English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background  To investigate the risk factors and construct a logistic model and an extreme gradient boosting (XGBoost) model to compare the predictive performances for readmission in acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients within one year.    Methods  In total, 636 patients with AECOPD were recruited and divided into readmission group (n = 449) and non-readmission group (n = 187). Backward stepwise regression method was used to analyze the risk factors for readmission. Data were divided into training set and testing set at a ratio of 7:3. Variables with statistical significance were included in the logistic model and variables with  P  < 0.1 were included in the XGBoost model, and receiver operator characteristic (ROC) curves were plotted.    Results  Patients with acute exacerbations within the previous 1 year [odds ratio (OR) = 4.086, 95% confidence interval (CI) 2.723–6.133,  P  < 0.001), long-acting β agonist (LABA) application (OR = 4.550, 95% CI 1.587–13.042,  P  = 0.005), inhaled corticosteroids (ICS) application (OR = 0.227, 95% CI 0.076–0.672,  P  = 0.007), glutamic-pyruvic transaminase (ALT) level (OR = 0.985, 95% CI 0.971–0.999,  P  = 0.042), and total CAT score (OR = 1.091, 95% CI 1.048–1.136,  P  < 0.001) were associated with the risk of readmission. The AUC value of the logistic model was 0.743 (95% CI 0.692–0.795) in the training set and 0.699 (95% CI 0.617–0.780) in the testing set. The AUC value of XGBoost model was 0.814 (95% CI 0.812–0.815) in the training set and 0.722 (95% CI 0.720–0.725) in the testing set.    Conclusions  The XGBoost model showed a better predictive value in predicting the risk of readmission within one year in the AECOPD patients than the logistic regression model. The findings of our study might help identify patients with a high risk of readmission within one year and provide timely treatment to prevent the reoccurrence of AECOPD.    Supplementary Information  The online version contains supplementary material available at 10.1186/s12890-021-01692-3.

Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge