Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study.
Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study.
复制标题
预测未来哮喘患者在医院就诊的学术卫生保健系统：预测模型开发和二次分析研究。
DOI：
10.2196/22796
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发表时间：
2021-04-16
影响因子：
7.4
通讯作者：
Luo G
中科院分区：
文献类型：
--
作者：
Tong Y;Messinger AI;Wilcox AB;Mooney SD;Davidson GH;Suri P;Luo G
哮喘影响了很大一部分人口，并导致许多医院的遭遇，涉及住院和急诊科每年的访问。为了减少这种遭遇的数量，许多医疗保健系统和健康计划部署预测模型来前瞻性地识别高风险患者，并为他们提供预防护理的护理管理服务。然而，以前的模型没有足够的准确性，很好地服务于这一目的。采用检查许多候选特征的建模策略，我们建立了一个新的机器学习模型来预测Intermountain Healthcare（一个非学术医疗保健系统）哮喘患者未来在哮喘医院的遭遇。该模型比以前发布的模型更准确。然而，目前还不清楚我们的建模策略如何推广到学术医疗保健系统，其患者组成与Intermountain Healthcare不同。本研究的目的是评估我们的建模策略，以华盛顿医学大学（UWM），学术卫生保健系统的普遍性。所有在2011年至2018年期间访问UWM设施的成人哮喘患者均作为患者队列。我们考虑了234个候选特征。通过对2011年至2018年的82，888个UWM数据实例的二次分析，我们建立了一个机器学习模型来预测哮喘患者在随后12个月内的哮喘医院就诊情况。我们的UWM模型产生的受试者工作特征曲线下面积（AUC）为0.902。当把二元分类的分界点放在前10%时，在预测风险最大的哮喘患者中，我们的UWM模型的准确性为90.6%（13，268/14，644），敏感性为70.2%（153/218），特异性为90.91%（13，115/14，426）。我们的建模策略显示出对UWM的良好的概括性，导致模型的AUC高于文献中先前报道的用于预测哮喘医院就诊的所有AUC。经过进一步的优化，我们的模型可以用来促进哮喘护理管理资源的高效和有效的分配，以改善结果。RR2-10.2196/resprot.5039
Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare. This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system. All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months. Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426). Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes. RR2-10.2196/resprot.5039
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影响因子：
2.5
作者：
Kern, Lisa M.;Grinspan, Zachary;Kaushal, Rainu
通讯作者：
Kaushal, Rainu
影响因子：
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作者：
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DOI：
10.1164/ajrccm.157.4.9708124
发表时间：
1998-04-01
影响因子：
24.7
作者：
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通讯作者：
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影响因子：
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作者：
Luo, Gang
通讯作者：
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影响因子：
14.2
作者：
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通讯作者：
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