Three-dimensional computed tomography. Central MessageWe describe a case of adolescent heart transplantation after medical optimization and physical rehabilitation during a profoundly extended duration of ambulatory centrally cannulated VA-ECMO support.

Background Although the American Heart Association and other professional societies have recommended shared decision-making as a way for patients with atrial fibrillation (AF) or atrial flutter to make informed decisions about using anticoagulation (AC), the best method for facilitating shared decision-making remains uncertain. Objective The aim of this study is to assess the AFib 2gether mobile app for usability, perceived usefulness, and the extent and nature of shared decision-making that occurred for clinical encounters between patients with AF and their cardiology providers in which the app was used. Methods We identified patients visiting a cardiology provider between October 2019 and May 2020. We measured usability from patients and providers using the Mobile App Rating Scale. From the 8 items of the Mobile App Rating Scale, we reported the average score (out of 5) for domains of functionality, esthetics, and overall quality. We administered a 3-item questionnaire to patients relating to their perceived usefulness of the app and a separate 3-item questionnaire to providers to measure their perceived usefulness of the app. We performed a chart review to track the occurrence of AC within 6 months of the index visit. We also audio recorded a subset of the encounters to identify evidence of shared decision-making. Results We facilitated shared decision-making visits for 37 patients visiting 13 providers. In terms of usability, patients’ average ratings of functionality, esthetics, and overall quality were 4.51 (SD 0.61), 4.26 (SD 0.51), and 4.24 (SD 0.89), respectively. In terms of usefulness, 41% (15/37) of patients agreed that the app improved their knowledge regarding AC, and 62% (23/37) agreed that the app helped clarify to their provider their preferences regarding AC. Among providers, 79% (27/34) agreed that the app helped clarify their patients’ preferences, 82% (28/34) agreed that the app saved them time, and 59% (20/34) agreed that the app helped their patients make decisions about AC. In addition, 32% (12/37) of patients started AC after their shared decision-making visits. We audio recorded 25 encounters. Of these, 84% (21/25) included the mention of AC for AF, 44% (11/25) included the discussion of multiple options for AC, 72% (18/25) included a provider recommendation for AC, and 48% (12/25) included the evidence of patient involvement in the discussion. Conclusions Patients and providers rated the app with high usability and perceived usefulness. Moreover, one-third of the patients began AC, and approximately 50% (12/25) of the encounters showed evidence of patient involvement in decision-making. In the future, we plan to study the effect of the app on a larger sample and with a controlled study design. Trial Registration ClinicalTrials.gov NCT04118270; https://clinicaltrials.gov/ct2/show/NCT04118270 International Registered Report Identifier (IRRID) RR2-21986

Background The Centers for Disease Control and Prevention has estimated that atrial fibrillation (AF) affects between 2.7 million and 6.1 million people in the United States. Those who have AF tend to have a much higher stroke risk than others. Although most individuals with AF benefit from anticoagulation (AC) therapy, a significant majority are hesitant to start it. To add, providers often struggle in helping patients negotiate the decision to start AC therapy. To assist in the communication between patients and providers regarding preferences and knowledge about AC therapy, different strategies are being used to try and solve this problem. In this research study, we will have patients and providers utilize the AFib 2gether app with hopes that it will create a platform for shared decision making regarding the prevention of stroke in patients with AF receiving AC therapy. Objective The aim of our study is to measure several outcomes related to encounters between patients and their cardiology providers where AFib 2gether is used. These outcomes include usability and perceived usefulness of the app from the perspective of patients and providers. In addition, we will assess the extent and nature of shared decision making. Methods Eligible patients and providers will evaluate the AFib 2gether mobile app for usability and perceived usefulness in facilitating shared decision making regarding understanding the patient’s risk of stroke and whether or not to start AC therapy. Both patients and providers will review the app and complete multiple questionnaires about the usability and perceived usefulness of the mobile app in a clinical setting. We will also audio-record a subset of encounters to assess for evidence of shared decision making. Results Enrollment in the AFib 2gether shared decision-making study is still ongoing for both patients and providers. The first participant enrolled on November 22, 2019. Analysis and publishing of results are expected to be completed in spring 2021. Conclusions The AFib 2gether app emerged from a desire to increase the ability of patients and providers to engage in shared decision making around understanding the risk of stroke and AC therapy. We anticipate that the AFib 2gether mobile app will facilitate patient discussion with their cardiologist and other providers. Additionally, we hope the study will help us identify barriers that providers face when placing patients on AC therapy. We aim to demonstrate the usability and perceived usefulness of the app with a future goal of testing the value of our approach in a larger sample of patients and providers at multiple medical centers across the country. Trial Registration ClinicalTrials.gov NCT04118270; https://clinicaltrials.gov/ct2/show/NCT04118270 International Registered Report Identifier (IRRID) DERR1-10.2196/21986

