How Good Are Cardiologists at Predicting Major Adverse Events in Fontan Patients?
JACC Adv
; 3(1): 100736, 2024 Jan.
Article
em En
| MEDLINE
| ID: mdl-38939804
ABSTRACT
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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article