Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning

Abstract

Aims: Previous studies using regression analyses have failed to identify which patients with repaired tetralogy of Fallot (rTOF) are at risk for deterioration in ventricular size and function despite using common clinical and cardiac function parameters as well as cardiac mechanics (strain and dyssynchrony). This study used a machine learning pipeline to comprehensively investigate the predictive value of the baseline variables derived from cardiac magnetic resonance (CMR) imaging and provide models for identifying patients at risk for deterioration.

Methods and results: Longitudinal deterioration for 153 patients with rTOF was categorized as 'none', 'minor', or 'major' based on changes in ventricular size and ejection fraction between two CMR scans at least 6 months apart (median 2.7 years). Baseline variables were measured at the time of the first CMR. An exhaustive variable search with a support vector machine classifier and five-fold cross-validation was used to predict deterioration and identify the most useful variables. For predicting any deterioration (minor or major) vs. no deterioration, the mean area under the curve (AUC) was 0.82 ± 0.06. For predicting major deterioration vs. minor or no deterioration, the AUC was 0.77 ± 0.07. Baseline left ventricular (LV) ejection fraction, LV circumferential strain, and pulmonary regurgitation were most useful for achieving accurate predictions.

Conclusion: For the prediction of deterioration in patients with rTOF, a machine learning pipeline uncovered the utility of baseline variables that was previously lost to regression analyses. The predictive models may be useful for planning early interventions in patients with high risk.

Figures

Figure 1

Performance of the predictive models. The best mean cross-validated AUC obtained from exhaustive variable search across the different numbers of baseline predictor variables for each scenario.

Figure 2

Distinguishing patients with major vs. no deterioration. Visualization of (A) the linear decision boundary between major and no deterioration, and (B) a contour plot of the probability (%) of experiencing major deterioration as a function of the two most predictive baseline variables (LVEF and PR fraction). LVEF, left ventricular ejection fraction; PR, pulmonary regurgitation.

Figure 3

Baseline variable importance for each scenario. Prevalence of individual baseline variables in the top 100 combinations for each scenario. CMR, cardiac magnetic resonance; HR, heart rate; Inter-Dyss, interventricular dyssynchrony; LV-Dyss, left ventricular dyssynchrony; LV-Ecc, left ventricular circumferential strain; LVEF, left ventricular ejection fraction; LV-Ell, left ventricular longitudinal strain; PR, pulmonary regurgitation; RV-Dyss, right ventricular dyssynchrony; RV-Ecc, right ventricular circumferential strain; RVEDVi, indexed right ventricular end-diastolic volume; RVEF, right ventricular ejection fraction; RV-Ell, right ventricular longitudinal strain; RVESVi, indexed right ventricular end-systolic volume; RVMASSi, right ventricular mass index.

Figure 4

Rank-ordering of the individual baseline variables. Ranking of individual baseline variables based on their mean prevalence in top 100 variable combinations across the four experimental scenarios. CMR, cardiac magnetic resonance; HR, heart rate; Inter-Dyss, interventricular dyssynchrony; LV-Dyss, left ventricular dyssynchrony; LV-Ecc, left ventricular circumferential strain; LVEF, left ventricular ejection fraction; LV-Ell, left ventricular longitudinal strain; PR, pulmonary regurgitation; RV-Dyss, right ventricular dyssynchrony; RV-Ecc, right ventricular circumferential strain; RVEDVi, indexed right ventricular end-diastolic volume; RVEF, right ventricular ejection fraction; RV-Ell, right ventricular longitudinal strain; RVESVi, indexed right ventricular end-systolic volume; RVMASSi, right ventricular mass index.

Central illustration

Machine-learning pipeline for predicting deterioration in patients with rTOF. A machine-learning pipeline was designed to evaluate four predictive models and identify the most important baseline variables to predict deterioration in ventricular size and function in patients with repaired tetralogy of Fallot (rTOF). The patients with rTOF were categorized into three deterioration groups using measures from follow-up cardiac magnetic resonance (CMR) scans and then the deterioration was predicted using baseline measurements. The predictive performance was reported using area under the curve (AUC). LV, left ventricular; RV, right ventricular.