Chang Junior João, Caneo Luiz Fernando, Turquetto Aida Luiza Ribeiro, Amato Luciana Patrick, Arita Elisandra Cristina Trevisan Calvo, Fernandes Alfredo Manoel da Silva, Trindade Evelinda Marramon, Jatene Fábio Biscegli, Dossou Paul-Eric, Jatene Marcelo Biscegli
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.
Heliyon. 2024 Feb 9;10(4):e25406. doi: 10.1016/j.heliyon.2024.e25406. eCollection 2024 Feb 29.
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.
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.
The study is conducted in a Brazilian hospital's Cardiovascular Surgery Department, focusing on transplants and cardiopulmonary surgeries.
Retrospective analysis is conducted on data from 2240 consecutive CHD patients undergoing surgery.
Ninety-three pre and intraoperative variables are used as ICU LOS predictors.
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.
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.
本研究旨在开发一种使用人工智能的预测模型，以估计先天性心脏病（CHD）患者术后在重症监护病房（ICU）的住院时间（LOS），改善护理计划和资源管理。
我们分析了2240例CHD手术患者的临床数据，以创建和验证预测模型。开发并评估了20个人工智能模型的准确性和可靠性。
该研究在巴西一家医院的心血管外科进行，重点是移植和心肺手术。
对2240例连续接受手术的CHD患者的数据进行回顾性分析。
93个术前和术中变量被用作ICU住院时间的预测指标。
利用回归和聚类方法估计ICU住院时间，基于回归的Light Gradient Boosting Machine在训练、测试和未知数据上的均方误差（MSE）分别为15.4天、11.8天和15.2天。关键预测指标包括“机械通气持续时间”、“手术日期体重”和“血管活性药物-正性肌力评分”等指标。同时，聚类模型Cat Boost Classifier在类似的关键预测指标下，准确率达到0.6917，曲线下面积（AUC）为0.8559。
通气时间、血管活性药物-正性肌力评分、缺氧时间、体外循环时间较长，以及体重、身高、BMI、年龄、血细胞比容和术前氧饱和度较低的患者，在ICU的住院时间较长，这与现有文献一致。
