bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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<0.001), and AF (aHR trends, 1.33; 95% CI, 1.17-1.52, p<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

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

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

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<0.001). The ratio of the patients with DR-FLASH score>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

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<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

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.
Effect of telemedicine on self-care in heart failure patients: insights from the iCOR randomised controlled trial
Authors: Pons Riverola A , Yun S , Jose N , Hidalgo E , Ruiz M , Alcoberro L , Jimenez S , Moliner P , Ramos R , Ras M , Verdu J M , Garay A , Corbella M , Enjuanes C , Comin-Colet J
Published: November 2023

Background/introduction

Poor self-care in heart failure (HF) patients is well recognised to lead to a worse prognosis, through an increased risk of readmissions and mortality. In this regard, the integration of eHealth-based telemonitoring and tele-intervention tools into integrated HF-care programmes could help to improve the self-care of these patients. Nevertheless, the current evidence regarding the impact of eHealth solutions on the self-care of HF patients is scarce.

Purpose

To evaluate the effect of telemedicine (TM) on self-care behaviour in patients with HF after hospital admission compared to usual care (UC).

Methods

We designed a post-hoc analysis of the "Insuficiència Cardíaca Optimització Remota" (iCOR) study focussing on self-care. iCOR was a randomised, controlled, single-centre proof-of-concept clinical trial, designed to evaluate the efficacy to adding a eHealth-solution to an existing specialised, comprehensive, multidisciplinary, nurse-based, hospital/primary care integrated HF management programme. Participants were randomised to TM or UC after hospital discharge ("vulnerable-phase") and were followed for 6 months. The primary study endpoint of the current analysis was the delta change of self-care after 6 months of follow-up, assessed using European Heart Failure Self-care Behaviour Scale-12 item (EHFScBS-12 item, lower scores meaning better self-care). To assess the change in self-care between the 2 groups, a repeated-measures ANOVA analysis was performed, adjusted for baseline self-care.

Results

178 patients were analysed (81 TM vs 97 UC). The median age of the patients was 74±11 years, 41% were female and the median ejection fraction was 47±17%. Self-care at baseline had a median score of 21.6±10.5 points.

As shown in Figure 1, there were no differences in baseline values of self-care between the two comparison groups (21.9±11 TM vs 21.5±11 UC, p-value > 0.05). Both groups experienced an improvement in self-care over time (p-value 0.002 for time effect). However, patients in the TM group experienced greater improvement (from 21.9±11 to 14±0.7 mean Delta change = -7.9±11) than in the UC (21.3±10.5 to 19±0.7, mean delta change = -2.2±10.4, p-value <0.001 in inter-groups comparison) (Figure 1). We designed several multivariate linear regression models exploring the determinants of self-care at baseline and the determinants of change in self-care over time. As shown in Table 1, baseline self-care and randomisation arm were the only independent predictors of improvement of self-care over time.

Conclusions

Integration of TM into HF care programs can contribute to further improve self-care and thus empowerment in the HF-management.Self-care status changeDeterminants of self-care
Prevalence of sleep apnea by a home diagnostic device in patients with atrial fibrillation
Authors: Hashiba Jensen M , Dalgaard F D , Laub R R L , Gottlieb V G , Nielsen O W N , Hansen J H , Lock Hansen M L H , Jennum P J , Lamberts M L
Published: November 2023

Background

Sleep apnea (SA), a modifiable risk factor in atrial fibrillation (AF), is associated with worse outcomes in AF hence diagnosing SA in patients with AF is clinically important. The gold standard to detect presence of SA is polysomnography which requires an in-patient, overnight stay in a specialized sleep laboratory. The procedure is time-consuming and inconvenient for patients and not widely available. As such, new and more conveniently assessment tools are needed for optimal screening of SA in patients with AF.

Purpose

We aimed to assess the prevalence and severity of SA in patients with AF, and, subsequently, to assess the positive predictive value (PPV) of moderate to severe and only severe SA by home-monitoring in comparison to cardio-respiratory monitoring (CRM) in consecutive patients with AF.