Background Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. Objective We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black–polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). Methods We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively. Results Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R0; difference in extracellular-intracellular resistance, R0 – R∞, and tissue heterogeneity, α) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R0 and R0 – R∞ had significantly lower values for patients with heart failure than for those without heart failure (R0: P=.006; R0 – R∞: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. Conclusions This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest.

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BACKGROUND Although the American Heart Association and other professional societies have recommended shared decision-making as a way for patients with atrial fibrillation or flutter (AF) to reach informed decisions about using anticoagulation (AC), the best method of facilitating shared decision-making remains uncertain. OBJECTIVE The aim of this study is to assess the AFib 2gether™ mobile app for usability, perceived usefulness, and extent and nature of shared decision making that occurred for clinical encounters between patients with AF and their cardiology providers in which the app was used. METHODS We identified patients coming to see a cardiology provider from October 2019 until May 2020. We measured usability from patients and providers through the mobile app rating scale (MARS). From the eight items of the MARS, we report the average score (out of 5) for domains of functionality, aesthetics, and overall quality. We administered a three-item questionnaire to patients relating to their perceived usefulness and a separate three-item questionnaire to providers to measure their perceived usefulness. We performed a chart review to track AC starts occurring within 6 months of the index visit. We also audio-recorded a subset of encounters to identify evidence of shared decision-making. RESULTS We facilitated shared decision-making visits for 37 patients seeing 13 providers. In terms of usability, patients’ ratings of functionality, aesthetics, and overall quality were (average ± standard deviation): 4.51 ± 0.61, 4.26 ± 0.51, and 4.24 ± 0.89, respectively. In terms of usefulness, 40% of patients agreed that the app improved their knowledge regarding AC and 62% agreed that the app helped clarify to their provider, their preferences regarding AC. Among providers, 79% agreed that the app helped clarify their patients’ preferences; 82% agreed that the app saved them time; and 59% agreed that the app helped their patients make decisions about AC. Additionally, 12 patients started AC after their shared decision-making visits. We audio-recorded 25 encounters. Of these encounters, 84% included mention of AC for AF, 44% included discussion of multiple options for AC, 72% included a provider recommendation for AC, and 48% included evidence of patient involvement in the discussion. CONCLUSIONS Patients and providers rated the app with high usability and perceived usefulness. Moreover, a third of patients began AC and in nearly ½ the encounters, there was evidence of patient involvement in decision-making. In the future, we plan to study the effect of the app in a larger sample and with a controlled study design. CLINICALTRIAL ClinicalTrials.gov NCT04118270. INTERNATIONAL REGISTERED REPORT RR2-21986

