European heart journal. Digital health最新文献

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. Large language models (LLMs) offer potential solutions for enhancing patient education and supporting clinical decision-making. This study aimed to evaluate LLMs' applications in CVD and explore their current implementation, from prevention to treatment. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this systematic review assessed LLM applications in CVD. A comprehensive PubMed search identified relevant studies. The review prioritized pragmatic and practical applications of LLMs. Key applications, benefits, and limitations of LLMs in CVD prevention were summarized. Thirty-five observational studies met the eligibility criteria. Of these, 54% addressed primary prevention and risk factor management, while 46% focused on established CVD. Commercial LLMs were evaluated in all but one study, with 91% (32 studies) assessing ChatGPT. The LLM applications were categorized as follows: 72% addressed patient education, 17% clinical decision support, and 11% both. In 68% of studies, the primary objective was to evaluate LLMs' performance in answering frequently asked patient questions, with results indicating accurate, comprehensive, and generally safe responses. However, occasional misinformation and hallucinated references were noted. Additional applications included patient guidance on CVD, first aid, and lifestyle recommendations. Large language models were assessed for medical questions, diagnostic support, and treatment recommendations in clinical decision support. Large language models hold significant potential in CVD prevention and treatment. Evidence supports their potential as an alternative source of information for addressing patients' questions about common CVD. However, further validation is needed for their application in individualized care, from diagnosis to treatment.

Aims: Accurate detection of coronary artery stenosis (CAS) on coronary computed tomography angiography is vital for saving lives, as timely diagnosis can prevent severe cardiac events. However, this task remains challenging due to data complexity and variability in imaging protocols. Deep learning offers promising potential to automate detection, but robust methods are essential to address real-world challenges effectively and enhance patient outcomes.

Methods and results: A total of 900 cases with curved multiplanar reformations, pre-generated during routine clinical workflows, were used to train a multi-instance learning (MIL) model for detecting significant CAS (≥50% luminal obstruction) in the left anterior descending (LAD), right coronary artery (RCA), and left circumflex (LCX), comprising 776 LAD, 694 RCA, and 600 LCX reconstructions. Patient-level predictions utilized attention scores to quantify each slice's contribution, ensuring a robust and interpretable diagnostic approach. The model achieved the best performance for LAD [area under the curve (AUC): 0.92, 95% confidence interval (CI): 0.87-0.96; Brier score: 0.11], followed by RCA (AUC: 0.91, 95% CI: 0.82-0.999; Brier score: 0.09) and LCX (AUC: 0.92, 95% CI: 0.84-0.99; Brier score: 0.07). Calibration was good for LAD but less precise for RCA and LCX. Attention scores enhanced diagnostic precision by focusing on the most relevant slices.

Conclusion: This study highlights the potential of MIL models for CAS detection, with remarkable performance in the LAD. By using attention scores, the model effectively identifies key slices from real-world data, seamlessly integrating with routine clinical workflows. Multi-range pre-processing addresses data complexity, enhancing diagnostic accuracy and supporting clinical decision-making.

Aims: The increasing need for diagnostic echocardiography tests presents challenges in preserving the quality and promptness of reports. While Large Language Models (LLMs) have proven effective in summarizing clinical texts, their application in echo remains underexplored.

Methods and results: Adult echocardiography studies, conducted at the Mayo Clinic from 1 January 2017 to 31 December 2017, were categorized into two groups: development (all Mayo locations except Arizona) and Arizona validation sets. We adapted open-source LLMs (Llama-2, MedAlpaca, Zephyr, and Flan-T5) using In-Context Learning and Quantized Low-Rank Adaptation fine-tuning (FT) for echo report summarization from 'Findings' to 'Impressions.' Against cardiologist-generated Impressions, the models' performance was assessed both quantitatively with automatic metrics and qualitatively by cardiologists. The development dataset included 97 506 reports from 71 717 unique patients, predominantly male (55.4%), with an average age of 64.3 ± 15.8 years. EchoGPT, a fine-tuned Llama-2 model, outperformed other models with win rates ranging from 87% to 99% in various automatic metrics, and produced reports comparable to cardiologists in qualitative review (significantly preferred in conciseness (P < 0.001), with no significant preference in completeness, correctness, and clinical utility). Correlations between automatic and human metrics were fair to modest, with the best being RadGraph F1 scores vs. clinical utility (r = 0.42) and automatic metrics showed insensitivity (0-5% drop) to changes in measurement numbers.

Conclusion: EchoGPT can generate draft reports for human review and approval, helping to streamline the workflow. However, scalable evaluation approaches dedicated to echo reports remains necessary.