Methods

This cross-sectional study recruited unselected patients with AF without known SA from an out-patient clinic at our University Hospital. Participants underwent four consecutive nights of sleep-recording with the home-monitoring device NightOwl™ (NO). Moderate to severe SA was defined as an Apnea-Hypopnea Index≥15 per hour of sleep. Participants with moderate to severe SA was offered CRM for validation of the diagnosis. Prevalence of SA was calculated by the proportion of participants with moderate to severe SA out of all study participants, and the PPV was calculated as the proportion of moderate to severe and only severe AHI by NO confirmed by CRM.

Results

We included 126 patients with AF with a median age of 68 (interquartile range: 60-75) years, 42 (33%) women, 70 (56%) hypertension, 61 (48%) hyperlipidemia and 49 (39%) heart failure. NO detected severe SA in 36 (29%) of patients with AF, moderate SA in 35 (28%), mild SA in 45 (36%) and no SA in 10 (8%). Of the 71 patients with moderate to severe SA by NO, 38 patients underwent CRM and the PPV of NO was 0.82 (31/38) to diagnose moderate and severe SA and 0.92 (22/24) to diagnose severe SA by CRM.

Conclusion

Moderate to severe SA by NO was highly prevalent in patients with AF without known SA. A home-monitoring device such as NO could be an easy and feasible SA screening tool in patients with AF.Figure 1:PrevalenceTable 1:Baseline Characteristics
Prediction of all-cause mortality using a multisensor implantable defibrillator algorithm for HF monitoring
Authors: D'onofrio A , Manzo M , Bertini M , Santini L , Savarese G , Dello Russo A , Santobuono V E , Lavalle C , Viscusi M , Amellone C , Calvanese R , Santoro A , Ziacchi M , Valsecchi S , Calo L
Published: November 2023

Background

The HeartLogic algorithm combines multiple implantable defibrillator (ICD) sensor data and has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation.

Purpose

We determined if remotely monitored data from this algorithm can be used to identify patients at high risk of mortality.

Methods

The HeartLogic feature was activated in 568 ICD patients from 26 centers.

Results

During a median follow-up of 26 months [25th–75th percentile: 16-37], 1200 HeartLogic alerts were recorded in 370 (65%) patients. Overall, the time IN the alert state was 13% of the total observation period (151 out of 1159 years) and 20% of the follow-up period of the 370 patients with alerts. During follow-up, 55 patients died (37 in the group with alerts). Experiencing any alert episode was associated with a substantially increased risk of death [hazard ratio (HR): 2.08, 95% confidence interval (CI): 1.16–3.73, P = 0.039]. Additionally, a time IN alert ≥20% was associated with death (HR: 4.07, 95%CI: 2.19-7.54, p<0.001, Figure), even after multivariate correction for age, atrial fibrillation on implantation, chronic kidney disease, ischemic cardiomyopathy (HR: 3.26, 95%CI:1.87-5.70, p<0.001). The rate of death was 0.25/patient-year (95%CI: 0.17-0.34) with the HeartLogic IN the alert state and 0.02/patient-year (95%CI: 0.01-0.03) OUT of the alert state, with an incidence rate ratio of 13.72 (95%CI: 7.62-25.60, p<0.001).

Conclusions

The HeartLogic algorithm provides an index that can be used to identify patients at higher risk of all-cause mortality. The index status identifies periods of significantly increased risk of death.
The TAG P2 score predicts low voltage areas in patients with atrial fibrillation - a novel prediction model
Authors: Dilk P , Marinov K , Nedios S , Dagres N , Bollmann A , Hindricks G , Seewoester T
Published: November 2023

Background

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with morbidity and mortality. One of the mainstays of AF treatment is the interventional isolation of the pulmonary veins. However, the presence of low voltage areas (LVAs) in the left atrium is associated with a worse outcome after catheter ablation. Therefore, identifying LVAs prior to intervention is important for tailoring individualized treatment approaches. Although several prediction models for LVAs exist, none of them incorporate electrocardiographical parameters. Therefore, this study aimed to develop and evaluate the predictive value of a novel LVAs prediction score that incorporates a novel ECG parameter, prospectively validate the score, and compare it with existing prediction scores.

