Evaluation of Risk Scores to Predict Pediatric Severe Asthma Exacerbations.

医学 哮喘 恶化 肺活量测定 队列 病历 风险评估 哮喘恶化 儿科 人口 急诊医学
作者
Chao Niu,Yuanfang Xu,Christine L. Schuler,Lijuan Gu,Kavisha Arora,Yunjie Huang,Anjaparavanda P. Naren,Sandy R. Durrani,Monir Hossain,Theresa W. Guilbert
出处
期刊:The Journal of Allergy and Clinical Immunology: In Practice [Elsevier]
卷期号:9 (12): 4393-4401.e8
标识
DOI:10.1016/j.jaip.2021.08.030
摘要

Asthma exacerbations commonly lead to unplanned health care utilization and are costly. Early identification of children at increased risk of asthma exacerbations would allow a proactive management approach.We evaluated common asthma risk factors to predict the probability of exacerbation for individual children aged 0-21 years using data from the electronic medical record (EMR).We analyzed longitudinal EMR data for over 3000 participants with asthma seen at Cincinnati Children's Hospital Medical Center over a 7-year period. The study population was divided into 3 age groups: 0-4, 5-11, and 12-21 years. Each age group was divided into a derivation cohort and a validation cohort, which were used to build a risk score model. We predicted risk of exacerbation in the next 12 months, validated the scores by risk stratum, and developed a clinical tool to determine the risk level based on this model.Risk model results were confirmed with validation cohorts by calendar year and age groups. Race, allergic sensitization, and smoke exposure were each important risk factors in the 0-4 age group. Abnormal spirometry and obesity were more sensitive predictors of exacerbation in children >12 years. For each age group, a higher expanded score was associated with a higher predicted probability of an asthma exacerbation in the subsequent year.This asthma exacerbation prediction model, and the associated clinical tool, may assist clinicians in identifying children at high risk for exacerbation that may benefit from more aggressive management and targeted risk mitigation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ononon发布了新的文献求助10
2秒前
2秒前
liu完成签到,获得积分10
4秒前
LWJ发布了新的文献求助10
5秒前
6秒前
大反应釜完成签到,获得积分10
6秒前
TT发布了新的文献求助10
9秒前
Jenny发布了新的文献求助10
11秒前
11秒前
完美凝竹发布了新的文献求助10
11秒前
我是站长才怪应助细腻沅采纳,获得10
12秒前
JG完成签到 ,获得积分10
12秒前
hhh完成签到,获得积分20
12秒前
科研通AI5应助想瘦的海豹采纳,获得10
13秒前
随性完成签到 ,获得积分10
13秒前
自由的信仰完成签到,获得积分10
14秒前
16秒前
17秒前
17秒前
夏夏发布了新的文献求助10
18秒前
打打应助Hangerli采纳,获得10
20秒前
完美凝竹完成签到,获得积分10
21秒前
zfzf0422发布了新的文献求助10
22秒前
蜘蛛道理完成签到 ,获得积分10
22秒前
冷傲迎梦发布了新的文献求助10
23秒前
852应助MEME采纳,获得10
23秒前
Godzilla发布了新的文献求助10
23秒前
大模型应助咕噜仔采纳,获得10
24秒前
蒋时晏应助pharmstudent采纳,获得30
24秒前
25秒前
忘羡222发布了新的文献求助20
26秒前
魏伯安发布了新的文献求助10
26秒前
27秒前
不爱吃糖完成签到,获得积分10
27秒前
28秒前
balabala发布了新的文献求助10
29秒前
睿123456完成签到,获得积分10
30秒前
此话当真完成签到,获得积分10
31秒前
33秒前
慕青应助wmmm采纳,获得10
34秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527990
求助须知:如何正确求助?哪些是违规求助? 3108173
关于积分的说明 9287913
捐赠科研通 2805882
什么是DOI,文献DOI怎么找? 1540119
邀请新用户注册赠送积分活动 716941
科研通“疑难数据库(出版商)”最低求助积分说明 709824