European heart journal. Digital health最新文献

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.

Aims: Large language models (LLMs) have shown potential in clinical decision support, but the influence of prompt design on their performance, particularly in complex cardiology decision-making, is not well understood.

Methods and results: We retrospectively reviewed 231 patients evaluated by our Heart Team for severe aortic stenosis, with treatment options including surgical aortic valve replacement, transcatheter aortic valve implantation, or medical therapy. We tested multiple prompt-design strategies using zero-shot (0-shot), Chain-of-Thought (CoT), and Tree-of-Thought (ToT) prompting, combined with few-shot prompting, free/guided-thinking, and self-consistency. Patient data were condensed into standardized vignettes and queried using GPT4-o (version 2024-05-13, OpenAI) 40 times per patient under each prompt (147 840 total queries). Primary endpoint was mean accuracy; secondary endpoints included sensitivity, specificity, area under the curve (AUC), and treatment invasiveness. Guided-thinking-ToT achieved the highest accuracy (94.04%, 95% CI 90.87-97.21), significantly outperforming few-shot-ToT (87.16%, 95% CI 82.68-91.63) and few-shot-CoT (85.32%, 95% CI 80.59-90.06; P < 0.0001). Zero-shot prompting showed the lowest accuracy (73.39%, 95% CI 67.48-79.31). Guided-thinking-ToT yielded the highest AUC values (up to 0.97) and was the only prompt whose invasiveness did not differ significantly from Heart Team decisions (P = 0.078). An inverted quadratic relationship emerged between few-shot examples and accuracy, with nine examples optimal (P < 0.0001). Self-consistency improved overall accuracy, particularly for ToT-derived prompts (P < 0.001).

Conclusion: Prompt design significantly impacts LLM performance in clinical decision-making for severe aortic stenosis. Tree-of-Thought prompting markedly improved accuracy and aligned recommendations with expert decisions, though LLMs tended toward conservative treatment approaches.

Aims: The recurrence rate of ventricular tachycardia (VT) after ablation remains high due to the difficulty in locating VT critical sites. This study proposes a machine learning approach for improved identification of ablation targets based on intracardiac electrograms (EGMs) features derived from standard substrate mapping in a chronic myocardial infarction (MI) porcine model.

Methods and results: Thirteen pigs with chronic MI underwent invasive electrophysiological studies using multipolar catheters (Advisor™ HD grid, EnSite Precision™). Fifty-six substrate maps and 35 068 EGMs were collected during sinus rhythm and pacing from multiple sites, including left, right, and biventricular pacing. Ventricular tachycardia was induced in all pigs, and a total of 36 VTs were localized and mapped with early, mid-, and late diastolic components of the circuit. Mapping sites within 6 mm from these critical sites were considered as potential ablation targets. Forty-six signal features representing functional, spatial, spectral, and time-frequency properties were computed from each bipolar and unipolar EGM. Several machine learning models were developed to automatically localize ablation targets, and logistic regressions were used to investigate the association between signal features and VT critical sites. Random forest provided the best accuracy based on unipolar signals from sinus rhythm map, provided an area under the curve of 0.821 with sensitivity and specificity of 81.4% and 71.4%, respectively.

Conclusion: This study demonstrates for the first time that machine learning approaches based on EGM features may support clinicians in localizing targets for VT ablation using substrate mapping. This could lead to the development of similar approaches in VT patients.

Aims: Heart failure (HF) is associated with high mortality and reduced quality of life (QoL). Interventions encouraging a healthy lifestyle and self-care can reduce morbidity and HF-related hospitalizations. We conducted a randomized controlled trial (RCT) to assess the impact of a digital health programme on QoL and clinical outcomes of patients. The programme included remote patient monitoring (RPM), self-care, HF education, and empowered positive lifestyle changes.

Methods and results: Patients (n = 175) received standard-of-care (SoC) at a HF outpatient clinic (control, n = 89) or SoC plus a digital health programme (intervention, n = 86) for 6 months, followed by a 6-month maintenance period. Compliance with RPM was 93% at 6 months. No significant between-group difference was found in the primary endpoint (health-related QoL), except in an exploratory subgroup of New York Heart Association class III patients, where the intervention group had a significantly smaller QoL decline (P = 0.023). For secondary endpoints, the intervention group had significantly greater improvements in self-care at 6 months (P < 0.001) and 12 months (P = 0.003), and in disease-specific knowledge at 12 months (P = 0.001). Several exploratory endpoints favoured the intervention, with significant improvements in triglycerides (P = 0.012), HbA1c (P = 0.014), and fasting glucose (P = 0.010). The TG/HDL cholesterol ratio and TG/glucose index improved significantly at both 6 and 12 months in between-group comparisons.

Conclusion: Although the digital programme did not improve health-related QoL, it led to benefits in other important outcomes such as self-care, disease-specific knowledge, and several key metabolic parameters.

