Stuart Phillip Thomas

Introduction: Atrial fibrillation (AF), a common arrhythmia, is associated with impaired quality of life (QoL) and increased stroke risk and mortality. Clinical guidelines recommend leveraging digital technologies to support patient education and AF self-management. Conversational artificial intelligence (AI) technologies may support patient engagement with self-management by enabling human-like conversations. This study aims to evaluate the effectiveness of a conversational AI intervention (Conversational HeAlth supporT in Atrial Fibrillation Self-Management - CHAT-AF-S) in improving QoL in patients with AF. Methods and analysis: CHAT-AF-S is a 3-month randomised controlled trial with 1:1 allocation and embedded process evaluation. We will randomise 480 adults (18 years of age and older) with documented AF to the CHAT-AF-S intervention or usual care. Primary outcome is the Atrial Fibrillation Effect on QualiTy-of-life (AFEQT) overall score. We will follow intention-to-treat principles and data analysts will be blinded. Intervention participants will be invited to complete a user experience survey and take part in an interview to explore the feasibility, acceptability, perceived utility, and barriers and enablers to implementing the intervention. Qualitative data will be analysed thematically. Ethics and dissemination: Ethics approval was obtained from the Western Sydney Local Health District Human Ethics Research Committee (2023/ETH00765). Written and informed consent will be obtained from all study participants before commencing any study procedures. Results will be disseminated via peer-reviewed publications and presentations at international conferences. Declaration of Interests: All investigators report nil conflicts of interest. Data Availability: The data that supports this project are available from the corresponding author upon reasonable request. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12623000850673 https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=386249

Background: Patient education and self-management support are critical for atrial fibrillation (AF) management. Conversational artificial intelligence (AI) has the potential to provide interactive and personalized support, but has not been evaluated in patients with AF. Objective: This study aimed to evaluate the feasibility of a conversational AI intervention to support patients with AF postdischarge. Methods: This was a single-blinded, 4:1-parallel–randomized controlled trial with process evaluation of feasibility and engagement. The primary outcome was the change in Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire total score between groups. Patients with AF (18 y and older) were recruited postdischarge from Westmead Hospital cardiology services and randomized to receive either the intervention or usual care. The 6-month intervention consisted of fully automated conversational AI phone calls (with speech recognition and natural language processing) that regularly assessed patient health and symptoms and provided self-management support and education. These phone calls were supplemented with an online survey (sent via text message or email) containing replicated call content when participants could not be reached after 3 call attempts. If participant responses were concerning (eg, poor overall health, low medication confidence, and high symptom burden), they would be followed up with an ad hoc phone call and directed to clinical care if required. A semipersonalized education website was also available as part of the intervention, and participants were encouraged weekly (nudges delivered via text messages or emails) to visit it. Results: A total of 103 patients (mean age, 63.7 y, SD 11.2 y; n=72, 70% male) were randomized (82 to the intervention); the target sample size was 385. The difference in the AFEQT total score was nonsignificant (adjusted mean difference 2.08, 95% CI −7.79 to 11.96; P=.46). An exploratory prepost comparison revealed an improvement in total AFEQT score in the intervention group only (baseline: 69.9, 95% CI 64.4 to 75.5; 6 months: 79.9, 95% CI 74.9 to 84.8; P=.01). Participants completed 4 of 7 outreaches on average, and 88.4% (304/344) of completed outreaches were reported as useful. Conclusions: This proof-of-concept study demonstrates the feasibility of conversational AI in supporting patients with chronic conditions postdischarge. Intervention participants had improvement in their atrial fibrillation quality of life, though the forced shortening of the evaluation was unable to demonstrate a significant difference between groups.

BACKGROUND: Transcatheter renal denervation (RDN) remains inconsistent despite developments in ablation technologies, due to the lack of an intraprocedural physiological end point. The aim of this study was to identify if aorticorenal ganglion (ARG) guided RDN using microwave (MW) catheter leads to more consistent denervation outcomes compared with empirical MW ablation. METHODS: Pigs underwent sham procedure (n=8) or bilateral RDN using an in-house built open-irrigated MW catheter. Before denervation, ipsilateral ARG pacing was performed leading to renal artery vasoconstriction. MW ablation group (MW-group; n=7) received 1 ablation (100–120 W for 360 seconds) in the mid-main renal artery based on artery caliber. ARG-guided-MW ablation group (ARG-MW-group; n=7) was permitted an additional ablation more distally or at higher power until a vasoconstrictive response was abolished. Animals were euthanized at 4 to 5 weeks post-procedure. RESULTS: ARG pacing caused an ipsilateral reduction in renal artery caliber from 4.67 to 4 mm; P =0.0006 in MW-group and 4.8 to 3.9 mm; P =0.001 in ARG-MW-group. Repeat ARG pacing at euthanasia led to a reduction in renal artery caliber in MW-group from 5.1 to 4.8 mm; P =0.006, but not in ARG-MW-group from 4.88 to 4.55 mm; P =0.08. There were no differences in ablation injury volumes between the groups. Compared with undenervated sham controls, ARG-MW-RDN versus MW-RDN caused median reductions in viable nerve area (antityrosine hydroxylase staining) at 4 to 5 weeks by 92.6% (interquartile range, 0.94–19.59%; P P =0.006) and median renal cortical norepinephrine content by 68.06% (interquartile range, 27.16–38.39%; P P =NS). CONCLUSIONS: ARG pacing serves as a physiological procedural end point to guide MW denervation to improve denervation outcomes.

Background: Ventricular tachycardia (VT) storm is associated with significantly increased morbidity, mortality, and exponential healthcare utilization. Although catheter ablation (CA) may be curative, there are limited data directly comparing outcomes of early CA with initial medical therapy. Methods: We compared outcomes of patients presenting with VT storm treated with initial CA versus those treated with initial medical therapy during their first storm presentation in an observational study. Retrospective data from the host institution from January 2014 to April 2020 of 129 patients with their first VT storm presentation were analyzed (58 underwent initial CA, 71 underwent treatment with initial medical therapy). Outcomes were compared in follow-up. Results: Median time to initial CA was 6 days. Over a median follow-up of 702 days, patients who underwent initial CA compared with those treated with initial medical therapy had significantly less: (i) VA recurrence (43% versus 92%; P =0.002); (ii) VT storm recurrence (28% versus 73%; P P =0.002); (iv) iatrogenic complications (at 12 months: 17% versus 45%; P P =0.01); (vi) total number of hospitalizations (median 1 versus 4; P P <0.001). There were no intraprocedural deaths in patients treated with early CA. Conclusion: In an observational setting in which patients presenting with storm, early CA appears superior to initial medical therapy in terms of VT recurrence, storm recurrence, iatrogenic complications, cardiovascular hospitalizations, and cumulative days in hospital in follow-up.

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

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. CLINICALTRIAL Australian New Zealand Clinical Trials Registry ACTRN12621000174886; https://www.australianclinicaltrials.gov.au/anzctr/trial/ACTRN12621000174886 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/34470