BACKGROUND Clinically, the most important signs and symptoms of acute decompensated heart failure (ADHF) relate to accumulation of excess body fluid, but autonomic dysregulation is another characteristic feature of ADHF physiology. Transthoracic bioimpedance (TBI) is a non-invasive, simple method for measuring fluid retention in lungs. Heart rate variability (HRV) is another widely used noninvasive tool to assess autonomic function. We hypothesize that TBI and HRV can be used for detection of fluid accumulation in ADHF participants. OBJECTIVE In this paper, we aimed to evaluate the performance of TBI and HRV parameters obtained using a fluid accumulation vest (FAV) with dry carbon black polydimethylsiloxane (CB-PDMS) electrodes in a prospective clinical study ‘System for Heart-failure Identification using an External Lung-fluid Device’ (S.H.I.E.L.D.). METHODS We computed fifteen parameters, eight calculated from the model to fit Cole-Cole plot from TBI measurements (R0, RI, R∞, R0 - R∞, FE, fc, α, and Cm), and seven based on linear (mean HR, HRVLF, HRVHF, HRVLFn, HRVHFn) and nonlinear (PDMSymp, and PDMPSymp) analysis of HRV. We compared the values of these parameters between three groups of participants: Control (non-HF hospitalized participants), Baseline (ADHF participants’ recordings taken at the time of admittance to the hospital), and Discharge (ADHF participants’ recordings acquired at the time of discharge from hospital). RESULTS Among the fifteen parameters, two TBI (R0 and R0-R∞) and three HRV (HRVHF, HRVLFn, and HRVHFn) parameters were found to be the most discriminatory between non-HF and ADHF groups. The two TBI parameters had statistically significantly lower values for ADHF participants than for non-HF participants, which is an indicator that accumulated fluids in the lungs are of higher volume for HF participants. We used several machine learning approaches to classify participants with fluid accumulation (Baseline ADHF) and without fluid accumulation (Control and ADHF participants at discharge), termed Wet vs. Dry groups, respectively. A cubic support vector machine model using TBI and HRV parameters achieved an accuracy of 92% classifying Wet and Dry groups. Looking at the parameters included in the model, the TBI parameters are related to intra and extra-cellular fluid, whereas the HRV parameters are mostly related to sympathetic activation. CONCLUSIONS This is useful, for instance, to provide in-home diagnostic wearable vest that can detect or predict fluid accumulation in HF participants. Results suggest that fluid accumulation, detection, and subsequently ADHF detection, could be performed using TBI and HRV measurements acquired with a wearable vest.

BACKGROUND The Center for Disease Control and Prevention has estimated that atrial fibrillation (AF) affects between 2.7-6.1 million people in the United States. Furthermore, those who have AF tend to have a much higher stroke risk than others. Although many individuals could largely benefit from an anticoagulant (AC), a significant majority are hesitant to start AC therapy. To further this issue, some providers tend to find themselves struggling to determine the risks and benefits of prescribing their patients AC. To assist in the communication between patient and provider preferences and knowledge regarding AC, different strategies are being used to try and solve this gap. In this research study, we have both patients and providers utilize the AFib 2getherTM app with hopes that it will create a platform for shared decision-making regarding management and treatment of AF with AC. OBJECTIVE The aims of our study are to measure usability, perceived usefulness to patients and providers, and feasibility of conducting shared decision visits using the mobile app, AFib 2getherTM. To measure provider knowledge of and confidence in utilizing a modern AF management approach and its association with the usability and feasibility. METHODS Eligible patients and providers will evaluate the AFib 2getherTM mobile app for usability and helpfulness in facilitating shared decision making on understanding the patient’s risk of stroke and whether or not to start AC. Both patients and providers will review the app and complete multiple questionnaires about the usability & feasibility of the mobile app in a clinical setting. RESULTS Enrollment in the AFib 2getherTM shared decision-making study is still ongoing for both patients and providers. CONCLUSIONS The AFib 2getherTM app emerged from the desire to increase patient and provider ability for shared decision-making around understanding risk of stroke and about AC. We hope the AFib 2getherTM mobile app will facilitate patient discussion with their cardiology and other providers. Additionally, we hope the study will help us identify a focus point in barriers that providers face when placing patients on AC. We aim to demonstrate the usability and feasibility of the app with a future goal of testing the value of our approach in a larger sample of patients and providers at multiple medical centers across the country. CLINICALTRIAL NCT04118270