European heart journal. Digital health最新文献

Aims: To assess the performance of an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm in identifying patients with moderate to severe aortic stenosis (AS) in an Asian cohort from a tertiary care centre.

Methods and results: We identified a randomly selected patients ≥60 years old who underwent echocardiography and ECG within in 31 days between 2012 and 2021 at the Samsung Medical Center in Korea. Patients with previous cardiac surgery, prosthetic valves, or pacemakers were excluded. The AI-ECG model, originally developed and validated by Mayo Clinic in the USA, was applied without fine-tuning. Performance metrics, including the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were calculated to compare AI-ECG predictions with TTE-confirmed AS status. Among 5425 patients, 1095 had moderate to severe AS, and 4330 age- and sex-matched patients without AS were included as controls. The AI-ECG model achieved an AUC of 0.85 (95% CI: 0.84-0.87) in detecting moderate to severe AS. Sensitivity, specificity, PPV, NPV, and accuracy were 0.83, 0.65, 0.37, 0.94, and 68.29%, respectively. The model's performance was consistent across various age and sex subgroups, with sensitivity increasing in older patients.

Conclusion: The AI-ECG model developed in the USA demonstrated comparable performance in detecting moderate to severe AS in an Asian cohort compared with its original validation population. These findings highlight the potential utility of AI-ECG as a non-invasive screening tool for AS across diverse patient populations.

Aims: Assessing myocardial fibrosis (MF) in patients with prior myocardial infarction (MI) is crucial for prognosis. Artificial intelligence-assisted electrocardiography (AI-ECG) has a great potential to detect MF. However, training a precise AI-ECG model requires voluminous ECGs. A biosimulation model may be an efficient substitution. This study aimed to develop and validate a novel artificial intelligence-assisted method using 12-lead electrocardiography (AI-MI-12ECG).

Methods and results: The AI-MI-12ECG was trained by a biosimulation model to visualize the presence, location, and size of MF in post-MI patients. A total of 182 post-MI patients were included in this prospective study. The MF detected by AI-MI-12ECG and the cardiologist were compared with the late gadolinium-enhanced (LGE) area of cardiac magnetic resonance (CMR). The results show that AI-MI-12ECG exhibited strong correlation with LGE in identifying the MI location (R = 0.955). Compared with CMR-LGE, AI-MI-12ECG achieved receiver operating characteristic curves of 0.95, 0.95, and 0.89 for left anterior descending coronary artery (LAD), right coronary artery (RCA), and left circumflex coronary artery (LCX) territories, respectively, with high accuracies for LAD (0.95), RCA (0.97), and LCX (0.91).

Conclusion: The AI-MI-12ECG trained using the biosimulation model in post-MI patients was adequately aligned with CMR-LGE. This highlights its potential for accurate detection of fibrosis and identification of individuals with significant infarct burdens.

Aims: Personalized risk assessment tools (PRTs) are recommended by cardiovascular guidelines to tailor prevention, diagnosis, and treatment. However, PRT implementation in clinical routine is poor. ACRIBiS (Advancing Cardiovascular Risk Identification with Structured Clinical Documentation and Biosignal Derived Phenotypes Synthesis) aims to establish interoperable infrastructures for standardized documentation of routine data and integration of high-resolution biosignals (HRBs) enabling data-based risk assessment.

Methods and results: Established cardiovascular risk scores were selected by their predictive performance and served as basis for building a core cardiovascular dataset with risk-relevant clinical routine information. Data items not yet represented in the Medical Informatics Inititative (MII) Core Dataset (CDS) FHIR profiles will be added to an extension module 'Cardiology' allowing for maximum interoperability. HRB integration will be implemented at each site through a modular infrastructure for electrocardiography (ECG) processing. Predictive performance of PRTs and their dynamic recalibration through HRB integration will be evaluated within the ACRIBiS cohort consisting of 5250 prospectively recruited patients at 15 German academic cardiology departments with 12-month follow-up. The potential of visualising these risks to improve patient education will also be assessed and supported by the development of a self-assessment app.

Discussion: The ACRIBiS project presents an innovative concept to harmonize clinical data documentation and integrate ECG data, ultimately facilitating personalized risk assessment to improve patient empowerment and prognosis. Importantly, the consensus-based documentation and interoperability specifications developed will support the standardisation of routine patient data collection at the national and international levels, while the ACRIBiS cohort dataset will be available for broad secondary use.

Trial registration: The study is registered at the German study registry (DRKS): #DRKS00034792.

Aims: Syncope remains a diagnostic challenge despite advancements in testing and treatment. Cardiac syncope is an independent predictor of mortality and can be difficult to distinguish from other causes of transient loss of consciousness (TLOC). This paper explores whether artificial intelligence (AI) can improve the evaluation and management of patients with syncope.

