Suppr超能文献

非故意损伤:中国北京市住院患者特征及院内死亡危险因素分析。

Unintentional injuries: A profile of hospitalization and risk factors for in-hospital mortality in Beijing, China.

机构信息

Department of Medical Records, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Collaborating Center for the WHO Family of International Classifications, Beijing, China; National Center for Quality Control of Medical Records, Beijing, China.

Beijing Municipal Commission of Health and Family Planning Information Center, Beijing, China.

出版信息

Injury. 2019 Mar;50(3):663-670. doi: 10.1016/j.injury.2019.01.029. Epub 2019 Jan 18.

Abstract

INTRODUCTION

Unintentional injuries (UIs) impose a significant burden on low- and middle-income countries (LMICs). However, available UI epidemiological data are limited for LMICs, including China. This article aimed to provide an overview of the UI hospitalization profile, identify risk factors for in-hospital mortality and provide diagnosis-specific survival risk ratios (SRRs) for reference by LMICs using hospital discharge abstract data (DAD) from Beijing, China.

PATIENTS AND METHODS

A cross-sectional study was conducted for patients sustaining UIs requiring admission. Information was retrieved from 138 hospitals in Beijing to describe the demographics, injury nature, mechanisms, severity and hospital outcomes. Multivariate logistic regression was performed to identify and evaluate risk factors for in-hospital mortality for UIs.

RESULTS

Falls (57.1%), transport accidents (19.9%) and exposure to inanimate mechanical forces (16.4%) were the leading causes of UI hospitalization. Falls and transport accidents were responsible for 94.2% of the in-hospital deaths caused by UIs. Injury mechanisms differed among sex (χ = 5322.1, P <  0.001) and age (χ = 24,143.3, P <  0.001) groups. Male sex (OR: 1.50, 95% confidence interval (CI): 1.23-1.79), age ≥ 85 years (OR: 16.39, 95% CI: 7.46-36.00), Barthel Index at admission ≤ 60 (OR: 25.78, 95% CI: 13.30-49.95), modified Charlson comorbidity index ≥ 6 (OR: 2.60, 95% CI: 1.91-3.55), International Classification of Diseases-based injury severity score (ICISS) < 0.85 (OR: 15.17, 95% CI: 12.57-18.30), sustaining injuries to the head/neck (OR: 23.20, 95% CI: 7.31-73.64), injuries caused by foreign body entering through natural orifice (OR: 34.00, 95%CI: 6.37-181.54) and injuries resulting from transport accidents (OR: 1.71, 95% CI: 1.41-2.07) were important risk factors for in-hospital mortality for UIs.

CONCLUSIONS

Hospital DAD are an objective and cost-effective data source that allows for a hospital-based perspective of UI epidemiology. Sex, age, functional status at admission, comorbidities, injury nature, severity and mechanism are significantly associated with the in-hospital mortality of UIs in China. This study generates a reference dataset of diagnosis-specific SRRs from a large trauma population in China, which may be more applicable in injury severity estimation using ICISS in LMICs.

摘要

简介

意外伤害(UI)给低收入和中等收入国家(LMICs)带来了巨大负担。然而,针对 LMICs 的 UI 流行病学数据有限,包括中国。本文旨在利用中国北京的医院出院摘要数据(DAD),提供 UI 住院概况概述,确定院内死亡的危险因素,并提供特定诊断的生存率风险比(SRR)供 LMICs 参考。

患者和方法

对需要住院的 UI 患者进行了横断面研究。从北京的 138 家医院检索信息,描述人口统计学、损伤性质、机制、严重程度和医院结局。采用多变量逻辑回归识别和评估 UI 院内死亡的危险因素。

结果

跌倒(57.1%)、交通意外(19.9%)和接触无生命机械力(16.4%)是 UI 住院的主要原因。跌倒和交通意外导致 94.2%的 UI 院内死亡。损伤机制在性别(χ=5322.1,P<0.001)和年龄(χ=24,143.3,P<0.001)组之间存在差异。男性(OR:1.50,95%置信区间(CI):1.23-1.79)、年龄≥85 岁(OR:16.39,95%CI:7.46-36.00)、入院时巴氏量表评分≤60(OR:25.78,95%CI:13.30-49.95)、改良 Charlson 合并症指数≥6(OR:2.60,95%CI:1.91-3.55)、国际疾病分类基于损伤严重度评分(ICISS)<0.85(OR:15.17,95%CI:12.57-18.30)、头部/颈部受伤(OR:23.20,95%CI:7.31-73.64)、异物通过自然孔道进入引起的损伤(OR:34.00,95%CI:6.37-181.54)和交通意外引起的损伤(OR:1.71,95%CI:1.41-2.07)是 UI 院内死亡的重要危险因素。

结论

医院 DAD 是一种客观且具有成本效益的数据源,可从医院角度了解 UI 流行病学。性别、年龄、入院时的功能状态、合并症、损伤性质、严重程度和机制与中国 UI 院内死亡率显著相关。本研究从中国大型创伤人群中生成了特定诊断的 SRR 参考数据集,这可能更适用于 LMICs 中使用 ICISS 进行损伤严重程度估计。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验