European heart journal. Digital health最新文献

Aims: This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD.

Methods and results: We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively.

Conclusion: The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.

Aims: We aimed to investigate the concordance between heart rate variability (HRV) derived from the photoplethysmographic (PPG) signal of a commercially available smartwatch compared with the gold-standard high-resolution electrocardiogram (ECG)-derived HRV in patients with cardiovascular disease.

Methods and results: We prospectively enrolled 104 survivors of acute ST-elevation myocardial infarction, 129 patients after an ischaemic stroke, and 30 controls. All subjects underwent simultaneous recording of a smartwatch (Garmin vivoactive 4; Garmin Ltd, Olathe, KS, USA)-derived PPG signal and a high-resolution (1000 Hz) ECG for 30 min under standardized conditions. HRV measures in time and frequency domain, non-linear measures, as well as deceleration capacity (DC) were calculated according to previously published technologies from both signals. Lin's concordance correlation coefficient (ρ_c) between smartwatch-derived and ECG-based HRV markers was used as a measure of diagnostic accuracy. A very high concordance within the whole study cohort was observed for the mean heart rate (ρ_c = 0.9998), standard deviation of the averages of normal-to-normal (NN) intervals in all 5min segments (SDANN; ρ_c = 0.9617), and very low frequency power (VLF power; ρ_c = 0.9613). In contrast, detrended fluctuation analysis (DF-α1; ρ_c = 0.5919) and the square mean root of the sum of squares of adjacent NN-interval differences (rMSSD; ρ_c = 0.6617) showed only moderate concordance.

Conclusion: Smartwatch-derived HRV provides a practical alternative with excellent accuracy compared with ECG-based HRV for global markers and those characterizing lower frequency components. However, caution is warranted with HRV markers that predominantly assess short-term variability.

[This corrects the article DOI: 10.1093/ehjdh/ztac026.].

Aims: We aimed to assess longer-term results (accessibility, hospital admissions, and mortality) in elderly patients referred to a cardiology department (CD) from primary care using e-consultation in outpatient care.

Methods and results: We included 9963 patients >80 years from 1 January 2010 to 31 December 2019. Until 2012, all patients attended an in-person consultation (2010-2012). In 2013, we instituted an e-consult programme (2013-2019) for all primary care referrals to cardiologists that preceded a patient's in-person consultation when considered. We used an interrupted time series (ITS) regression approach to investigate the impact of e-consultation on (i) cardiovascular hospital admissions and mortality. We also analysed (ii) the total number and referral rate (population-adjusted referred rate) in both periods, and (iii) the accessibility was measured as the number of consultations and variation according to the distance from the municipality and reference hospital. During e-consultation, the demand for care increased (12.8 ± 4.3% vs. 25.5 ± 11.1% per 1000 inhabitants, P < 0.001) and referrals from different areas were equalized. After the implementation of e-consultation, we observed that the increase in hospital admissions and mortality were stabilized [incidence rate ratio (iRR): 1.351 (95% CI, 0.787, 2.317), P = 0.874] and [iRR: 1.925 (95% CI: 0.889, 4.168), P = 0.096], respectively. The geographic variabilities in hospital admissions and mortality seen during the in-person consultation were stabilized after e-consultation implementation.

Conclusions: Implementation of a clinician-to-clinician e-consultation programme in outpatient care was associated with improved accessibility to cardiology healthcare in elderly patients. After e-consultations were implemented, hospital admissions and mortality were stabilized.

Aims: Ischaemic heart disease results from insufficient coronary blood flow. Direct measurement of absolute flow (mL/min) is feasible, but has not entered routine clinical practice in most catheterization laboratories. Interventional cardiologists, therefore, rely on surrogate markers of flow. Recently, we described a computational fluid dynamics (CFD) method for predicting flow that differentiates inlet, side branch, and outlet flows during angiography. In the current study, we evaluate a new method that regionalizes flow along the length of the artery.

Methods and results: Three-dimensional coronary anatomy was reconstructed from angiograms from 20 patients with chronic coronary syndrome. All flows were computed using CFD by applying the pressure gradient to the reconstructed geometry. Side branch flow was modelled as a porous wall boundary. Side branch flow magnitude was based on morphometric scaling laws with two models: a homogeneous model with flow loss along the entire arterial length; and a regionalized model with flow proportional to local taper. Flow results were validated against invasive measurements of flow by continuous infusion thermodilution (Coroventis™, Abbott). Both methods quantified flow relative to the invasive measures: homogeneous (r 0.47, P 0.006; zero bias; 95% CI -168 to +168 mL/min); regionalized method (r 0.43, P 0.013; zero bias; 95% CI -175 to +175 mL/min).

Conclusion: During angiography and pressure wire assessment, coronary flow can now be regionalized and differentiated at the inlet, outlet, and side branches. The effect of epicardial disease on agreement suggests the model may be best targeted at cases with a stenosis close to side branches.

Aims: Currently, virtual reality (VR) constitutes a vital aspect of digital health, necessitating an overview of study trends. We classified type A studies as those in which health care providers utilized VR devices and type B studies as those in which patients employed the devices. This study aimed to analyse the characteristics of each type of studies using natural language processing (NLP) methods.

Methods and results: Literature related to VR in cardiovascular research was searched in PubMed between 2010 and 2022. The characteristics of studies were analysed based on their classification as type A or type B. Abstracts of the studies were used as corpus for text mining. A binary logistic regression model was trained to automatically categorize the abstracts into the two study types. Classification performance was evaluated by accuracy, precision, recall, F-1 score, and c-statistics of the receiver operator curve (ROC) analysis. In total, 171 articles met the inclusion criteria, where 120 (70.2%) were type A studies and 51 (29.8%) were type B studies. Type A studies had a higher proportion of case reports than type B studies (18.3% vs. 3.9%, P = 0.01). As for abstract classification, the binary logistic regression model yielded 88% accuracy and an area under the ROC of 0.98. The words 'training', '3d', and 'simulation' were the most powerful determinants of type A studies, while the words 'patients', 'anxiety', and 'rehabilitation' were more indicative for type B studies.

Conclusions: NLP methods revealed the characteristics of the two types of VR-related research in cardiology.