Life and Death: A Multicenter Study Evaluating Cardiologists' Approach to Difficult Conversations with Fontan Patients and Families.

Outpatient cardiologists provide longitudinal care for Fontan patients. As these patients age, they face mounting morbidities, necessitating challenging conversations about prognosis and goals of care. We created a novel survey to evaluate cardiologists' attitudes surrounding risk counseling for patients/caregivers. Cardiologists were recruited during concomitant outpatient enrollment of individuals with Fontan operation > age 10. Physician demographic data, expectations of timing in discussing adverse event risk, and perceived barriers were collected. Barriers were analyzed using a thematic approach. 40 cardiologists (9 institutions) responded regarding 155 patients (mean age 21.2 years, SD 7.7). Physicians were mostly male (58%) with mean practice of 21 years post-fellowship (SD 12). Most felt the time was right to have a conversation with patient (55%) and family (62%), and majority thought patient (53%) and family (75%) were ready for such a conversation. Most had previously discussed prognosis with patient (72%) and family (75%). Providers were inclined to discuss risk with caregivers earlier (mean patient age 9 years, SD 11) than patients (mean patient age 17 years, SD 6.4). Nevertheless, 42% of physicians perceived significant barriers and provided 58 narrative comments categorized into 4 major themes: (1) Patient-related (53.4%), including cognitive limitations and mental health; (2) Provider-related (16.4%), including lack of familiarity, preservation of happiness, and discomfort; (3) Family related (12.3%), including protection/denial and psychosocial stressors; (4) Other (26%), including social barriers. Experienced cardiologists are willing to have difficult conversations; nearly half reported largely patient-related barriers. Facilitating these conversations is critical for the adolescent/young adult with Fontan physiology.

How Good Are Cardiologists at Predicting Major Adverse Events in Fontan Patients?

Background: It is unknown how well cardiologists predict which Fontan patients are at risk for major adverse events (MAEs). Objectives: The purpose of this study was to examine the accuracy of cardiologists' ability to identify the "good Fontan" patient, free from MAE within the following year, and compare that predicted risk cohort to patients who experienced MAE. Methods: This prospective, multicenter study included patients ≥10 years with lateral tunnel or extracardiac Fontan. The cardiologist was asked the yes/no "surprise" question: would you be surprised if your patient has a MAE in the next year? After 12 months, the cardiologist was surveyed to assess MAE. Agreement between cardiologist predictions of MAE and observed MAE was determined using the simple kappa coefficient. Multivariable generalized linear mixed effects models were performed to identify factors associated with MAE. Results: Overall, 146 patients were enrolled, and 99/146 (68%) patients w`ere predicted to be a "good Fontan." After 12 months, 17 (12%) experienced a MAE. The simple kappa coefficient of cardiologists' prediction was 0.17 (95% CI: 0.02-0.32), suggesting prediction of MAE was 17% better than random chance. In the multivariable cardiologist-predicted MAE (N = 47) model, diuretic/beta-blocker use (P ≤ 0.001) and systolic dysfunction (P = 0.005) were associated with MAE. In the observed multivariable MAE (N = 17) model, prior unplanned cardiac admission (P = 0.006), diuretic/beta-blocker use (P = 0.028), and ≥moderate atrioventricular valve regurgitation (P = 0.049) were associated with MAE. Conclusions: Cardiologists are marginally able to predict which Fontan patients are at risk for MAE over a year. There was overlap between factors associated with a cardiologist's prediction of risk and observed MAE, namely the use of diuretic/beta-blocker.

Impact of Age-Related Change in Caval Flow Ratio on Hepatic Flow Distribution in the Fontan Circulation.

BACKGROUND: The Fontan operation is a palliative technique for patients born with single ventricle heart disease. The superior vena cava (SVC), inferior vena cava (IVC), and hepatic veins are connected to the pulmonary arteries in a total cavopulmonary connection by an extracardiac conduit or a lateral tunnel connection. A balanced hepatic flow distribution (HFD) to both lungs is essential to prevent pulmonary arteriovenous malformations and cyanosis. HFD is highly dependent on the local hemodynamics. The effect of age-related changes in caval inflows on HFD was evaluated using cardiac magnetic resonance data and patient-specific computational fluid dynamics modeling. METHODS: SVC and IVC flow from 414 patients with Fontan were collected to establish a relationship between SVC:IVC flow ratio and age. Computational fluid dynamics modeling was performed in 60 (30 extracardiac and 30 lateral tunnel) patient models to quantify the HFD that corresponded to patient ages of 3, 8, and 15 years, respectively. RESULTS: SVC:IVC flow ratio inverted at ≈8 years of age, indicating a clear shift to lower body flow predominance. Our data showed that variation of HFD in response to age-related changes in caval inflows (SVC:IVC, 2, 1, and 0.5 corresponded to ages, 3, 8, and 15+, respectively) was not significant for extracardiac but statistically significant for lateral tunnel cohorts. For all 3 caval inflow ratios, a positive correlation existed between the IVC flow distribution to both the lungs and the HFD. However, as the SVC:IVC ratio changed from 2 to 0.5 (age, 3-15+) years, the correlation's strength decreased from 0.87 to 0.64, due to potential flow perturbation as IVC flow momentum increased. CONCLUSIONS: Our analysis provided quantitative insights into the impact of the changing caval inflows on Fontan's long-term HFD, highlighting the importance of SVC:IVC variations over time on Fontan's long-term hemodynamics. These findings broaden our understanding of Fontan hemodynamics and patient outcomes.

A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology.

Purpose To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods This retrospective study used 250 cardiac MRI examinations (November 2007-December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two U-Net 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) by using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. Results There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volume (EDV) (bias: -0.6 mL/m2, LOA: -20.6 to 19.5 mL/m2) and end-systolic volume (ESV) (bias: -1.1 mL/m2, LOA: -18.1 to 15.9 mL/m2), with high intraclass correlation coefficients (ICCs > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agreement for ventricular mass (bias: -1.9 g/m2, LOA: -17.3 to 13.5 g/m2) and an ICC of 0.94. There was also acceptable agreement for stroke volume (bias: 0.6 mL/m2, LOA: -17.2 to 18.3 mL/m2) and ejection fraction (bias: 0.6%, LOA: -12.2% to 13.4%), with high ICCs (>0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. Conclusion The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multiple centers. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry. Keywords: Cardiac, Adults and Pediatrics, MR Imaging, Congenital, Volume Analysis, Segmentation, Quantification Supplemental material is available for this article. © RSNA, 2023.