Construction of a risk prediction model for in-hospital mortality in patients with acute exacerbation of chronic obstructive pulmonary disease

The nomogram based on mechanical ventilation, disease duration, admission condition, cor pulmonale, and heart failure exhibits excellent predictive performance for in-hospital mortality in AECOPD patients, providing a valuable tool for clinical decision-making.

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Publication Details

Journal
European journal of medical research
Published
2026-01-21
DOI
https://doi.org/10.1186/s40001-026-03885-0
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
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article

Construction of a risk prediction model for in-hospital mortality in patients with acute exacerbation of chronic obstructive pulmonary disease

Chunli Huang, Changguang Ye, Jieyun Zhu, Lu Zhao et al.
European journal of medical research
Chronic Obstructive Pulmonary Disease (COPD) Research
article

Construction of a risk prediction model for in-hospital mortality in patients with acute exacerbation of chronic obstructive pulmonary disease

Chunli Huang, Changguang Ye, Jieyun Zhu, Lu Zhao, Yin Shen, Qiuyun Song, Dongzan Pan, Zhaoqiang Cai, QiaoyanWang
article en

Abstract

The nomogram based on mechanical ventilation, disease duration, admission condition, cor pulmonale, and heart failure exhibits excellent predictive performance for in-hospital mortality in AECOPD patients, providing a valuable tool for clinical decision-making.

European journal of medical researchVol. 31(1)
Zhuhai People's Hospital (CN), Guangxi University of Chinese Medicine (CN), The People's Hospital of Guangxi Zhuang Autonomous Region (CN), Qihe People's Hospital (CN)
Good health and well-being
Openalex Percentile: Top 81%
Chronic Obstructive Pulmonary Disease (COPD) Research
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Construction of a risk prediction model for in-hospital mortality in patients with acute exacerbation of chronic obstructive pulmonary disease — Chunli Huang, Changguang Ye, et al. · European journal of medical research (2026) | TGRS Research Map | TGRS