Evidence map›Paper›PMID 39367367›Full record

ArticleBMC pulmonary medicine2024

Comprehensive comorbidity assessment for the ECOPD: a long-term multi-centre retrospective study.

Hui Lian, Lei Kou, Xiaozhen Han, Zhu Rui, Sun Dong, Xin Zhang, Liukai Zhao, Qianyu Yue, Xiaomeng Hou, Baiqiang Cai

Abstract readMulticenter Study
In one paragraph

Article in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. A Narrative Review of the Correlation Between Comorbidity and Acute Exacerbation of COPD Patients.International journal of chronic obstructive pulmonary disease · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Hui LianDepartment of Health Care, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lei KouIndividual investigator, Cleveland, OH, USA.
Xiaozhen HanIndividual investigator, Cleveland, OH, USA.
Zhu RuiDepartment of Medical Record, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Sun DongDepartment of Geriatrics, Geriatric Diseases Institute of Chengdu, Chengdu Fifth People's Hospital, Chengdu, China.
Xin ZhangDepartment of Pulmonary and Critical Care Medicine, Hebei Chest Hospital, Shijiazhuang, China.
Liukai ZhaoDepartment of Critical Care Medicine, People's Hospital of Zhengzhou, South District, Henan, China.
Qianyu YueDepartment of Pulmonary and Critical Care Medicine, First People's Hospital of Yunnan Province, Yunnan, China.
Xiaomeng HouDepartment of Health Care, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. 13651268681@163.com.
Baiqiang CaiDepartment of Pulmonary Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. caibq2009@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExacerbation of chronic obstructive pulmonary disease (ECOPD) results in severe adverse outcomes and mortality. It is often associated with increased local and systemic inflammation. However, individual susceptibility to exacerbations remains largely unknown. Our study aimed to investigate the association between comorbidities and exacerbation outcomes.

methodsWe included patients with the primary discharge diagnosis of exacerbation for more 10 years in China. Data on all comorbidities were collected and analysed to determine the impact of the comorbidities on 1-year exacerbation readmission, length of hospital stay, and hospital cost. Univariable and multivariable logistic regression analyses were performed, and predictive models were developed.

resultsThis extensive investigation evaluated a total of 15,708 individuals from five prominent locations in China, revealing notable variations in the prevalence of comorbidities and healthcare expenses among different regions. The study shows that there is a high rate of readmission within one year, namely 15.8%. The most common conditions among readmitted patients are hypertension (38.6%), ischemic heart disease (16.9%), and diabetes mellitus (16.6%). An extensive multivariable study revealed that age, gender, and particular comorbidities such as malnutrition and hyperlipidemia are important factors that can significantly predict greater readmission rates, longer hospital stays or increased healthcare costs. The multivariable models show a moderate to good ability to predict patient outcomes, with concordance index ranging from 0.701 to 0.752. This suggests that targeted interventions in these areas could improve patient outcomes and make better use of healthcare resources.

conclusionsThe results regarding the association between severe exacerbations and systemic disease status support the integration of systematic evaluation of comorbidities into the management of exacerbations and the intensification of treatment of important comorbidities as a appropriate measure for prevention of further exacerbations. Our models also provide a novel tool for clinicians to determine the risk of the 1-year recurrence of severe ECOPD in hospitalised patients.

Indexed as

ComorbidityLength of StayPatient ReadmissionPulmonary Disease, Chronic ObstructiveAgedAged, 80 and overChinaDisease ProgressionFemaleHospital CostsHumansLogistic ModelsMaleMiddle AgedRetrospective StudiesRisk FactorsClinical prediction modelComorbiditiesExacerbation of chronic obstructive pulmonary diseasePrevention

Identifiers

PMID39367367
PMCPMC11451238

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.