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

Autor: Chang Junior J; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil.; Fundação Armando Alvares Penteado-FAAP, São Paulo, Brazil.; Escola Superior de Engenharia e Gestão-ESEG, São Paulo, Brazil., Binuesa F; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil., Caneo LF; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil., Turquetto ALR; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil.; Health Technology Assessment Center of Clinics Hospital-NATS-HCFMUSP, São Paulo, Brazil., Arita ECTC; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil., Barbosa AC; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil., Fernandes AMDS; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil., Trindade EM; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil.; Health Technology Assessment Center of Clinics Hospital-NATS-HCFMUSP, São Paulo, Brazil.; São Paulo State Health Secretariat-SES-SP, São Paulo, Brazil., Jatene FB; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil., Dossou PE; Institut Catholique des Arts et Métiers-Icam, Paris-Sénart, France., Jatene MB; Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2020 Sep 04; Vol. 15 (9), pp. e0238199. Date of Electronic Publication: 2020 Sep 04 (Print Publication: 2020).
DOI: 10.1371/journal.pone.0238199
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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