinkg, innovation and creativity; some of the key attibutes that doctors are expected to showcase.Performance of ChatGPT on EECCExample of MCQ input at ChatGPT
Validation of an artificial intelligence model for 12-lead ECG interpretation
Authors: Demolder A , Herman R , Vavrik B , Martonak M , Boza V , Herman M , Palus T , Kresnakova V , Bahyl J , Iring A , Nelis O , Fabbricatore D , Perl L , Hatala R , Bartunek J 
Published: November 2023

Abstract

Background
The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic tools to assess cardiac abnormalities. However, automated ECG interpretation remains inferior to physician interpretation in terms of accuracy and reliability.

Purpose
This study evaluated the accuracy of an AI-powered ECG model in providing a precise diagnosis of 12-lead ECGs and compared its diagnostic performance to primary care physicians and cardiologists through extensive benchmarking.

Methods
A deep neural network (DNN) was trained on standard 12-lead ECGs to detect 38 diagnoses (grouped into 6 categories: rhythm, conduction abnormalities, chamber enlargement, infarction, ectopy, and axis), denoting the most common types of electrocardiographic abnormalities. Performance of AI-powered ECG diagnosis was evaluated on an independent test set annotated by consensus of two expert cardiologists. Benchmarking was performed against three individual primary care physicians and six individual cardiologists who independently annotated the same ECG test set. The key metrics used to compare performances were positive predictive value (PPV), negative predictive value (NPV), Sensitivity, Specificity, and F1 score.

Results
A total of 931,344 standard 12-lead ECGs from 172,750 patients were used to train a DNN. The independent test set had 11,932 annotated ECG labels. The model attained an overall mean F1 score of 0.921, sensitivity 0.910 (0.889–0.931), specificity 0.968 (0.954–0.981), PPV 0.939 (0.919–0.958), and NPV 0.965 (0.951–0.979) [Figure 1]. In all 6 diagnostic categories, the DNN achieved higher mean F1 scores than the mean cardiologist and primary care physician (Rhythm 0.951 vs. 0.892 vs. 0.734; Conduction abnormalities 0.883 vs. 0.824 vs. 0.693; Chamber enlargement 0.970 vs. 0.761 vs. 0.562; Infarction 0.918 vs. 0.853 vs. 0.781; Ectopy 0.966 vs. 0.951 vs. 0.897; Axis 0.909 vs. 0.644 vs. 0.528, respectively). The ability of the DNN to identify atrial fibrillation achieved nearly perfect performance (PPV of 0.989 and NPV of 0.990). Diagnostic performance surpassed primary care physicians and was non-inferior to cardiologists based on the F1 scores for all individual diagnoses.

Conclusions
Our results demonstrate the AI-powered ECG model’s ability to accurately identify electrocardiographic abnormalities from the 12-lead ECG, showcasing its utility as clinical tool for healthcare professionals.
Impact of P-wave morphology on recurrent arrhythmia characteristics and low-voltage risk score in paroxysmal atrial fibrillation patients underwent catheter ablation
Authors: Yano M , Egami Y , Kawanami S , Sugae H , Ukita K , Kawamura A , Nakamura H , Yasumoto K , Tsuda M , Okamoto N , Matsunaga-Lee Y , Nishino M , Tanouchi J 
Published: November 2023

Abstract

Background
The P-wave morphology’s impact on clinical outcomes post-catheter ablation (CA) and recurrent arrhythmia characteristics or electrophysiological findings in paroxysmal atrial fibrillation (PAF) patients remains unclear.

Purpose
The aim of this study was to elucidate the impact of combination of Pd and Pvm (Pd/Pvm) on the clinical outcomes after CA and recurrent characteristic or electrophysiological findings in repeated ablation.

Methods
PAF patients who underwent CA in the Osaka Rosai Atrial Fibrillation ablation (ORAF) registry were enrolled. P-wave duration and P-wave vector magnitude (the square root of the sum of the squared P-wave amplitude in leads II, V6 and one-half of the P-wave amplitude in V2) were measured. We divided the patients into two groups; patients with high and low Pd/Pvm based on the cut-off value calculated by statistical methods. We evaluated (1) late recurrence of atrial fibrillation (AF) / atrial tachycardia (AT) (LRAF) (2) clinical risk score for low-voltage area (DR-FLASH score) and (3) recurrent characteristics and electrophysiological findings in repeated ablation between the two groups.

