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ECOPD 的全面合并症评估:一项长期多中心回顾性研究。

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

机构信息

Department of Health Care, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Individual investigator, Cleveland, OH, USA.

出版信息

BMC Pulm Med. 2024 Oct 4;24(1):487. doi: 10.1186/s12890-024-03257-6.

DOI:10.1186/s12890-024-03257-6
PMID:39367367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11451238/
Abstract

BACKGROUND

Exacerbation 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.

METHODS

We 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.

RESULTS

This 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.

CONCLUSIONS

The 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.

摘要

背景

慢性阻塞性肺疾病(COPD)加重会导致严重的不良后果和死亡率。它通常与局部和全身炎症的增加有关。然而,个体对加重的易感性在很大程度上仍然未知。我们的研究旨在探讨合并症与加重结局之间的关系。

方法

我们纳入了在中国因加重而首次出院诊断超过 10 年的患者。收集了所有合并症的数据并进行了分析,以确定合并症对 1 年加重再入院、住院时间和住院费用的影响。进行了单变量和多变量逻辑回归分析,并建立了预测模型。

结果

这项广泛的研究评估了来自中国五个著名地点的总共 15708 人,揭示了不同地区合并症的患病率和医疗费用的显著差异。研究表明,一年内再入院率很高,为 15.8%。再入院患者最常见的疾病是高血压(38.6%)、缺血性心脏病(16.9%)和糖尿病(16.6%)。广泛的多变量研究表明,年龄、性别以及营养不良和高脂血症等特定合并症是显著预测更高再入院率、更长住院时间或增加医疗费用的重要因素。多变量模型显示出对患者结局的中等至良好的预测能力,一致性指数范围为 0.701 至 0.752。这表明在这些领域进行有针对性的干预可以改善患者结局并更好地利用医疗资源。

结论

关于严重加重与系统性疾病状况之间关系的结果支持将系统性合并症评估纳入加重管理,并将重要合并症的治疗强化作为预防进一步加重的适当措施。我们的模型还为临床医生提供了一种新的工具,用于确定住院患者严重 COPD 加重 1 年内复发的风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/c67b2a0fef21/12890_2024_3257_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/984311680c22/12890_2024_3257_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/e60f448d90f5/12890_2024_3257_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/13a8d2bcc2cf/12890_2024_3257_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/4b07734348a4/12890_2024_3257_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/c67b2a0fef21/12890_2024_3257_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/984311680c22/12890_2024_3257_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/e60f448d90f5/12890_2024_3257_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/13a8d2bcc2cf/12890_2024_3257_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/4b07734348a4/12890_2024_3257_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c59/11451238/c67b2a0fef21/12890_2024_3257_Fig5_HTML.jpg

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