Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study

Abstract

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

Recent advances in cardiac anaesthesia

“…[ 14 , 15 ] Chang Junior et al . [ 16 ] evaluated the ability of various AI predictive models on the large data pools for predicting individual patient mortality after congenital heart surgery. They suggested the random forest model for predicting individual mortality after cardiac surgery.…”

The future of Cardiothoracic surgery in Artificial intelligence

“…Chang Junior J et al studied the role of artificial intelligence and reported that the sheer number of cardiac surgical interventions available for congenital cardiac diseases and the low volume of patients compared with adults make it hard to collect large amounts of data regarding pre-operative safety and efficacy for a single procedure [ 25 ]. The random forest model of artificial intelligence can learn from large pools of data and accurately predict individual death risks in patients with congenital heart disease.…”

“…The random forest model of artificial intelligence can learn from large pools of data and accurately predict individual death risks in patients with congenital heart disease. The findings of this model can assist patients, surgeons, and family members of patients in understanding the risks associated with a cardiac surgical intervention [ 25 ]. With the advent and integration of AI into clinical care, the traditional systems are replaced with more efficient and more accurate systems.…”

Medicine-Based Evidence in Congenital Heart Disease: How Artificial Intelligence Can Guide Treatment Decisions for Individual Patients

“…As some have proposed, such algorithms could embrace a lifespan perspective as part of the development and implementation strategy, incorporating longitudinal data and evidence from all stages of life (27). Other potential applications include individualized prediction of the effect of drugs and/or interventions in complex hemodynamic settings (20), prediction of the feasibility of and risk associated with surgical or cathetermediated interventions (28)(29)(30)(31)(32), and incorporation of "soft" outcomes such as exercise capacity and quality of life into the decision-making process (13).…”