Results
This study included 1005 PAF patients. The median follow-up duration was 722 [288, 1119] days. LRAF occurred in 251 patients (25.0%). A cut-off value of Pd/Pvm=699.1ms/mV gave AUC=0.87, sensitivity=72.9%, specificity=85.3%. High Pd/Pvm had a greater risk of LRAF than low Pd/Pvm (p&lt;0.001). The ratio of the patients with DR-FLASH score&gt;3 was significantly higher in those with high Pd/Pvm than low. The ratio of AT recurrence was significantly higher in the patients with high Pd/Pvm than in those with low Pd/Pvm (32.4% vs 18.8%, p=0.034). The ratio of AT occurring during repeated ablation and reproducible frequently occurring atrial premature contractions or AF triggers in left atrium (LA) were significantly higher in the patients with high Pd/Pvm than low Pd/Pvm.

Conclusion
Combination of P-wave morphology, Pd/Pvm, was significantly associated with LRAF. Pd/Pvm was significantly correlated with clinical risk score for predicting LA low-voltage area and incidence of AT and LA-origin AF triggers.
Humanized interactive digital cardiology training
Authors: Canesin M , Furtado F , Goncalves R , Carraro D , Uemura L , Fuganti C , Rodrigues R , Mesas C 
Published: November 2023

Abstract

Background
The consolidation of new digital educational cardiology paradigms requires the implementation of innovative strategies to transform students into competent professionals.

Objectives
To assess cardiology knowledge and satisfaction of medical students before and after the use of a new active method with a humanized interactive clinical cases digital platform, called virtual case-based learning (VCBL).

Methods
This was a prospective study of a cardiology teaching-learning process for medical students. A 40 cardiology multiple choice questions test (in five cardiology topics) and a satisfaction evaluation questionnaires were applied in 2018 (87 students) and 2019 (89 students). The new VCBL method was compared with the traditional cardiology active methodology PBL (problem-based learning) (Picture 1). Descriptive and association analyses were made using the Statistical Package for the Social Sciences.

Results
A total of 167 evaluation forms were administered to medical students and analyzed. In the evaluation of the cardiology cognitive 40 multiple choice question test, the 2018 (PBL) and the 2019 (VCBL) students groups achieve a mean score of 41.7 and 73.3 points (Picture 2), respectively (p&lt;0.001). Among the students who responded to the satisfaction evaluation form, 76.0% gave the highest rating to the question if this methodology improves a better training to the traditional PBL in their perception . Nearly 70.0% of students positively evaluated the knowledge acquisition with this new clinical cases humanized virtual model; 78.0% reported to feel prepared for working in outpatient care; and 94.0% positively evaluated the new methodology.

Conclusion
In this initial study, the results indicate that new digital options are needed for cardiology training. The new active method of humanized interactive clinical cases digital platform, using VCBL method, may help the cardiology teaching-learning process, promoting higher cardiology knowledge and satisfaction by the students and could also be tested for professionals cardiology training.Flowchart of the learning models’ stepsComparison of Students Cardiology Scores
Inappropriate NT-proBNP short-term repeat testing: effectiveness of an electronic alert
Authors: Miranda I , Fialho I , Augusto J , Nabais J , Sardinha T , Azevedo D , Santos M 
Published: November 2023

Abstract

Background
NT-proBNP is a costly laboratory marker commonly employed as a diagnostic and prognostic biomarker in heart failure (HF). While it may be helpful during admission and discharge for patients with acute decompensated HF (ADHF), it may not be appropriate to measure it repeatedly over a short period of time due to the associated unnecessary expenses.

Objective
To determine the effectiveness of an electronic alert on reducing the prevalence of inappropriate use of NT-proBNP and estimate the potential cost savings.

Methods
We introduced an electronic alert on the 25th Nov 2022 that required clinicians to provide a clinical justification for ordering NT-proBNP within 5 days of a previous test result. The alert could easily be dismissed with a single keystroke. We assessed the percentage of potentially unnecessary testing (i.e. repeat testing within less than 5 days) during the intervention period (duration of 1 month, from 25th Nov to 15th Dec 2022) and compared it to two control periods: the 2 months previous to the intervention, from 1st Oct to 24th Nov 2022 (control period A), and one year before the intervention, from 25th Nov-15th Dec 2021 (control period B). The cost of each test was estimated at €29.60.

Results
During the intervention period, the total number (n) of NTproBNP tests ordered and the percentage (%) of repeat testing in less than 5 days was n=1022 and 20%, respectively. During the control period A, the numbers were n=2543 and 22%, respectively. During the control period B, these values were n=840 and 20%, respectively. The percentage of potential inappropriate testing was not significantly different when comparing the intervention period to the control period A (p=0.74) and control period B (p=0.48). The total number of NT-proBNP tests per year is around 11 000. A strategy that would reduce potential 20% inappropriate tests would save €65 120 per year.

Conclusion
Around 20% of NT-proBNP tests are ordered within 5 days or less of a previous test. An electronic alert prompting doctors to type a clinical justification for the NT-proBNP whenever a result was available in the previous 5 days is not effective. An effective intervention to reduce inappropriate testing could save over €65 000 per year in a moderate dimension hospital. More effective strategies are necessary.