Methods and results: We conducted a literature review and incorporated the opinions of experts in the fields of syncope and AI. The cause of TLOC is often unclear, hospitalization criteria are ambiguous, diagnostic tests are frequently non-informative, and assessments are costly. Patients are left with unanswered questions and limited guidance. Artificial intelligence (AI) has the potential to optimize syncope evaluation by processing large data sets, detecting imperceptible patterns, and assisting clinicians. However, AI has limitations, including errors, lack of human empathy, and uncertain clinical utility. Liability issues further complicate its integration. We present three viewpoints: (i) AI is crucial for advancing syncope management; (ii) AI can enhance the patient experience; and (iii) AI in syncope care is inevitable.

Conclusion: Artificial intelligence may improve syncope diagnosis and management, particularly through machine learning-based test interpretation and wearable device data. However, it has yet to surpass human clinical judgment in complex decision-making. Current challenges include gaps in understanding syncope mechanisms, AI interpretability, generalizability, and clinical integration. Standardized diagnostic approaches, real-world validation, and curated data sets are essential for progress. Artificial intelligence may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.

The complexity and spatial relationships between vascular and cardiac structures, as well as anatomical diversity, pose a challenge for planning and performing cardiac interventions. Medical imaging, especially precise three-dimensional imaging techniques, plays a key role in the decision-making process. While traditional imaging methods like angiography, echocardiography, computed tomography, and magnetic resonance imaging remain gold standards, they have limitations in representing spatial relationships effectively. To overcome these limitations, advanced techniques such as three-dimensional printing, three-dimensional modelling, and Extended Realities are needed. Focusing on Extended Realities, their main advantages are direct spatial visualization based on medical data, interaction with objects, and immersion in cardiac anatomy. These benefits impact procedural planning and intra-procedural navigation. The following publication presents current applications, benefits, drawbacks, and limitations of Virtual, Augmented, and Mixed Reality technologies in cardiac interventions. The aim of this review is to improve understanding and utilization of the entire spectrum of these innovative tools in clinical practice.

Aims: Machine learning (ML) algorithms applied to the electrocardiography (ECG) have been successful in several cardiac diagnoses, however, rarely been used for the diagnostics of takotsubo syndrome (TTS). Our aim was to develop ML-based ECG-models to differentiate TTS from patients with myocardial infarction (MI).

Methods and results: Cross-sectional study in Stockholm. A neural network with UNet architecture was trained and validated on 507 TTS cases and 14 978 controls with suspected and verified MI, identified from the Swedish coronary angiography and angioplasty register. Cross-validation was performed. The models were compared with cardiologists using previously proposed ECG criteria. Receiver operating characteristics (ROC) area under the curve (AUC) for discriminating TTS from patients with ST-elevation and non-ST-elevation MI ROC AUC 0.88 (cross-validation: 0.85-0.92) and 0.86 (cross-validation: 0.82-0.91), respectively. ROC AUC for discriminating TTS from verified MI [non-ST-elevation MI (NSTEMI) and ST-elevation MI (STEMI)] was 0.87 (cross-validation: 0.83-0.91) with sensitivity (0.75) and specificity (0.83) with low positive predictive value (PPV) and high negative predictive value (NPV). Results for suspected MI was ROC AUC 0.85 (cross validation: 0.81-0.91) with sensitivity (0.75) and specificity (0.79) with low PPV (0.11) and high NPV (0.99). The committee of two cardiologists using a combination of ECG criteria achieved an ROC AUC of 0.71.

Conclusion: Machine learning models could discriminate TTS from MI (NSTEMI and STEMI) and suspected MI with high sensitivity and NPV, outperforming cardiologists using conventional criteria. The models require further refinement to increase PPV, precision-recall and external validation, but it holds promise for TTS screening aiding the clinician in ruling out TTS.

Aims: To identify ischaemic cardiomyopathy (ICM) patients with different phenotypes for evaluating their outcomes and heterogeneous treatment effects (HTEs) of coronary artery bypass grafting (CABG).

Methods and results: We applied a machine learning-based consensus, K-Medoids clustering analysis to the Surgical Treatment for Ischemic Heart Failure trial. We compared the risk of all-cause mortality and cardiovascular mortality among different phenotypes. The survival benefits of CABG compared with medical therapy alone were assessed in the identified phenotypes for evaluating HTEs. The consensus clustering analysis identified three distinct clinical phenotypes among 1212 ICM patients based on 19 variables. Specifically, phenotype 1 (n = 371) was characterized by younger ages, higher left ventricular ejection fraction (LVEF), and lower left ventricular end-systolic volume index (n = 371). Phenotype 2 had higher angina grades and more left main/left anterior descending artery stenosis (n = 520). Phenotype 3 had lower LVEF, higher New York Heart Association (NYHA) grades, more diabetes, and less hypertension (n = 321). After a median of 9.8 follow-up years, phenotype 3 had the highest risk of all-cause mortality [hazard ratio (HR), 1.96; 95% confidence intervals (CI), 1.62-2.37] and cardiovascular mortality (HR, 2.46; 95% CI, 1.95-3.10) compared to phenotype 1. Among phenotype 3, CABG provided significant survival benefits in all-cause mortality (HR, 0.75; 95% CI, 0.58-0.96) and cardiovascular mortality (HR, 0.67; 95% CI, 0.50-0.90) compared with medical therapy alone.