Methods

The study enrolled 264 patients with AF undergoing catheter ablation. LVAs were determined using high-density maps, which were defined as areas of low voltage amplitude (<0.5 mV). Before intervention, all patients underwent cardiovascular magnetic resonance imaging to measure left atrial dimensions. A novel LVAs prediction score (TAG P²) was established, which included a parameter of cumulative atrial electrical forces in relation to left atrial area (PWAi<0.478 msec * mV * m²/cm² – 2pts), along with type of AF (persistent – 1pt.), age (>70 years - apt.), and gender (female – 1pt.). The score was prospectively validated in a cohort of 126 patients.

Results

The TAG P² score was effective in predicting LVAs in the study population (AUC: 0.918; 95%CI [0.884 to 0.951]; p<0.001) and the results were confirmed in the validation cohort (AUC: 0.884; 95%CI [0.826 to 0.943]; p<0.001). The TAG P² score outperformed existing prediction scores, such as the DR-FLASH (AUC: 0.788; 95%CI [0.685 to 0.891]) and APPLE scores (AUC: 0.764; 95%CI [0.671 to 0.856]).

Conclusion

The TAG P² score, which incorporates a novel ECG parameter (PWAi) effectively predicts LVAs in patients undergoing catheter ablation for AF.
Diagnostic accuracy of a smartphone application for artificial intelligence-based interpretation of 12-lead ECG in primary care (AMSTELHEART-1)
Authors: Himmelreich J C L , Harskamp R E
Published: November 2023

Background

The use of 12-lead electrocardiogram (ECG) is common in routine primary care, however it can be difficult for less experienced ECG readers to adequately interpret the ECG.

Objective

To validate a novel smartphone application as a stand-alone interpretation tool for 12-lead ECG in primary care.

Methods

We recruited consecutive patients who underwent 12-lead ECG as part of routinely indicated primary care in the Netherlands. All ECGs were assessed by a smartphone application which analyses a photographed image of 12-lead ECG for automated interpretation, installed on an Android platform and an iOS platform. We validated the application for detecting: 1) any major ECG abnormality (MEA, primary outcome) defined as atrial fibrillation/flutter (AF), markers of (past) myocardial ischemia or clinically relevant impulse and/or conduction abnormalities; or 2) AF (key secondary outcome). The reference standard was a blinded expert panel.

Results

We included 290 patients from 11 Dutch general practices with median age 67 (IQR 55-74) years, 48% were female. On reference ECG, 71 patients (25%) had MEA, 35 (12%) had AF. The app's sensitivity and specificity for MEA were 86% (95%CI:76-93) and 92% (95%CI:87-95), respectively. For AF, sensitivity and specificity were 97% (95%CI:85-100) and 99% (95%CI:97-100), respectively. Performance was comparable between Android and iOS platform (Kappa=0.95, 95%CI:0.91-0.99 and Kappa=1.00, 95%CI:1.00-1.00 for MEA and AF, respectively).

Conclusions

A smartphone application that interprets photographed 12-lead ECG images had good diagnostic accuracy in a primary care setting for major ECG abnormalities, and near-perfect properties for diagnosing AF.
Self-monitoring using Mobile App to improve symptoms and Reduce rehospitalization and morTality in Heart Failure (SMART-HF trial)i
Authors: Yoon M , Park J J , Lee S H , Jung M H , Youn J C , Shim C Y , Choi J O , Choi J Y , Kim E J , Yoo B S , Son Y J , Choi D J
Published: November 2023

Background

Considering the uncertainty of the benefits of mobile apps in patients with heart failure (HF), we developed a mobile health platform and aimed to evaluate this intervention in terms of improving symptoms and outcomes in HF.

