Outcome Prediction After Tetralogy of Fallot Repair: A Prospective Clinical and Cardiovascular Magnetic Resonance Study

Abstract

Background: Identification of individuals at risk for major adverse cardiovascular events is essential for contemporary management of patients with repaired tetralogy of Fallot. We sought to identify clinical and cardiovascular magnetic resonance imaging (CMR) predictors of adverse clinical outcomes in repaired tetralogy of Fallot.

Methods: Children and adults prospectively enrolled in the CORRELATE (Comprehensive Outcomes Registry Late After Tetralogy of Fallot Repair) registry followed in North American, European, and Asian centers were studied. All patients had at least moderate pulmonary regurgitation and CMR at enrollment. Time-to-event analyses were performed from CMR completion to primary outcome, defined as mortality, resuscitated sudden death, sustained ventricular arrhythmia, or heart failure admission. Principal component analysis was used to create distinct CMR scores that collectively captured 80% of the variance among 10 CMR measures (systolic function, biventricular volumes/mass, and biatrial areas).

Results: In 720 patients (55% male, median age 30.3±14 years, 78% adult) with mean follow-up 5.7±1.8 years, the primary outcome occurred in 38 patients (5.2%) at a rate of 0.9/100 patient-years. A well-calibrated risk scoring system was created for prediction of the primary outcome at 5 years based on 5 predictors: age, diabetes, right ventricular systolic pressure, and 2 CMR principal component scores (predominantly reflecting atrial areas in the first principal component score and ventricular volumes in the second principal component score) (c-statistic for the composite risk score 0.79 [95% Cl, 0.71-0.88]).

Conclusions: Clinical and imaging characteristics can contribute to risk prediction in repaired tetralogy of Fallot. Further study will be required to evaluate the utility of a risk scoring system for identification of individuals who may benefit from enhanced surveillance, intensified medical therapy, and/or optimally timed intervention.

Keywords: imaging; outcome; tetralogy of Fallot.

Figure 1. Cumulative incidence of primary and secondary outcomes, overall and by age group, with 95% CIs (shaded regions).

The y‐axes are truncated at 15% in the top row and 40% in the bottom row so that details in the cumulative incidence plots are more apparent (n=720).

Figure 2. Summary of principal component analysis models of selected cardiovascular magnetic resonance imaging variables.

R ² values >20% with positive associations are shown in red, values >20% with negative associations in black, and R ² values ≤20% in blue. The font is also sized according to the value of the R ². Hazard ratios are estimated in models that include the 4 principal components. EDVi indicates end‐diastolic volume indexed; EF, ejection fraction; ESVi, end‐systolic volume indexed; LA, left atrium; LV, left ventricle; PC, principal component analysis; RA, right atrium; and RV, right ventricle.

Figure 3. Multivariable Bayesian Cox model using a horseshoe prior with estimated hazard ratios with 95% credible intervals between baseline variables and the primary outcome (left panel) and secondary outcome (right panel).

Percentages on the left of each panel are the posterior probabilities that the hazard ratio is >1 (in black) and <1 (in blue). BMI indicates body mass index; CAD, coronary artery disease; DM, diabetes; EF, ejection fraction; HTN, hypertension; LA, left atrium; LAS, linear analogue scale; LV, left ventricle; NYHA, New York Heart Association; PC, principal component analysis; PCS, physical component score from the SF‐12 survey; RV, right ventricle systolic pressure; RVp; SAS, specific activity scale; TOF, tetralogy of Fallot; and TR, tricuspid regurgitation.

Figure 4. Observed incidence of the primary outcome according to groupings of the proposed risk score.

This figure shows the Kaplan‐Meier estimated 5‐year incidence of the primary outcome (as a percentage), averaged across imputed datasets, and the observed cumulative incidence curves and the observed frequencies of events over the entire follow‐up time (as fractions) when risk scores are calculated in the first imputed dataset and patients are stratified by quintile. Quintiles 2–4 are grouped because their individual incidence curves are similarly low (see text for event rates for all quintiles).