Conclusion: We identified three phenotypes with distinct outcomes and HTEs among ICM patients. Patients with lower LVEF, higher NYHA grades, and diabetes had the poorest clinical outcomes but were more likely to derive greater survival benefits from CABG.

Aims: Paravalvular regurgitation (PVR) is frequently observed following Transcatheter Aortic Valve Replacement (TAVR). Periprocedural monitoring of invasive hemodynamics has shown promise for diagnosis of PVR, but automated software options are lacking. We aimed to develop a rule-based algorithm for automated assessment of hemodynamic indices of PVR, and evaluate its construct validity and discriminatory value for cardiac magnetic resonance (CMR)-derived relevant PVR compared to standard manual hemodynamic assessment.

Methods and results: Left ventricular and aortic pressures were invasively measured during TAVR using fluid-filled pigtail catheters. To evaluate construct validity of automated vs. manual assessment of invasive hemodynamics, we compared (i) proportion of cardiac cycles affected by arrhythmias/noise, (ii) pressure gradients, and (iii) PVR indices. Additionally, we compared the discriminatory value of automatically and manually determined PVR indices for CMR-determined relevant PVR at 30-days. In total, 77 patients were enrolled (664 cardiac cycles). Automated filtering of cardiac cycles affected by arrhythmias/noise had a high sensitivity (95.2%) and specificity (86.4%). In addition, excellent agreement was observed between automated and manual computation of mean gradients pre- and post-TAVR [39.3 ± 12.1 vs. 37.5 ± 11.9 mmHg, intra-class correlation coefficient (ICC): 0.916; 1.92 ± 5.87 vs. 1.14 ± 5.89, ICC: 0.957, respectively], and PVR indices [diastolic delta (DD): 41.7 ± 12.4 vs. 40.6 ± 12.3 mmHg, ICC: 0.982, respectively]. Automated and manual assessment of DD showed comparable discriminatory value for relevant PVR [area under the curve (AUC): 0.81 vs. 0.80, respectively].

Conclusion: Rule-based, automated assessment of hemodynamic indices of PVR showed excellent construct validity and discriminatory value for CMR-determined relevant PVR, supporting its use for real-time evaluation and risk stratification in TAVR patients.

Aims: Smartphone-based digital screening was shown to increase the detection rate of atrial fibrillation (AF) requiring oral anticoagulation (OAC) compared with usual care. In this pre-specified subgroup analysis of the eBRAVE-AF trial, we explored sex-specific differences in digital AF-screening.

Methods and results: In eBRAVE-AF (NCT04250220), participating policyholders of a German health insurance company were randomly assigned to a 6-month digital or conventional AF-screening strategy. For digital screening, participants used smartphone-based photoplethysmography (PPG) to detect pulse wave irregularities, which were confirmed using 14-day external ECG-recorders. The primary endpoint was newly diagnosed AF treated with OAC. After 6 months, participants were assigned to a second, cross-over study-phase. The efficacy of AF-screening in women and men was assessed by Cox-regression analysis. 5551 (31% females; 55% ≥ 65 years) of 67 488 invited policyholders free of AF participated in the study and were randomly assigned to digital screening (n = 2860) or usual care (n = 2691). Participation rate was significantly higher among men than women (8.7% vs. 7.3%; P < 0.001). Male sex was a significant predictor for reaching the primary endpoint (HR 1.74; 95% CI: 1.08-2.82, P = 0.023), which was pronounced in patients undergoing digital screening (HR 2.48; 95% CI: 1.52-4.05, P < 0.001). Digital screening did not significantly increase the detection rate of AF requiring OAC in women (HR 1.83; 95% CI: 0.74-4.54; P = 0.193; P-interaction = 0.563).

Conclusion: Men showed higher willingness to participate in this digital study and digital AF-screening was effective for them. While digital screening increased the detection rate of AF with OAC in women, the effect was not statistically significant, likely due to limited power.

Extended reality (XR) is an emerging technology currently finding its way into various medical fields. This systematic review aimed to compile a comprehensive overview of the current data on XR in cardiovascular medicine. To identify the currently available evidence of the applications of XR in cardiology, we searched PubMed and Web of Science until 31 July 2024 using predefined keywords. After screening, a total of 164 studies were included. Overall, the publications were characterized by very heterogeneous study designs. From the published data, it can already be deduced that XR can support every area of cardiology, from education (n = 31) and training (n = 36) to peri-procedural care (n = 78) and rehabilitation (n = 16). Extended reality offers a wide range of applications, and the aim of using these technologies is to optimize the clinical practice. However, these technologies are still in development, and randomized controlled trials are urgently needed to identify their benefits and limitations.