Publikasi Scopus FKUI 2021 per tanggal 31 Mei 2021 (358 artikel)

Amelia V., Siswantining T., Kamelia T.
57221954910;57193446800;35603752000;
Prediction model of exacerbations in patients with Chronic Obstructive Pulmonary Disease (COPD) at RSCM
2021
Journal of Physics: Conference Series
1725
1
012011
Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Division of Pulmonology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia
Amelia, V., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Siswantining, T., Department of Mathematics, Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Indonesia, Depok, 16424, Indonesia; Kamelia, T., Division of Pulmonology, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Jakarta, 10430, Indonesia
Chronic Obstructive Pulmonary Disease (COPD) is a worldwide health problem. COPD has a tendency for exacerbations. Exacerbations are worsening of acute respiratory symptoms resulting in additional therapy. Exacerbations in COPD increase the risk of death. The objective of this study is to determine the prediction model of exacerbations in patients with COPD based on factors affecting exacerbations in patients with COPD at RSCM (Rumah Sakit Cipto Mangunkusumo). The data used in this study is secondary data from the medical records of patients with COPD in RSCM. The sample was chosen using purposive sampling technique. The samples in this study are 107 patients with COPD. The method used is binary logistic regression analysis. The results of this study indicate that the factors that significantly influence the exacerbations of COPD are breathlessness, history of ICS use, and history of antibiotics use. Appropriate logistic regression model has been obtained. The result indicates that patients with COPD who have breathlessness, have history of ICS use, and have history of antibiotics use are more at risk of exacerbations than those who don't. Accuracy test has been conducted with classification table at cut point 0.5. The prediction model has an accuracy rate of 74.77 %. © 2021 Journal of Physics: Conference Series.
COPD; Exacerbations; Logistic regression
Antibiotics; Forecasting; Logistic regression; Predictive analytics; Binary logistic regression; Chronic obstructive pulmonary disease; Logistic Regression modeling; Medical record; Prediction model; Respiratory symptoms; Sampling technique; Secondary datum; Pulmonary diseases
IOP Publishing Ltd
17426588
Conference Paper
Q3
227
17171