Methods

Seven tertiary hospitals in South Korea participated in this prospective, randomized, open-label, nationwide, multicenter, pilot trial (SMART-HF trial). Patients aged ≥20 years hospitalized for acute heart failure who were able to use a smartphone were enrolled before discharge. Eligible participants were then randomized 1:1 to either the intervention group (AIDA/KT-app with devices) or the control group (AIDA/KT-app only). The mobile health platform consists of three parts: 1) smartphone app for patients (2) dashboard system for physicians (3) clinical decision support system. Only in the intervention group, the applications were automatically paired with a Bluetooth blood pressure monitor, weight scale and body water analyzer. The intervention group were able to enter their HF symptom scale into the app and receive feedback or alerts on their input. In the control group, patients could only enter their blood pressure, weight, and HF symptom into the app, and there are no connected devices and no feedback from the app.

Results

84 patients were randomized to the intervention group (n=43) and control group (n=41). Overall, 62.8% of the patients were male, and the median age was 63.0 years. At 4 weeks, dyspnea score by questionnaire (range 0-10) was 1.5±2.3, and 2.0±2.4 in the intervention and control groups, respectively, and was not significantly different (P=0.415). The total HF symptom score (range 0-12) including dyspnea, fatigue, edema, and palpitation entered by the participants in the apps was not significantly different between two groups (2.0±2.1, and 1.6±2.1, P=0.454). In the intervention group, the level of body water composition showed trend toward decreasing during study period (initial level 7.46 -> final level 6.71, P=0.10). App adherence during the study period was higher in the intervention group compared with control group (70.6±32.5% vs. 47.8±32.0%, P=0.004). App satisfaction scores tended to be higher in the intervention group (7.1±2.6 vs. 6.2±2.9, P=0.147). Secondary composite endpoints including death, rehospitalization, and urgent HF visit were not significantly different between two groups (4/40 vs. 3/38, P=0.999).

Conclusions

Our mobile health platform showed feasibility and efficacy for HF patients although clinical outcomes were not significantly different, maybe due to small sample size. Considering that motivating patients to use the mHealth tool more frequently may lead to greater clinical benefit, further large research with a positive user experience is needed for HF patient care.
Association between empathy levels in physicians and patient satisfaction scores in a cardiovascular clinic
Authors: Sigal A , Baro Vila R C , Massa M , Benzadon M , Gelpi R J , Alves De Lima A E
Published: November 2023

Introduction

A good doctor-patient relationship is crucial to medical practice. A fundamental element that drives this relationship is the empathy of the treating physician, which has also been validated as a predictor of clinical competence and patient results.

Purpose

We conducted an original study to correlate empathy values of our physicians and patient satisfaction surveys. In this subanalysis, we evaluated whether age, sex or time since qualifying as specialist had an impact on empathy values.

Methods

In our originaly study we sought to correlate the empathy values of our physicians in our cardiovascular clinic with the results of outpatient satisfaction surveys, measured with HCAPS, using a linear regression model. The Jefferson Empathy scale is a self reported 20 question survey, answered through a 1-7 Likert scale, with a maximum score of 140 points. It is further subclassified into 3 categories: "Perspective", "Care with compassion" and "Stepping into patient's shoes". The HCAPS survey is a patient satisfaction query, with 5 items that inquire as to the patient's perception of being treated with respect by the physician, the interest shown in their problems, the time dedicated to the consult, and the quality of the explanation of their chief concern and treatment options. It is anwered through a Likert scale from "Totally diasgree" to "Totally agree". There is also a final question that asks for a global impression.

Results

65 doctors answered the Empathy survey, 30.8% of them women. Mean age was 48.8 years, and over half were under 50 years old. 81% had been practicing for over 5 years. 61671 HCAPS surveys were analyzed, corresponding to those 65 practitioners. Mean Empathy score was 120.7 (DS 11.1). We found that patients perceived greater respectful treatment (p=0.03) and had their treatment options better explained to them (p=0.02) by physicians with higher levels of empathy. The other HCAPS queries had a positive, though non statistically significant, relationship with Empathy scores. Doctors with higher scores in the subcategory "stepping into patient's shoes" had a significant increase in patient's perception of the interest shown in their concerns (p=0.01). There were no differences in physicians' empathy levels according to their age, gender, or time since qualifying as a specialist.

