Intermediate care units and their role in medical wards

Duarte, Joana Azevedo; Ribeiro, Raquel; Melo, Luis Alberto Martins Palhares de; Furtado, A H; Henriques, Célia

doi:10.1016/j.ejim.2017.08.005

Cited by 2 publications

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients residing in an intermediate care facility had worse outcomes than those who resided independently at home, when they developed ARDS. This is not unique to ARDS, but had been reported in various conditions, such as ischemic colitis and pulmonary conditions (Peixoto et al, 2017; Duarte et al, 2017). However, most prediction models for ARDS failed to incorporate this factor (Frenzel et al, 2011; Zhang & Ni, 2015; Balzer et al, 2016; Luo et al, 2017), which might be responsible for their lack of satisfactory accuracy.…”

Section: Discussionmentioning

confidence: 71%

Prediction model for patients with acute respiratory distress syndrome: use of a genetic algorithm to develop a neural network model

Zhang

2019

PeerJ

View full text Add to dashboard Cite

Background Acute respiratory distress syndrome (ARDS) is associated with significantly increased risk of death, and early risk stratification may help to choose the appropriate treatment. The study aimed to develop a neural network model by using a genetic algorithm (GA) for the prediction of mortality in patients with ARDS. Methods This was a secondary analysis of two multicenter randomized controlled trials conducted in forty-four hospitals that are members of the National Heart, Lung, and Blood Institute, founded to create an acute respiratory distress syndrome Clinical Trials Network. Model training and validation were performed using the SAILS and OMEGA studies, respectively. A GA was employed to screen variables in order to predict 90-day mortality, and a neural network model was trained for the prediction. This machine learning model was compared to the logistic regression model and APACHE III score in the validation cohort. Results A total number of 1,071 ARDS patients were included for analysis. The GA search identified seven important variables, which were age, AIDS, leukemia, metastatic tumor, hepatic failure, lowest albumin, and FiO2. A representative neural network model was constructed using the forward selection procedure. The area under the curve (AUC) of the neural network model evaluated with the validation cohort was 0.821 (95% CI [0.753–0.888]), which was greater than the APACHE III score (0.665; 95% CI [0.590–0.739]; p = 0.002 by Delong’s test) and logistic regression model, albeit not statistically significant (0.743; 95% CI [0.669–0.817], p = 0.130 by Delong’s test). Conclusions The study developed a neural network model using a GA, which outperformed conventional scoring systems for the prediction of mortality in ARDS patients.

show abstract