Evidence map›Paper›PMID 40114423›Full record

Observational studyChronic respiratory disease

Clinical characteristics and prognosis prediction in patients with AECOPD and type 2 diabetes mellitus: A multicenter observational study.

Xiaoqian Li, Qun Yi, Yuanming Luo, Hailong Wei, Huiqing Ge, Huiguo Liu, Jianchu Zhang, Xianhua Li, Xiufang Xie, Pinhua Pan and 9 more

Abstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Chronic respiratory disease. 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. Observational
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

19 authors.

Xiaoqian LiDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Qun YiDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Yuanming LuoState Key Laboratory of Respiratory Disease, Guangzhou Medical University, Guangzhou, Guangdong Province, China.
Hailong WeiDepartment of Respiratory and Critical Care Medicine, People's Hospital of Leshan, Leshan, Sichuan Province, China.
Huiqing GeDepartment of Respiratory and Critical Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China.
Huiguo LiuDepartment of Respiratory and Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
Jianchu ZhangDepartment of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China.
Xianhua LiDepartment of Respiratory and Critical Care Medicine, The First People's Hospital of Neijiang City, Neijiang, Sichuan Province, China.
Xiufang XieDepartment of Respiratory and Critical Care Medicine, The First People's Hospital of Neijiang City, Neijiang, Sichuan Province, China.
Pinhua PanDepartment of Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
Mengqiu YiDepartment of Emergency, First People's Hospital of Jiujiang, Jiujiang, Jiangxi Province, China.
Lina ChengDepartment of Emergency, First People's Hospital of Jiujiang, Jiujiang, Jiangxi Province, China.
Hui ZhouDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Chengdu University, Chengdu, Sichuan Province, China.
Liang LiuDepartment of Respiratory and Critical Care Medicine, The Affiliated Hospital of Chengdu University, Chengdu, Sichuan Province, China.
Chen ZhouCenter of Infectious Diseases, Division of Infectious Diseases in State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Jiarui ZhangDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Lige PengDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Jiaqi PuDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
Haixia ZhouDepartment of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.ORCID 0000-0002-5205-4487

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ObjectivesDiabetes is a common comorbidity in COPD population. This study aimed to explore the impacts of T2DM on clinical characteristics and outcomes of patients with exacerbation of COPD, as well as develop a specified prognostic model for these patients.MethodsAECOPD patients were enrolled from a prospective, noninterventional, multicenter cohort study. Propensity score matching with a 1:2 ratio was performed to compare the characteristics and prognosis between patients with and without T2DM. Predictors for short-term mortality were determined by logistic regression analysis and a prediction nomogram were established and further validated in another cohort.ResultsA total of 1804 AECOPD patients with T2DM and 3608 matched patients without T2DM were included. AECOPD patients with T2DM presented with worse disease profile and prognosis. Eight independent predictors for short-term mortality were determined, including advanced age, disturbance of consciousness, chronic cardiac disease, low blood pressure, high heart rate, elevated neutrophil, urea nitrogen and random blood glucose. A prognostic nomogram was established with an AUC of 0.878 (95%CI: 0.842-0.915) in derivation cohort and 0.834 (95% CI: 0.767-0.901) in validation cohort, which was superior to DECAF (0.647 [95%CI: 0.535-0.760]) and BAP-65 score (0.758 [95%CI: 0.666-0.850]). The calibration curve and decision curve analysis also indicated its accuracy and applicability. Besides, a web calculator based on the nomogram was constructed to simplify the use of prognostic nomogram in clinical practice.ConclusionsComorbid diabetes is significantly associated with severe disease profile and worse prognosis in AECOPD population. Our nomogram may help to facilitate early risk assessment and proper decision-making among patients with AECOPD and T2DM.

Indexed as

Diabetes Mellitus, Type 2Pulmonary Disease, Chronic ObstructiveAgedComorbidityDisease ProgressionFemaleHumansMaleMiddle AgedNomogramsPrognosisPropensity ScoreProspective StudiesRisk AssessmentRisk FactorsAECOPDprognostic modelType 2 diabetes mellitus

Identifiers

PMID40114423
PMCPMC11926837

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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.