Machine learning to predict outcomes of fetal cardiac disease: a pilot study

Abstract BACKGROUND: Prediction of outcomes following a prenatal diagnosis of congenital heart disease is challenging. Machine learning (ML) algorithms may be used to reduce clinical uncertainty and improve prognostic accuracy. METHODS: We performed a pilot study to train ML algorithms to predict postnatal outcomes based on clinical data. Specific objectives were to predict 1) in-utero or neonatal death, 2) high-acuity neonatal care and 3) favourable outcomes. We included all fetuses with cardiac disease at Sunnybrook Health Sciences Centre, Toronto, Canada, from 2012 – 2021. Prediction models were created using the XgBoost algorithm (tree-based) with 5-fold cross validation. RESULTS: Among 211 cases of fetal cardiac disease, 61 were excluded (39 terminations, 21 lost to follow-up, 1 isolated arrhythmia), leaving a cohort of 150 fetuses. Fifteen (10%) demised (10 neonates) and 70 (52%) of live births required high acuity neonatal care. Of those with clinical follow-up, 57/82 (70%) had a favourable outcome. Prediction models for live birth, high acuity neonatal care and favourable outcome had AUCs of 0.75, 0.82 and 0.72, respectively. The most important predictors for death were the presence of non-cardiac or genetic abnormalities and more severe structural heart disease. High acuity of postnatal care was predicted by increased nuchal thickness, lower gestational age (GA) and birthweight and favourable outcome was predicted by normal fetal right ventricular function, no tricuspid valve abnormalities, and normal GA/weight at birth. CONCLUSION Prediction models using ML provide good discrimination of key prenatal and postnatal outcomes among fetuses with congenital heart disease.