Aims: Artificial intelligence (AI) applied to a single-lead electrocardiogram (AI-ECG) can detect impaired left ventricular systolic dysfunction [LVSD: left ventricular ejection fraction (LVEF) ≤ 40%]. This study aimed to determine if AI-ECG can also predict the two-year risk of major adverse cardiovascular events (MACE) and all-cause mortality independent of LVSD.

Methods and results: Clinical outcomes after two-year follow-up were collected on patients who attended for routine echocardiography and received simultaneous single-lead-ECG recording for AI-ECG analysis. MACE and all-cause mortality were compared by Cox regression, measured against the classification of LVEF > or ≤40%. A subgroup analysis was performed on patients with echocardiographic LVEF ≥ 50%. With previously established thresholds, 'positive' AI-ECG was defined as an LVEF-predicted ≤40%, and negative AI-ECG signified an LVEF-predicted >40%; 1007 patients were included for analysis (mean age, 62.3 years; 52.4% male). 339 (33.7%) had an AI-ECG-predicted LVEF ≤ 40% and had a higher MACE rate (LVEF ≤ 40% vs. >40%: 34.2% vs.11.9%; adjusted hazard ratio (aHR) 1.93; 95% CI, 1.39-2.69; P < 0.001), primarily driven by increased mortality (23% vs. 9.6%; P < 0.001; aHR 1.56; 95% CI, 1.06-2.29; P = 0.0239). In patients with echocardiographic LVEF ≥ 50%, there was a higher incidence of MACE in those with an AI-ECG 'false positive' prediction of LVEF ≤ 40% (27.2% vs.11.9%; P < 0.001; aHR 1.71 and 95% CI, 1.11-2.47) and all-cause mortality (20.4% vs. 9.6%; P < 0.001; aHR 1.59, 95% CI, 1.09-2.42).

Conclusion: An AI-ECG algorithm designed to detect LVEF ≤ 40% can also identify patients at risk of MACE and all-cause mortality from single-lead ECG recording-independent of actual LVEF on echo. This requires further evaluation as a point-of-care risk stratification tool.

Aims: To evaluate feasibility, efficacy, and safety of standardized medical titration at home using telemonitoring. Treatment for heart failure with reduced ejection fraction (HFrEF) has advanced rapidly, emphasizing swift initiation and titration of guideline-directed medical therapy (GDMT) to improve outcomes. Implementing this in practice remains a significant challenge for healthcare. This study proposes a standardized home-based titration process incorporating home-based monitoring (HBM) to enhance GDMT titration, reduce delays, and limit the need for in-clinic assessment visits.

Methods and results: 60 patients were enrolled in this open cohort study. Standardized pre-specified titration schedules in combination with HBM were evaluated. Outcome measures included the time to optimal medical therapy (OMT), doses of GDMT at 8 weeks and 6 months, and safety evaluation through adverse events. The median time to OMT was 48 days (IQR 42-60). All participants achieved OMT within 6 months. At 8 weeks, 73%, 85%, and 88% had reached target doses for beta-blockers, ACE inhibitors, and mineral receptor antagonists, respectively. All participants reached SGLT2i target dosage. By 6 months, 62%, 73%, 80%, and 97% were on target doses for these medications, and 43% had achieved target doses for all four GDMT drugs. No serious adverse events occurred during titration.

Conclusion: We present a novel and promising approach for achieving OMT and high GDMT doses in patients with HFrEF. The utilization of standardized protocols has the potential to optimize the titration process of GDMT, and with HBM support, it can be accomplished with few in-clinic visits.

Aims: The APOLLO-QR (APPlying smartphOne for piLLs intake cOnfirmation by QR code reading) study assessed the congruence between a quick response (QR) code-based digital self-reporting and pill count in measuring medication adherence.

Methods and results: The APOLLO-QR pilot, observational study prospectively included patients owning a smartphone accepting to undergo a home-telemonitoring of ticagrelor adherence by sending feedback of each pill intake through an email generated by framing a QR code placed on the medication packaging. Ticagrelor adherence was measured at 1 and 3 months by pill count allowing to calculate accuracy of the digital self-reporting in estimating drug adherence by assessing the correspondence between the number of received feedback emails and the number of pills taken from those prescribed. Among 109 patients, 30-day adherence to ticagrelor was 98.6 ± 2.6% as measured by pill count vs. 88.9 ± 10.4% as assessed by the number of feedback emails sent by the digital self-reporting, which provided an accuracy in estimating drug adherence of 90.1 ± 10.1%. Similar results were achieved at three months among the 95 patients (87.2%) continuing the study. Only nine patients (8.3%) missed sending four consecutive feedback emails of whom three (2.8%) had voluntarily discontinued ticagrelor within 1 month. A high patient satisfaction emerged from responses to a questionnaire showing that tested telemonitoring was consistently perceived as easy, convenient, and useful, although the need for more interactivity was suggested.

Conclusion: The QR code-based self-reporting of pill intake showed a high accuracy in estimating medication adherence and yielded a good patient satisfaction, suggesting a potential for its clinical applicability.