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

João Chang Junior, Fábio Binuesa, Luiz Fernando Caneo, Aida Luiza Ribeiro Turquetto, Elisandra Cristina Trevisan Calvo Arita, Aline Cristina Barbosa, Alfredo Manoel da Silva Fernandes, Evelinda Marramon Trindade, Fábio Biscegli Jatene, Paul-Eric Dossou and Marcelo Biscegli Jatene

PLOS ONE, 2020, vol. 15, issue 9, 1-21

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 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.

Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238199 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 38199&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0238199

DOI: 10.1371/journal.pone.0238199

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().