The analysis of risk factors associated with readmission in patients with exacerbation of COPD

This study aims to evaluate risk factors for readmission within 6 months after acute exacerbation of chronic obstructive pulmonary disease (COPD) and to develop a multifactorial predictive model. A total of 151 patients with acute exacerbation of COPD, admitted to our hospital from October 2021 to December 2023, were included in this retrospective analysis. Data on baseline characteristics, medical history, lung function, lifestyle, comorbidities, inflammatory markers, and treatment adherence were obtained from electronic medical records to identify risk factors associated with readmission. Univariate and multivariate logistic regression analyses were used to identify independent risk factors, construct a predictive model, and assess its predictive efficacy using the receiver operating characteristic (ROC) curve. Patients in the readmission group were older (69.8 ± 9.5 years vs 65.2 ± 8.4 years, P = .048), had a higher proportion of males (76.4% vs 59.5%, P = .032), higher body mass index (25.3 ± 3.8 kg/m2 vs 23.1 ± 3.2 kg/m2, P = .018), more frequent exacerbations (3.2 ± 1.0 episodes vs 1.5 ± 0.8 episodes, P = .009), longer disease duration (12.1 ± 7.3 years vs 8.4 ± 5.7 years, P = .043), and higher GOLD classification (70% in stages III-IV vs 50%, P = .043) and BODE index (5.1 ± 1.4 vs 3.8 ± 1.2, P = .022). Additionally, they had a higher prevalence of cardiovascular comorbidities (55.6% vs 31.6%, P = .015), lower FEV1 levels (45.3 ± 10.1% vs 52.7 ± 8.5%, P = .033), higher levels of C-reactive protein (CRP) (15.2 ± 6.5 mg/L vs 10.4 ± 4.9 mg/L, P = .005), fractional exhaled nitric oxide (FeNO) (32.5 ± 10.4 ppb vs 26.7 ± 8.9 ppb, P = .03), and end-tidal carbon dioxide partial pressure (PetCO2) (40.1 ± 6.3 mm Hg vs 36.2 ± 5.7 mm Hg, P = .028), all of which were significant independent risk factors for readmission. The area under the ROC curve for the multivariate regression model was 0.801, indicating good predictive efficacy. This study evaluates multiple factors affecting readmission risk after acute exacerbation of COPD, highlighting the importance of early identification of high-risk patients and constructing an effective predictive model. Further large-sample, multi-center studies are needed to validate these findings and explore personalized interventions to reduce readmission rates and improve the quality of life for COPD patients.

Authors

Institutions

Publication Details

Journal
Medicine
Published
2025-06-06
DOI
https://doi.org/10.1097/md.0000000000041997
Citations
1
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
Field-Weighted Citation Impact
1.52
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The analysis of risk factors associated with readmission in patients with exacerbation of COPD

Feng Li, Jie Li, Haibing Su
1 citations
Medicine
Chronic Obstructive Pulmonary Disease (COPD) Research
1.52
article

The analysis of risk factors associated with readmission in patients with exacerbation of COPD

Feng Li, Jie Li, Haibing Su
article en
1 citations

Abstract

This study aims to evaluate risk factors for readmission within 6 months after acute exacerbation of chronic obstructive pulmonary disease (COPD) and to develop a multifactorial predictive model. A total of 151 patients with acute exacerbation of COPD, admitted to our hospital from October 2021 to December 2023, were included in this retrospective analysis. Data on baseline characteristics, medical history, lung function, lifestyle, comorbidities, inflammatory markers, and treatment adherence were obtained from electronic medical records to identify risk factors associated with readmission. Univariate and multivariate logistic regression analyses were used to identify independent risk factors, construct a predictive model, and assess its predictive efficacy using the receiver operating characteristic (ROC) curve. Patients in the readmission group were older (69.8 ± 9.5 years vs 65.2 ± 8.4 years, P = .048), had a higher proportion of males (76.4% vs 59.5%, P = .032), higher body mass index (25.3 ± 3.8 kg/m2 vs 23.1 ± 3.2 kg/m2, P = .018), more frequent exacerbations (3.2 ± 1.0 episodes vs 1.5 ± 0.8 episodes, P = .009), longer disease duration (12.1 ± 7.3 years vs 8.4 ± 5.7 years, P = .043), and higher GOLD classification (70% in stages III-IV vs 50%, P = .043) and BODE index (5.1 ± 1.4 vs 3.8 ± 1.2, P = .022). Additionally, they had a higher prevalence of cardiovascular comorbidities (55.6% vs 31.6%, P = .015), lower FEV1 levels (45.3 ± 10.1% vs 52.7 ± 8.5%, P = .033), higher levels of C-reactive protein (CRP) (15.2 ± 6.5 mg/L vs 10.4 ± 4.9 mg/L, P = .005), fractional exhaled nitric oxide (FeNO) (32.5 ± 10.4 ppb vs 26.7 ± 8.9 ppb, P = .03), and end-tidal carbon dioxide partial pressure (PetCO2) (40.1 ± 6.3 mm Hg vs 36.2 ± 5.7 mm Hg, P = .028), all of which were significant independent risk factors for readmission. The area under the ROC curve for the multivariate regression model was 0.801, indicating good predictive efficacy. This study evaluates multiple factors affecting readmission risk after acute exacerbation of COPD, highlighting the importance of early identification of high-risk patients and constructing an effective predictive model. Further large-sample, multi-center studies are needed to validate these findings and explore personalized interventions to reduce readmission rates and improve the quality of life for COPD patients.

MedicineVol. 104(23)
Jingzhou Central Hospital (CN), Jingning County People's Hospital (CN)
Good health and well-being
Openalex Percentile: Top 18%
Chronic Obstructive Pulmonary Disease (COPD) Research
1.52
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.