Discussion

These results validate empathy as a key skill in the doctor-patient relationship, and show that high levels of this personality attribute lead to better patient satisfaction results. Our results differ from other published work in that we found no difference in empathy levels according to gender or practice time, whereas most previous literature found that women and longer practice time were positively associated with empathy levels.
Knowledge and unmet needs of patients with atrial fibrillation in China: a nationwide cross-sectional survey
Authors: Jiang C X , Liu X X , Yuan L , Yuan H T , Zhu H M , Li N , Xu J , Long D Y , Chen M L , Ma C S
Published: November 2023

Background

Knowledge about the disease and treatment options is necessary for active patient engagement in shared decision-making, which is essential for ’patient-centered’ atrial fibrillation (AF) management.

Purpose

To evaluate the knowledge level and identify unmet needs of patients with AF in China.

Methods

A nationwide cross-sectional survey was conducted using online or paper questionnaires between May and August 2022. Adult AF outpatients or inpatients in China were eligible for participation. The questionnaires included items on knowledge regarding AF, treatment options, risk factors, AF treatment practice, and AF patients’ unmet needs.

Results

This survey included 2,002 patients with AF [mean age 68.62±13.45, 1,040 (52%) men] from 55 centers across 17 provinces in China. Most patients (66.18%) had knowledge scores of < 4 (an 8-item disease knowledge scale, the score ranged from 0 [worst] to 8 [best]), which is a poor knowledge level. Approximately half of the patients did not know that "AF can cause blood clots which can lead to stroke (cerebral infarction)." The proportion of patients who knew about oral anticoagulants, cardiac rhythm/heart rate control drugs, and catheter ablation were 85.96%, 81.37%, and 41.36%, respectively. However, only 79.52%, 72.08%, and 16.18% of patients received these respective treatments. Moreover, only 51.46% of patients were satisfied or very satisfied with their treatments. The improvement in life quality, prevention and treatment of complications, and symptom control were the three most important aspects for patients when choosing treatments. The impact of AF on daily activities, sleep, emotions, and memory were reported by 80.77%, 63.54%, 52.80%, and 66.18% patients, respectively. Most patients with AF recognized hypertension (87.50%) and diabetes mellitus (50.33%) as risk factors, but few recognized obesity and excessive alcohol intake. Overall, 93.56% of patients obtained AF knowledge from physicians. They expect the source of knowledge to include regular community education(61.29%), as well as digital tools(58.74%) between physicians and patients. Furthermore, 90.86% of patients considered involvement in treatment decisions necessary.

Conclusions

This study reveals that the knowledge level of patients with AF in China regarding the disease and treatment options is insufficient, and there remain unmet needs for AF management. Thus, more patient-centered education through multiple channels is required to improve patients' involvement in treatment decisions.
Association between psychological distress and cardiovascular health amongst cancer survivors in the United States: findings from nationally representative data
Authors: Chan J S K , Satti D I , Dee E C , Sharma G , Virani S S , Liu T , Tse G
Published: November 2023

Background

Although both psychological distress and cancer are associated with worse cardiovascular health, the associations between psychological distress and cardiovascular health in cancer survivors are unclear and requires delineation, especially as the number of long-term cancer survivors increases.

Purpose

To investigate the relationship between psychological distress and cardiovascular health amongst cancer survivors.

Methods

This cross-sectional study used data from the 2013-2017 National Health Interview Survey, an annual survey representative of the non-institutionalized population of the United States. Subjects with missing data for any of the exposure, outcome, or covariates were excluded. Psychological distress was measured by the six-item Kessler scale (K6), and severe psychological distress (SPD) was defined as K6 ≥13. Cardiovascular health (CVH) was quantified based on the American Heart Association’s Life’s Essential Eight. As dietary data was unavailable, the score included seven binary items (hypertension, diabetes mellitus, hypercholesterolemia, smoking, physical activity, inadequate sleep, and obesity), with higher scores indicating worse CVH. Multivariable Poisson regression was used to test associations between SPD and CVH, adjusting for the presence of any known cardiac condition, family income, education level, race, sex, sexual orientation, insurance coverage, and age.

