已发表论文

稳定期慢性阻塞性肺疾病(COPD)患者临床特征与肠道菌群失衡的相关性及早期预警模型的构建

 

Authors Zeng X, Yang H, Yang Y, Gu X, Ma X, Zhu T

Received 24 July 2021

Accepted for publication 29 November 2021

Published 18 December 2021 Volume 2021:16 Pages 3417—3428

DOI https://doi.org/10.2147/COPD.S330976

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Zhang

Objective: Establish a simple predictive model and scoring rule that is suitable for clinical medical staff in respiratory departments to assess intestinal flora imbalance occurrence in stable chronic obstructive pulmonary disease (COPD) patients.
Methods: From January 1, 2019, to December 31, 2020, COPD patients (195 cases) – who attended the Outpatient Department, Respiratory and Critical Care, Yixing Hospital, Jiangsu University – were enrolled in a cross-sectional study. Based on stool examination results, patients were divided into experimental (41 cases) and control (154 cases) groups. Single-factor and logistic regression analyses were performed with the baseline data of the two groups to obtain a new predictive model, which was further simplified.
Results: Five predictive factors composed the model: body mass index (BMI), serum albumin (ALB), Charlson’s Comorbidity Index (CCI), gastrointestinal symptom score (GSRs), and Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. The model to predict intestinal flora imbalance in stable COPD patients had an area under the ROC curve (AUC) of 0.953 [95% CI (0.924, 0.982)]. After simplifying the scoring rules, the AUC was 0.767 [95% CI (0.676, 0.858)].
Conclusion: In the current study, we obtained a model that could effectively predict intestinal flora imbalance risk in stable COPD patients, being suitable for implementation in early treatments to improve the prognosis. Moreover, all indicators can be easily and simply obtained.
Keywords: lung disease, chronic obstruction, early warning score, gastrointestinal tract, flora imbalance, predictive model