Computer-based models to identify high-risk children with asthma
Computer-based models to identify high-risk children with asthma
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DOI：
10.1164/ajrccm.157.4.9708124
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发表时间：
1998-04-01
影响因子：
24.7
通讯作者：
Leong, AB
中科院分区：
文献类型：
--
作者：
Lieu, TA;Quesenberry, CP;Leong, AB
对哮喘人群的有效管理需要有方法来识别具有不良后果的高风险患者。这项研究的目的是开发和验证预测模型，该模型使用来自大型健康维护组织(HMO)的计算机化使用数据来预测哮喘相关的住院和急诊科(ED)就诊。在这项采用分裂样本验证的回溯性队列设计中，基线年的变量被用来预测16520名哮喘相关使用儿童在随访一年中与哮喘相关的不良结果。在比例风险模型中，在过去6个月内服用过口服类固醇处方(相对危险度：1.9；95%可信区间[CI]：1.3至2.8)或曾住院(相对危险度：1.7；95%可信区间：1.1至2.7)，以及没有在电脑上登记的私人医生(相对危险度：1.6；95%可信区间：1.1至2.3)与未来住院风险增加相关。分类树确定了以前的住院和急诊，前6个月内6个或更多的β-激动剂吸入剂(单位)，以及前6个月内3个或更多开哮喘药物的内科医生作为预测因素。分类树的表现类似于比例风险模型，并确定了比普通患者住院风险高三倍、急诊风险高两倍的患者。我们的结论是，基于计算机的预测模型可以识别哮喘预后不良的高危儿童，并可能在改善哮喘管理的基于人群的努力中有用。
Effective management of populations with asthma requires methods for identifying patients at high risk for adverse outcomes. The aim of this study was to develop and validate prediction models that used computerized utilization data from a large health-maintenance organization (HMO) to predict asthma-related hospitalization and emergency department (ED) visits. In this retrospective cohort design with split-sample validation, variables from the baseline year were used to predict asthma-related adverse outcomes during the follow-up year for 16,520 children with asthma-related utilization. In proportional-hazard models, having filled an oral steroid prescription (relative risk [RR]: 1.9; 95% confidence interval [CI]: 1.3 to 2.8) or having been hospitalized (RR: 1.7; 95% CI: 1.1 to 2.7) during the prior 6 mo, and not having a personal physician listed on the computer (RR: 1.6; 95% CI: 1.1 to 2.3) were associated with increased risk of future hospitalization. Classification trees identified previous hospitalization and ED visits, six or more beta-agonist inhalers (units) during the prior 6 mo, and three or more physicians prescribing asthma medications during the prior 6 mo as predictors. The classification trees performed similarly to proportional-hazards models, and identified patients who had a threefold greater risk of hospitalization and a twofold greater risk of ED visits than the average patient. We conclude that computer-based prediction models can identify children at high risk for adverse asthma outcomes, and may be useful in population-based efforts to improve asthma management.