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Background: TEXTMEDS (Text Messages to Improve Medication Adherence and Secondary Prevention After Acute Coronary Syndrome) examined the effects of text message-delivered cardiac education and support on medication adherence after an acute coronary syndrome.Methods: TEXTMEDS was a single-blind, multicenter, randomized controlled trial of patients after acute coronary syndrome. The control group received usual care (secondary prevention as determined by the treating clinician); the intervention group also received multiple motivational and supportive weekly text messages on medications and healthy lifestyle with the opportunity for 2-way communication (text or telephone). The primary end point of self-reported medication adherence was the percentage of patients who were adherent, defined as >80% adherence to each of up to 5 indicated cardioprotective medications, at both 6 and 12 months.Results: A total of 1424 patients (mean age, 58 years [SD, 11]; 79% male) were randomized from 18 Australian public teaching hospitals. There was no significant difference in the primary end point of self-reported medication adherence between the intervention and control groups (relative risk, 0.93 [95% CI, 0.84-1.03]; P=0.15). There was no difference between intervention and control groups at 12 months in adherence to individual medications (aspirin, 96% vs 96%; β-blocker, 84% vs 84%; angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, 77% vs 80%; statin, 95% vs 95%; second antiplatelet, 84% vs 84% [all P>0.05]), systolic blood pressure (130 vs 129 mm Hg; P=0.26), low-density lipoprotein cholesterol (2.0 vs 1.9 mmol/L; P=0.34), smoking (P=0.59), or exercising regularly (71% vs 68%; P=0.52). There were small differences in lifestyle risk factors in favor of intervention on body mass index <25 kg/m2 (21% vs 18%; P=0.01), eating ≥5 servings per day of vegetables (9% vs 5%; P=0.03), and eating ≥2 servings per day of fruit (44% vs 39%; P=0.01).Conclusions: A text message-based program had no effect on medical adherence but small effects on lifestyle risk factors.Registration: URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364448; Unique identifier: ANZCTR ACTRN12613000793718. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is an increasingly common chronic health condition for which integrated care that is multidisciplinary and patient-centric is recommended yet challenging to implement. Objective: The aim of Coordinating Health Care With Artificial Intelligence-Supported Technology in AF is to evaluate the feasibility and potential efficacy of a digital intervention (AF-Support) comprising preprogrammed automated telephone calls (artificial intelligence conversational technology), SMS text messages, and emails, as well as an educational website, to support patients with AF in self-managing their condition and coordinate primary and secondary care follow-up. Methods: Coordinating Health Care With Artificial Intelligence-Supported Technology in AF is a 6-month randomized controlled trial of adult patients with AF (n=385), who will be allocated in a ratio of 4:1 to AF-Support or usual care, with postintervention semistructured interviews. The primary outcome is AF-related quality of life, and the secondary outcomes include cardiovascular risk factors, outcomes, and health care use. The 4:1 allocation design enables a detailed examination of the feasibility, uptake, and process of the implementation of AF-Support. Participants with new or ongoing AF will be recruited from hospitals and specialist-led clinics in Sydney, New South Wales, Australia. AF-Support has been co-designed with clinicians, researchers, information technologists, and patients. Automated telephone calls will occur 7 times, with the first call triggered to commence 24 to 48 hours after enrollment. Calls follow a standard flow but are customized to vary depending on patients' responses. Calls assess AF symptoms, and participants' responses will trigger different system responses based on prespecified protocols, including the identification of red flags requiring escalation. Randomization will be performed electronically, and allocation concealment will be ensured. Because of the nature of this trial, only outcome assessors and data analysts will be blinded. For the primary outcome, groups will be compared using an analysis of covariance adjusted for corresponding baseline values. Randomized trial data analysis will be performed according to the intention-to-treat principle, and qualitative data will be thematically analyzed. Results: Ethics approval was granted by the Western Sydney Local Health District Human Ethics Research Committee, and recruitment started in December 2020. As of December 2021, a total of 103 patients had been recruited. Conclusions: This study will address the gap in knowledge with respect to the role of postdischarge digital care models for supporting patients with AF. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12621000174886 International Registered Report Identifier (IRRID): DERR1-10.2196/34470 [ABSTRACT FROM AUTHOR]

