The Random Forest artificial intelligence model accurately predicted preoperative risk of hospital mortality in patients with congenital heart defects, achieving a ROC AUC of 0.902 and a sensitivity of 92.2%.
Observational (n=2,240)
No
Can artificial intelligence models accurately predict preoperative risk of hospital death in patients with congenital heart disease undergoing cardiac surgery?
A Random Forest artificial intelligence model accurately predicts preoperative risk of hospital death in patients with congenital heart disease undergoing cardiac surgery, achieving an AUC of 0.902.
Effect estimate: ROC AUC 0.902
BACKGROUND: Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce. OBJECTIVES: Our goal is to generate a predictive model calculator adapted to the regional reality focused on individual mortality prediction among patients with congenital heart disease undergoing cardiac surgery. METHODS: Two thousand two hundred forty CHD consecutive patients' data from InCor's heart surgery program was used to develop and validate the preoperative risk-of-death prediction model of congenital patients undergoing heart surgery. There were six artificial intelligence models most cited in medical references used in this study: Multilayer Perceptron (MLP), Random Forest (RF), Extra Trees (ET), Stochastic Gradient Boosting (SGB), Ada Boost Classification (ABC) and Bag Decision Trees (BDT). RESULTS: The top performing areas under the curve were achieved using Random Forest (0.902). Most influential predictors included previous admission to ICU, diagnostic group, patient's height, hypoplastic left heart syndrome, body mass, arterial oxygen saturation, and pulmonary atresia. These combined predictor variables represent 67.8% of importance for the risk of mortality in the Random Forest algorithm. CONCLUSIONS: The representativeness of "hospital death" is greater in patients up to 66 cm in height and body mass index below 13.0 for InCor's patients. The proportion of "hospital death" declines with the increased arterial oxygen saturation index. Patients with prior hospitalization before surgery had higher "hospital death" rates than who did not required such intervention. The diagnoses groups having the higher fatal outcomes probability are aligned with the international literature. A web application is presented where researchers and providers can calculate predicted mortality based on the CgntSCORE on any web browser or smartphone.
Chang et al. (Fri,) conducted a observational in Congenital heart defects (n=2,240). Artificial intelligence predictive models (Random Forest) vs. Other machine learning algorithms was evaluated on Hospital mortality (death in the hospital or within 30 days of cardiac surgery) (ROC AUC 0.902). The Random Forest artificial intelligence model accurately predicted preoperative risk of hospital mortality in patients with congenital heart defects, achieving a ROC AUC of 0.902 and a sensitivity of 92.2%.