Results

Altogether, 138,001 subjects (weighted population of 203,223,831) were analysed, of whom 13,485 (weighted population of 17,648,471) were cancer survivors. SPD was independently associated with worse cardiovascular health both in cancer survivors (adjusted risk ratio (aRR) 1.24 [95% confidence interval: 1.19, 1.29], p<0.001) and those without known cancer (aRR 1.41 [1.39, 1.44], p<0.001), but the association was weaker in the former (pinteraction=0.001). The relationships were grossly linear in both groups of patients (Figure 1). Amongst cancer survivors, SPD was independently associated with all components of the cardiovascular health score except obesity (all p<0.05). Furthermore, amongst cancer survivors, the association was stronger in those who were younger (pinteraction<0.05) or female (pinteraction=0.014), but did not differ significantly by family income, race, or the presence of known cardiac conditions (Figure 2). The association remained significant for most cancer sites.

Conclusions and relevance

SPD was associated with worse cardiovascular health amongst cancer survivors, especially young, female patients, although the association was weaker than that in non-cancer subjects. These highlighted the importance of a multidisciplinary approach with careful psychological assessments in the care of cancer survivors, while shedding light on the patients who may benefit most from future quality improvement programs.Figure 1Figure 2
5-lead 3D-vectorcardiography differentiates between high and low cardiovascular risk profiles in patients with suspected or know coronary heart disease
Authors: Schmidt-Lucke C , Kohl S , Kammeier A , Knobl H , Burchert W , Schmidt-Lucke J A , Lischke B , Lindner O
Published: November 2023

5-lead 3D-vectorcardiography (3D-VCG), including a dorsal electrode with ensemble of artificial neural networks (ANN) offers additional information over standard 12-lead electrocardiography (ECG) in the detection of cardiac ischaemia. Artificial Intelligence (AI) and access to a massive global clinical data repository have the potential to boost the performance of VCG beyond conventional techniques. VCG offers ease-of-use at rest and is therefore a promising candidate for becoming a new screening tool for coronary artery diseases (CAD). We, thus, tested the hypothesis of 3D-VCG being able to differentiate between patients with lower and high risk of cardiovascular disease (CVD) and identify those requiring coronary interventions.

In this exploratory study, we analysed patients in a national multicentre trial. Patients were recruited from general cardiologists’ praxis and radiologist centre with patients referred for further diagnosis of suspected or confirmed CVD. Based on the Perfusion (P)-Factor of the 5-lead 3D- vectorcardiography, including 731 parameters, e.g., QRS-T angle and in-house features calculated in time and frequency domains, such as beat moments a P-Index is calculated. Based on the P-Index, patients were either classified as high, medium, or low risk for CVD (medium + high defined as high CVD-risk). Confirmation of CAD was performed according to the practitioners’ discretion blinded to the P-Index. Number of risk factors (mod. PROCAM score) were compared between the high- and low-risk group using an independent t-test.

The 468 patients in this sample (m:w 61:39, age: 66 [40-87] years) showed 3.6 [0 - 7] CVRF, 16% had arrhythmias or conduction disturbances (AF, PM, BBB), 24% pts. had consecutive PCI or CABG. 62% were classified as low while 38% were classified as high risk for CVD by the P-Index. Number of cardiovascular risk factors was significantly higher in the high-risk CVD-group as defined by P-Index compared to low CVD-risk (4.0 [3.0 – 5.0] vs. 3.5 [2.0 – 4.0], p < 0.05). P-Index differentiated between suspected CVD with or without consequent PCI or CABG (Chi2 = 4.02, p<0.05).