With increasing use of handheld ECG devices for atrial fibrillation (AF) screening, it is important to understand their accuracy in community and hospital settings and how it differs among settings and other factors. A systematic review of eligible studies from community or hospital settings reporting the diagnostic accuracy of handheld ECG devices (ie, devices producing a rhythm strip) in detecting AF in adults, compared with a gold standard 12-lead ECG or Holter monitor, was performed. Bivariate hierarchical random-effects meta-analysis and meta-regression were performed using R V.3.6.0. The search identified 858 articles, of which 14 were included. Six studies recruited from community (n=6064 ECGs) and eight studies from hospital (n=2116 ECGs) settings. The pooled sensitivity was 89% (95% CI 81% to 94%) in the community and 92% (95% CI 83% to 97%) in the hospital. The pooled specificity was 99% (95% CI 98% to 99%) in the community and 95% (95% CI 90% to 98%) in the hospital. Accuracy of ECG devices varied: sensitivity ranged from 54.5% to 100% and specificity ranged from 61.9% to 100%. Meta-regression showed that setting (p=0.032) and ECG device type (p=0.022) significantly contributed to variations in sensitivity and specificity. The pooled sensitivity and specificity of single-lead handheld ECG devices were high. Setting and handheld ECG device type were significant factors of variation in sensitivity and specificity. These findings suggest that the setting including user training and handheld ECG device type should be carefully reviewed. [ABSTRACT FROM AUTHOR]

Background: There are no randomized data to inform the extent to which transvenous cardiac leads cause tricuspid regurgitation (TR)., Objectives: This study sought to determine the effect of a transvenous implantable cardioverter-defibrillator (TV-ICD) on TR severity, and secondarily, on right ventricular (RV) size and function., Methods: We evaluated TR severity before and 6 months after implantable cardioverter-defibrillator insertion in a post hoc analysis of adults randomized to receive a transvenous (n = 252) or subcutaneous implantable cardioverter-defibrillator (S-ICD) (n = 251) device. TR and RV size and systolic function were assessed by echocardiographic images analyzed in a core laboratory., Results: At baseline, at least mild TR was present in 30% of individuals. At 6 months, the proportion of participants with any TR in the TV-ICD group was 42% vs 19% in the S-ICD group (P < 0.001). The proportion with moderate or severe TR was 7% in the TV-ICD group vs 2% in the S-ICD group (P = 0.021). At 6 months, the OR of at least 1 grade worsening of TR in the TV-ICD group as compared with the S-ICD group was 7.2 (95% CI: 3.3-15.8; P < 0.001). There were no differences between groups with respect to RV size or systolic function., Conclusions: Six months following TV-ICD insertion, there was a 7-fold increase in the risk of at least 1 grade worsening of TR, with 7% of individuals having TR that was moderate or severe. There was no detectable difference in RV size or function; however, longer follow-up is needed., Competing Interests: Funding Support and Author Disclosures This study was funded by Boston Scientific through an unrestricted research grant. Boston Scientific did not have any input into the preparation of this manuscript. Dr Leong has received consultancy fees from Boston Scientific. Dr Mondésert has received consultancy and speaking fees from Bostin Scientific, Abbott, Medtronic, and Biotronik. Dr Joza has received research support from Medtronic Inc; consulting fees from Boston Scientific; and honoraria from Biosense Webster Canada. Dr Healey has received research support and speaking fees from BMS/Pfizer, Boston Scientific, and Medtronic; speaking fees from Servier; and consultancy fees from Bayer and Boston Scientific. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Cardiovascular disease (CVD) risk prediction is important for guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared with traditional risk scores in CVD risk prognostication., Methods and Results: MEDLINE, EMBASE, CENTRAL, and SCOPUS Web of Science Core collections were searched for studies comparing ML models to traditional risk scores for CVD risk prediction between the years 2000 and 2021. We included studies that assessed both ML and traditional risk scores in adult (≥18 year old) primary prevention populations. We assessed the risk of bias using the Prediction Model Risk of Bias Assessment Tool (PROBAST) tool. Only studies that provided a measure of discrimination [i.e. C-statistics with 95% confidence intervals (CIs)] were included in the meta-analysis. A total of 16 studies were included in the review and meta-analysis (3302 515 individuals). All study designs were retrospective cohort studies. Out of 16 studies, 3 externally validated their models, and 11 reported calibration metrics. A total of 11 studies demonstrated a high risk of bias. The summary C-statistics (95% CI) of the top-performing ML models and traditional risk scores were 0.773 (95% CI: 0.740-0.806) and 0.759 (95% CI: 0.726-0.792), respectively. The difference in C-statistic was 0.0139 (95% CI: 0.0139-0.140), P < 0.0001., Conclusion: ML models outperformed traditional risk scores in the discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CVD events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilized for primary prevention.This review was registered with PROSPERO (CRD42020220811)., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)