Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.
Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.
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DOI：
10.2196/43017
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发表时间：
2023-05-17
影响因子：
8.5
通讯作者：
Ridgway, Jessica P.
中科院分区：
文献类型：
--
作者：
Mason, Joseph A.;Friedman, Eleanor E.;Devlin, Samantha A.;Schneider, John A.;Ridgway, Jessica P.
关键词：
减少对艾滋病毒感染者的护理失误对于结束艾滋病毒流行至关重要，并有利于他们的健康。预测建模可以识别与HIV护理失误相关的临床因素。以前的研究已经确定了这些因素在一个单一的诊所或使用一个全国性的诊所网络，但公共卫生战略，以提高在美国的护理保留往往发生在一个区域管辖范围内（例如，一个城市或县）。我们试图利用伊利诺斯州芝加哥的一个大型、多站点、非策展的电子健康记录（EHR）数据库建立艾滋病毒护理失误的预测模型。我们使用了来自芝加哥地区以患者为中心的结果研究网络（CAPriCORN）的2011-2019年数据，该数据库包括多个卫生系统，涵盖了生活在芝加哥的23，580名艾滋病毒诊断者中的大多数。CAPriCORN使用基于散列的重复数据删除方法来跟踪具有不同EHR的多个芝加哥医疗保健系统中的人员，提供了一个独特的全市艾滋病毒护理保留视图。从数据库中，我们使用诊断代码，药物，实验室测试，人口统计学和遭遇信息来建立预测模型。我们的主要结果是艾滋病毒护理失误，定义为随后的艾滋病毒护理接触之间超过12个月。我们使用所有变量构建了逻辑回归，随机森林，弹性净逻辑回归和XGBoost模型，并将其性能与仅包含人口统计学和保留历史的基线逻辑回归模型进行了比较。我们在数据库中纳入了至少有2次艾滋病毒护理接触的艾滋病毒感染者，得到了16，930名艾滋病毒感染者的191，492次接触。所有模型均优于基线logistic回归模型，XGBoost模型的改善最大（受试者工作特征曲线下面积0.776，95% CI 0.768-0.784 vs 0.674，95% CI 0.664-0.683; P<0.001）。最重要的预测因素包括护理失误的历史，被传染病提供者看到（与初级保健提供者相比），护理地点，西班牙裔种族和以前的艾滋病毒实验室检测。随机森林模型（受试者工作特征曲线下面积0.751，95%CI 0.742-0.759）显示年龄、保险类型和慢性合并症（如高血压）是预测护理失误的重要变量。我们使用真实世界的方法来利用现代EHR中可用的全部数据来预测HIV护理失误。我们的研究结果加强了先前已知的因素，如既往护理失误的历史，同时也显示了实验室检测，慢性合并症，社会人口特征和临床特异性因素对预测芝加哥艾滋病毒感染者护理失误的重要性。我们为其他人提供了一个框架，可以使用来自一个城市内多个不同医疗保健系统的数据，使用EHR数据检查护理失误，这将有助于司法部门努力改善艾滋病毒护理的保留。
Reducing care lapses for people living with HIV is critical to ending the HIV epidemic and beneficial for their health. Predictive modeling can identify clinical factors associated with HIV care lapses. Previous studies have identified these factors within a single clinic or using a national network of clinics, but public health strategies to improve retention in care in the United States often occur within a regional jurisdiction (eg, a city or county). We sought to build predictive models of HIV care lapses using a large, multisite, noncurated database of electronic health records (EHRs) in Chicago, Illinois. We used 2011-2019 data from the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a database including multiple health systems, covering the majority of 23,580 people with an HIV diagnosis living in Chicago. CAPriCORN uses a hash-based data deduplication method to follow people across multiple Chicago health care systems with different EHRs, providing a unique citywide view of retention in HIV care. From the database, we used diagnosis codes, medications, laboratory tests, demographics, and encounter information to build predictive models. Our primary outcome was lapses in HIV care, defined as having more than 12 months between subsequent HIV care encounters. We built logistic regression, random forest, elastic net logistic regression, and XGBoost models using all variables and compared their performance to a baseline logistic regression model containing only demographics and retention history. We included people living with HIV with at least 2 HIV care encounters in the database, yielding 16,930 people living with HIV with 191,492 encounters. All models outperformed the baseline logistic regression model, with the most improvement from the XGBoost model (area under the receiver operating characteristic curve 0.776, 95% CI 0.768-0.784 vs 0.674, 95% CI 0.664-0.683; P<.001). Top predictors included the history of care lapses, being seen by an infectious disease provider (vs a primary care provider), site of care, Hispanic ethnicity, and previous HIV laboratory testing. The random forest model (area under the receiver operating characteristic curve 0.751, 95% CI 0.742-0.759) revealed age, insurance type, and chronic comorbidities (eg, hypertension), as important variables in predicting a care lapse. We used a real-world approach to leverage the full scope of data available in modern EHRs to predict HIV care lapses. Our findings reinforce previously known factors, such as the history of prior care lapses, while also showing the importance of laboratory testing, chronic comorbidities, sociodemographic characteristics, and clinic-specific factors for predicting care lapses for people living with HIV in Chicago. We provide a framework for others to use data from multiple different health care systems within a single city to examine lapses in care using EHR data, which will aid in jurisdictional efforts to improve retention in HIV care.
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影响因子：
4.9
作者：
Mugavero, Michael J.;Davila, Jessica A.;Giordano, Thomas P.
通讯作者：
Giordano, Thomas P.
DOI：
10.1097/qai.0b013e318273762f
发表时间：
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影响因子：
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作者：
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通讯作者：
Mugavero MJ
影响因子：
4.4
作者：
Ridgway, Jessica P.;Ajith, Aswathy;Friedman, Eleanor E.;Mugavero, Michael J.;Kitahata, Mari M.;Crane, Heidi M.;Moore, Richard D.;Webel, Allison;Cachay, Edward R.;Christopoulos, Katerina A.;Mayer, Kenneth H.;Napravnik, Sonia;Mayampurath, Anoop
通讯作者：
Mayampurath, Anoop