Clinical Characteristics of Overweight Patients With Acute Exacerbation Chronic Obstructive Pulmonary Disease (AECOPD)

IF 1.9 4区 医学 Q3 RESPIRATORY SYSTEM
Yuxin Gong, Fawang Du, Yu Yao, Hanchao Wang, Xiaochuan Wang, Wei Xiong, Qin Wang, Gaoyan He, Linlin Chen, Heng Du, Juan Yang, Brent A. Bauer, Zhongruo Wang, Huojin Deng, Tao Zhu
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Abstract

Introduction

Low body weight in patients with COPD is associated with a poor prognosis and more comorbidities. However, the impact of increased body weight in patients with COPD remains controversial. The aim of this study was to explore the clinical features of overweight patients with AECOPD.

Methods

In this multicenter cross-sectional study, a total of 647 AECOPD patients were recruited. Finally, 269 normal weight and 162 overweight patients were included. Baseline characteristics and clinical and laboratory data were collected. The least absolute shrinkage and selection operator (LASSO) regression was performed to determine potential features, which were substituted into binary logistic regression to reveal overweight-associated clinical features. The nomogram and its associated curves were established to visualize and verify the logistic regression model.

Results

Six potential overweight-associated variables were selected by LASSO regression. Subsequently, a binary logistic regression model identified that the rates of type 2 diabetes (T2DM) and hypertension and levels of lymphocytes (LYM)%, and alanine aminotransferase (ALT) were independent variables of overweight in AECOPD patients. The C-index and AUC of the ROC curve of the nomogram were 0.671 and 0.666, respectively. The DCA curve revealed that the nomogram had more clinical benefits if the threshold was at a range of 0.22~0.78.

Conclusions

Collectively, we revealed that T2DM and hypertension were more common, and LYM% and ALT were higher in AECOPD patients with overweight than those with normal weight. The result suggests that AECOPD patients with overweight are at risk for additional comorbidities, potentially leading to worse outcomes.

Abstract Image

体重超标的急性加重期慢性阻塞性肺病 (AECOPD) 患者的临床特征。
导言:慢性阻塞性肺病患者体重过轻与预后不良和合并症增多有关。然而,体重增加对慢性阻塞性肺病患者的影响仍存在争议。本研究旨在探讨超重的 AECOPD 患者的临床特征:在这项多中心横断面研究中,共招募了 647 名 AECOPD 患者。最后,纳入了 269 名正常体重和 162 名超重患者。研究人员收集了基线特征、临床和实验室数据。通过最小绝对收缩和选择算子(LASSO)回归确定潜在特征,并将其代入二元逻辑回归以揭示与超重相关的临床特征。建立了提名图及其相关曲线,以直观显示和验证逻辑回归模型:结果:通过 LASSO 回归筛选出六个潜在的超重相关变量。随后,二元逻辑回归模型发现,2 型糖尿病(T2DM)和高血压发病率、淋巴细胞(LYM)% 和丙氨酸氨基转移酶(ALT)水平是 AECOPD 患者超重的独立变量。提名图 ROC 曲线的 C 指数和 AUC 分别为 0.671 和 0.666。DCA曲线显示,如果阈值在0.22~0.78之间,提名图的临床获益更大:综上所述,我们发现超重的 AECOPD 患者比体重正常者更常见 T2DM 和高血压,LYM% 和 ALT 也更高。这一结果表明,超重的 AECOPD 患者有可能出现更多合并症,从而导致更差的预后。
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来源期刊
Clinical Respiratory Journal
Clinical Respiratory Journal 医学-呼吸系统
CiteScore
3.70
自引率
0.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: Overview Effective with the 2016 volume, this journal will be published in an online-only format. Aims and Scope The Clinical Respiratory Journal (CRJ) provides a forum for clinical research in all areas of respiratory medicine from clinical lung disease to basic research relevant to the clinic. We publish original research, review articles, case studies, editorials and book reviews in all areas of clinical lung disease including: Asthma Allergy COPD Non-invasive ventilation Sleep related breathing disorders Interstitial lung diseases Lung cancer Clinical genetics Rhinitis Airway and lung infection Epidemiology Pediatrics CRJ provides a fast-track service for selected Phase II and Phase III trial studies. Keywords Clinical Respiratory Journal, respiratory, pulmonary, medicine, clinical, lung disease, Abstracting and Indexing Information Academic Search (EBSCO Publishing) Academic Search Alumni Edition (EBSCO Publishing) Embase (Elsevier) Health & Medical Collection (ProQuest) Health Research Premium Collection (ProQuest) HEED: Health Economic Evaluations Database (Wiley-Blackwell) Hospital Premium Collection (ProQuest) Journal Citation Reports/Science Edition (Clarivate Analytics) MEDLINE/PubMed (NLM) ProQuest Central (ProQuest) Science Citation Index Expanded (Clarivate Analytics) SCOPUS (Elsevier)
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