Predictors of in-ICU length of stay among congenital heart defect patients using artificial intelligence model: A pilot study.

Autor:	Chang Junior J; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil.; Escola Superior de Engenharia e Gestão - ESEG, Rua Apeninos, 960, São Paulo, Brazil.; Centro Universitário Armando Alvares Penteado - FAAP, Rua Alagoas, 903, São Paulo, Brazil., Caneo LF; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil., Turquetto ALR; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil.; Núcleo de Avaliação de Tecnologias da Saúde - NATS-HCFMUSP, Brazil., Amato LP; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil.; Núcleo de Avaliação de Tecnologias da Saúde - NATS-HCFMUSP, Brazil., Arita ECTC; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil., Fernandes AMDS; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil., Trindade EM; Núcleo de Avaliação de Tecnologias da Saúde - NATS-HCFMUSP, Brazil.; Laboratório de Ensino, Pesquisa e Inovação Em Saúde - LEPIC-HCFMUSP, Superintendência / Hospital Das Clínicas da FMUSP, Rua Dr. Ovidio Pires de Campos, 225, 5°. Andar - Superintendência, Sao Paulo, Brazil.; Sao Paulo State Health Secretariat-SES-SP, Sao Paulo, Brazil., Jatene FB; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil., Dossou PE; Institut Catholique des Arts et Metiers-Icam, Paris-Senart, France., Jatene MB; Hospital Das Clínicas HCFMUSP, Universidade de São Paulo, Instituto Do Coração - InCor, Av. Dr. Enéas Carvalho de Aguiar, 44, CEP 05403-000, São Paulo, Brazil.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 Feb 09; Vol. 10 (4), pp. e25406. Date of Electronic Publication: 2024 Feb 09 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e25406
Abstrakt:	Objective: This study aims to develop a predictive model using artificial intelligence to estimate the ICU length of stay (LOS) for Congenital Heart Defects (CHD) patients after surgery, improving care planning and resource management. Design: We analyze clinical data from 2240 CHD surgery patients to create and validate the predictive model. Twenty AI models are developed and evaluated for accuracy and reliability. Setting: The study is conducted in a Brazilian hospital's Cardiovascular Surgery Department, focusing on transplants and cardiopulmonary surgeries. Participants: Retrospective analysis is conducted on data from 2240 consecutive CHD patients undergoing surgery. Interventions: Ninety-three pre and intraoperative variables are used as ICU LOS predictors. Measurements and Main Results: Utilizing regression and clustering methodologies for ICU LOS (ICU Length of Stay) estimation, the Light Gradient Boosting Machine, using regression, achieved a Mean Squared Error (MSE) of 15.4, 11.8, and 15.2 days for training, testing, and unseen data. Key predictors included metrics such as "Mechanical Ventilation Duration", "Weight on Surgery Date", and "Vasoactive-Inotropic Score". Meanwhile, the clustering model, Cat Boost Classifier, attained an accuracy of 0.6917 and AUC of 0.8559 with similar key predictors. Conclusions: Patients with higher ventilation times, vasoactive-inotropic scores, anoxia time, cardiopulmonary bypass time, and lower weight, height, BMI, age, hematocrit, and presurgical oxygen saturation have longer ICU stays, aligning with existing literature. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Authors.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu