Incremental value of machine learning for risk prediction in tetralogy of Fallot

Objective: Machine learning (ML) can facilitate prediction of major adverse cardiovascular events (MACEs) in repaired tetralogy of Fallot (rTOF). We sought to determine the incremental value of ML above expert clinical judgement for risk prediction in rTOF. Methods: Adult congenital heart disease (ACHD) clinicians (>= 10 years of experience) participated (one cardiac surgeon and four cardiologists (two paediatric and two adult cardiology trained) with expertise in heart failure (HF), electrophysiology, imaging and intervention). Clinicians identified 10 high-yield variables for 5-year MACE prediction (defined as a composite of mortality, resuscitated sudden death, sustained ventricular tachycardia and HF). Risk for MACE (low, moderate or high) was assigned by clinicians blinded to outcome for adults with rTOF identified from an institutional database (n=25 patient reviews conducted by five independent observers). A validated ML model identified 10 variables for risk prediction in the same population. Results: Prediction by ML was similar to the aggregate score of all experts (area under the curve (AUC) 0.85 (95% CI 0.58 to 0.96) vs 0.92 (0.72 to 0.98), p=0.315). Experts with >= 20 years of experience had superior discriminative capacity compared with <20 years (AUC 0.98 (95% CI 0.86 to 0.99) vs 0.80 (0.56 to 0.93), p=0.027). In those with <20 years of experience, ML provided incremental value such that the combined (clinical+ML) AUC approached >= 20 years (AUC 0.85 (95% CI 0.61 to 0.95), p=0.055). Conclusions: Robust prediction of 5-year MACE in rTOF was achieved using either ML or a multidisciplinary team of ACHD experts. Risk prediction of some clinicians was enhanced by incorporation of ML suggesting that there may be incremental value for ML in select circumstances.