Aims: Cardiovascular diseases are a global health crisis, with hypertension as a significant risk factor. Traditional cuff-based blood pressure measurements have various limitations, prompting the exploration of photoplethysmography as an alternative for continuous monitoring. This study aimed to assess a cuff-calibrated wrist-worn photoplethysmography-based blood pressure device against European Society of Hypertension recommendations.

Methods and results: The study assessed photoplethysmography-based blood pressure measurement stability over 28 days in 150 patients by comparing measurements of the wrist-worn photoplethysmography-based device against three daily automated reference blood pressure measurements. Additionally, awake-asleep blood pressure changes were analysed in 40 patients receiving 24-h ambulatory blood pressure monitoring. Data analysis included overall accuracy and recalibration needs during long-term monitoring, the accuracy of monitoring awake-asleep blood pressure changes, and resilience against hydrostatic pressure changes due to variations in device position. Across 28 days, mean errors of 3.84 mmHg (SD 4.46) for systolic and 4.08 mmHg (SD 3.97) for diastolic blood pressure were achieved. Before recalibration on Day 28, mean errors were 2.49 (SD 3.10) for systolic and 2.98 (SD 3.48) for diastolic blood pressure. Awake-asleep blood pressure change accuracy was demonstrated with mean errors of 2.36 (SD ± 2.40) for systolic and 2.17 (SD ± 2.13) for diastolic blood pressure. Hydrostatic pressure testing indicated resilience against changes in device position.

Conclusion: The studied wrist-worn photoplethysmography-based device demonstrated accurate and stable blood pressure monitoring over 28 days, during awake-asleep blood pressure changes and hydrostatic pressure changes. These findings support the device's potential for remote patient monitoring.

Study registration: ClinicalTrials.gov identifier: NCT05899959.

Aims: The real-world effectiveness of the artificial intelligence model based on electrocardiogram (AI-ECG) signals from portable devices for detection of left ventricular systolic dysfunction (LVSD) requires further exploration.

Methods and results: In this prospective, single-centre study, we assessed the diagnostic performance of AI-ECG for detecting LVSD using a six-lead hand-held portable device (AliveCor KardiaMobile 6L). We retrained the AI-ECG model, previously validated with 12-lead ECG, to interpret the 6-lead ECG inputs. Patients aged 19 years or older underwent six-lead ECG recording during transthoracic echocardiography. The primary outcome was the area under the receiver operating characteristic curve (AUROC) for detecting LVSD, defined as an ejection fraction below 40%. Of the 1716 patients recruited prospectively, 1635 were included for the final analysis (mean age 60.6 years, 50% male), among whom 163 had LVSD on echocardiography. The AI-ECG model based on the six-lead portable device demonstrated an AUROC of 0.924 [95% confidence interval (CI) 0.903-0.944], with 83.4% sensitivity (95% CI 77.8-89.0%) and 88.7% specificity (95% CI 87.1-90.4%). Of the 1079 patients evaluated using the AI-ECG model based on the conventional 12-lead ECG, the AUROC was 0.962 (95% CI 0.947-0.977), with 90.1% sensitivity (95% CI 85.0-95.2%) and 91.1% specificity (95% CI 89.3-92.9%).

Conclusion: The AI-ECG model constructed with the six-lead hand-held portable ECG device effectively identifies LVSD, demonstrating comparable accuracy to that of the conventional 12-lead ECG. This highlights the potential of hand-held portable ECG devices leveraged with AI as efficient tools for early LVSD screening.

Aims: Valve-in-valve transcatheter aortic valve implantation (ViV-TAVI) has proven to be an effective treatment option for high-risk patients with degenerated surgical bioprosthetic aortic valves. Multislice computed tomography (MSCT) analysis, the current gold standard for procedural planning, has certain limitations. Virtual reality (VR) could optimize pre-procedural planning by delivering three-dimensional (3D) patient-specific information. This study aimed to investigate the feasibility of visualizing the bioprosthesis and adjacent structures with VR, as well as the accuracy and reproducibility of VR measurements and their advantages and limitations in planning ViV-TAVI.

Methods and results: The visualizations and measurements were performed using 3mensio software and VR software by analysts blinded to the results of the other software based on MSCT data from 20 patients who underwent ViV-TAVI interventions. Moreover, eight physicians graded numerous aspects of pre-procedural ViV-TAVI planning with and without VR visualizations. The analysis showed no significant differences between the measurements and strong correlations with correlation coefficients between 0.874 and 0.994, P < 0.001. Moreover, good-to-excellent intra- and interobserver reliability with intraclass correlation coefficient values between 0.897 and 0.986 was documented. The qualitative analysis showed that 3D visualization using VR facilitates assessing the spatial relationships between the structures. Furthermore, VR enabled a superior visual understanding of the bioprosthesis and the distances between the virtual prosthesis and the coronaries as well as the sinotubular junction.