The AI-based 3D-VCG is an innovative diagnostic tool that can help determine a patient's cardiovascular risk profile in resting condition for clinical and research purposes. Furthermore, the P-Index identified those with significant stenoses in need for coronary intervention and may thus be a further scalable screening method to differentiate patients with consequent interventional approach or need for risk modification. These preliminary data will have to be confirmed in a prospective, large-scale performance study to verify the diagnostic accuracy.
The role of heart rate variability in predicting the response to beta blocker in patients with frequent premature ventricular contractions
Authors: Chun K J , Seo J H , Ryu D R , Lee B K , Cho B R
Published: November 2023

Background

Frequent premature ventricular contractions (PVCs) in patients with structurally normal heart are not uncommon. Beta blocker is generally used to reduce symptom and burden of PVCs, but the response to beta blockers vary from patient to patient.

Purpose

This study aimed to investigate whether the heart rate variability (HRV) values could predict the response to beta blockers in patients with frequent PVCs.

Methods

We analyzed the patients who underwent 24-hour Holter monitoring and had PVC burden >3% in baseline 24-hour Holter monitoring. Bisoprolol and nebivolol was used to reduce symptom and burden of PVCs. The response to beta blocker was defined as the reduction of PVCs burden more than 50% in follow-up Holter monitoring. Baseline HRV was measured by time domain and frequency domain methods using 24-hour Holter monitoring.

Results

A total of 125 patients (mean age, 52.1 ± 14.7 years; male, 38.3%) underwent repeated 24-hour Holter monitoring were finally analyzed. Mean follow-up duration between the Holter monitoring was 205 ± 200 days. Among 125 patients with frequent PVCs, 45 patients had response to beta blockers. The ratio of low-frequency (LF) and high-frequency (HF) was higher in response group than those with non-response to beta blocker (P=0.016). LF, HF, and other variables measured by time-domain method were not associated with response to beta blocker.

Conclusion

The HRV value, especially LF/HF ratio, could predict the response to beta blockers in patients with idiopathic frequent PVCs.
Quantity and quality of shared decision making incorporation across major international cardiology guidelines
Authors: Macdonald B J , Turgeon R D
Published: November 2023

Background/Introduction

Shared decision-making (SDM) is a key component of the provision of ethical care, but prior reviews have indicated that clinical practice guidelines poorly promote and facilitate SDM.

Purpose

To identify and characterize all pharmacotherapy recommendations integrating SDM within contemporary cardiovascular guidelines using a novel classification system.

Methods

The American College of Cardiology (ACC), Canadian Cardiovascular Society (CCS), and European Society of Cardiology (ESC) digital guideline libraries (2012–2022) were searched for current guidelines, or subsequent updates, that included at least one pharmacotherapy recommendation. We identified all pharmacotherapy recommendations for each guideline. Recommendations that incorporated SDM were rated according to a novel Supporting Higher Patient Autonomy in Recommendations (SHARE) rating framework – which evaluates the quality of SDM incorporation based on directness (1-3; assessing whether SDM was incorporated directly and impartially into the recommendation’s text) and facilitation (A-D; assessing whether decision aids or quantified benefits/harms were provided).

Results

From 65 guidelines and updates, 2,655 recommendations (35% of total) addressed pharmacotherapy. Of these recommendations, 171 (6%) incorporated SDM. By category, general cardiology guidelines contained the highest proportion of pharmacotherapy recommendations incorporating SDM (10%), whereas heart failure and myocardial disease contained the least (3%). The proportion of pharmacotherapy recommendations incorporating SDM was comparable across societies (ACC: 8%, CCS: 9%, ESC: 5%), with no trend for change over time. Only 5/171 (3%) of SDM recommendations met SHARE grade 1A (impartial recommendations for SDM supported by a decision aid), whereas 63% were SHARE grade 3D (SDM mentioned only in supporting text and without any tools or information to facilitate SDM).

Conclusions

Across guidelines published by three cardiovascular societies over the last decade, few recommendations incorporated SDM in any form, and even fewer adequately facilitated SDM. Guidelines should move away from merely advocating for consideration of patient preferences, and toward facilitating meaningful engagement in SDM.