Conclusion: Virtual reality can be a valuable addition to the pre-procedural planning of ViV-TAVI interventions, thanks to detailed 3D visualization and precise measurements. Further studies are needed to assess the impact on patient outcomes.

Current risk stratification tools can limit the optimal implementation of new and emerging therapies for patients with heart rhythm disorders. For example, stroke prevention treatments have outpaced means for stroke risk stratification for patients with atrial fibrillation (AF). Artificial intelligence (AI) techniques have shown promise for improving various tasks in cardiovascular medicine. Here, we explain key concepts in AI that are central to using these technologies for better risk stratification, highlighting one approach particularly well suited to the task of portable, personalized risk stratification-probabilistic graphical models (PGMs). Probabilistic graphical models can empower physicians to ask and answer a variety of clinical questions, which we demonstrate using a preliminary model of AF-related stroke risk among 1.6 million patients within the University of Utah Health System. This example also highlights the ability of PGMs to combine social determinants of health and other non-traditional variables with standard clinical and demographic ones to improve personalized risk predictions and address risk factor interactions. When combined with electronic health data, these computational technologies hold great promise to empower personalized, explainable, and equitable risk assessment.

Aims: Little is known about the role of electrocardiography (ECG) in the community population independent of physical and laboratory examinations. Thus, this study developed and validated several ECG-based models for cardiovascular disease (CVD) risk assessment, with or without simple questionnaire-based variables.

Methods and results: Using a derivation cohort of 3734 Chinese participants aged ≥40 years, we developed the ECG-based models to predict the risk of developing CVD (comprising fatal and non-fatal coronary heart disease, unstable angina, stroke, and heart failure). Candidate predictors associated with CVD were screened from hundreds of ECG characteristics using a hybrid algorithm. By incorporating the questionnaire-based predictors, we constructed the ECG-questionnaire model. All models were tested in an external validation cohort (n = 1224) to determine their discrimination and calibration. Over a maximum follow-up of 7 years, 433 CVD events occurred in the derivation cohort. The ECG model with 37 selected features achieved comparable performance concerning the clinical model using traditional cardiovascular risk factors (C-statistic: 0.690, 95% confidence interval [CI]: 0.638-0.743) in the external validation cohort. Such performance significantly improved when the questionnaire-based predictors were added (C-statistic: 0.734, 95% CI: 0.685-0.784; calibration χ²: 3.334, P = 0.950). Compared with the clinical model, 17.4% of the participants were correctly assigned to the corresponding risk groups, with an absolute integrated discrimination index of 0.048 (95% CI: 0.016-0.080).

Conclusion: The ECG model with/without questionnaire-based variables can accurately predict future CVD risk independent of physical and laboratory examinations, suggesting its great potential in routine clinical practice.

Aims: Aortic stenosis (AS) is a condition marked by high morbidity and mortality in severe, symptomatic cases without intervention via transcatheter aortic valve implantation (TAVI) or surgical aortic valve replacement (SAVR). Racial and ethnic disparities in access to these treatments have been documented, particularly in North America, where socioeconomic factors such as health insurance confound analyses. This study evaluates disparities in AS management across racial and ethnic groups, accounting for socioeconomic deprivation, using an artificial intelligence (AI) framework.

Methods and results: We conducted a retrospective cohort study using a natural language processing pipeline to analyse both structured and unstructured data from > 1 million patients at a London hospital. Key variables included age, sex, self-reported race and ethnicity, AS severity, and socioeconomic status. The primary outcomes were rates of valvular intervention and all-cause mortality. Among 6967 patients with AS, Black patients were younger, more symptomatic, and more comorbid than White patients. Black patients with objective evidence of AS on echocardiography were less likely to receive a clinical diagnosis than White patients. In severe AS, TAVI and SAVR procedures were performed at lower rates among Black patients than among White patients, with a longer time to SAVR. In multivariate analysis of severe AS, controlling for socioeconomic status, Black patients experienced higher mortality (hazard ratio = 1.42, 95% confidence interval = 1.05-1.92, P = 0.02).

Conclusion: An AI framework characterizes racial and ethnic disparities in AS management, which persist in a universal healthcare system, highlighting targets for future healthcare interventions.