No-show Prediction Model Performance Among People With HIV: External Validation Study.
No-show Prediction Model Performance Among People With HIV: External Validation Study.
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DOI：
10.2196/43277
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发表时间：
2023-03-29
影响因子：
7.4
通讯作者：
Ridgway, Jessica P.
中科院分区：
文献类型：
--
作者：
Mason, Joseph A.;Friedman, Eleanor E.;Rojas, Juan C.;Ridgway, Jessica P.
关键词：
定期医疗护理对于艾滋病毒感染者来说非常重要。 HIV 感染者的缺席预测模型可以通过允许提供者主动接触高风险错过预约的患者来改善临床护理。 Epic 是电子病历系统的主要提供商，它创建了一个模型来预测患者缺席门诊医疗保健预约的概率；然而，该模型尚未在艾滋病毒感染者中得到外部验证。我们检查了 Epic 的缺席模型在学术医疗中心的 HIV 感染者中的表现，并评估了该表现是否受到人口统计和 HIV 临床信息的影响。我们获得了 2022 年 1 月 21 日至 3 月 30 日在芝加哥大学医学院对艾滋病毒感染者进行的所有面对面预约的接触数据。我们将见面时预测的缺席概率与这些约会的实际结果进行了比较。我们还检查了 Epic 模型在艾滋病毒感染者中仅在传染病科进行艾滋病毒护理预约时的表现。我们进一步将艾滋病毒感染者预约艾滋病毒护理的缺席模型与我们使用 Epic 模型中使用的七个易于访问的特征的子集以及与艾滋病毒临床护理或人口统计相关的四个附加特征创建的替代随机森林模型进行了比较。我们确定了 674 名 HIV 感染者，他们在研究期间共安排了 1406 次面对面预约。其中，我们确定了 331 名艾滋病毒感染者，他们提供了 440 次艾滋病毒护理预约。在任何门诊诊所的所有预约中，Epic 模型在 HIV 感染者中的表现的接受者操作特征曲线 (AUC) 下的面积 (AUC) 为 0.65 (95% CI 0.63-0.66)，并且仅在 HIV 护理预约中的 AUC 为 0.63 (95% CI 0.59-0.67)。我们为参加 HIV 护理预约的 HIV 感染者创建的替代模型的 AUC 为 0.78 (95% CI 0.75-0.82)，比仅限于 HIV 护理预约的 Epic 模型有显着改进 (P<.001)。在替代模型中被确定为重要的特征包括交付时间、预约时长、HIV 病毒载量 >200 个拷贝/mL、较低的 CD4 T 细胞计数（50 至 <200 个细胞/mm3 和 200 至 <350 个细胞/mm3）以及女性。对于 HIV 感染者来说，这两种模型的表现都显着低于 Epic 报告的水平。替代模型相对于专有 Epic 模型的性能改进表明，在 HIV 感染者中，纳入人口统计信息可能会增强预约出勤率的预测。替代模型进一步揭示，通过使用 CD4 计数和 HIV 病毒载量测试结果等 HIV 临床信息作为模型中的特征，可以改进 HIV 感染者的预约出勤预测。
Regular medical care is important for people living with HIV. A no-show predictive model among people with HIV could improve clinical care by allowing providers to proactively engage patients at high risk of missing appointments. Epic, a major provider of electronic medical record systems, created a model that predicts a patient’s probability of being a no-show for an outpatient health care appointment; however, this model has not been externally validated in people with HIV. We examined the performance of Epic’s no-show model among people with HIV at an academic medical center and assessed whether the performance was impacted by the addition of demographic and HIV clinical information. We obtained encounter data from all in-person appointments among people with HIV from January 21 to March 30, 2022, at the University of Chicago Medicine. We compared the predicted no-show probability at the time of the encounter to the actual outcome of these appointments. We also examined the performance of the Epic model among people with HIV for only HIV care appointments in the infectious diseases department. We further compared the no-show model among people with HIV for HIV care appointments to an alternate random forest model we created using a subset of seven readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. We identified 674 people with HIV who contributed 1406 total scheduled in-person appointments during the study period. Of those, we identified 331 people with HIV who contributed 440 HIV care appointments. The performance of the Epic model among people with HIV for all appointments in any outpatient clinic had an area under the receiver operating characteristic curve (AUC) of 0.65 (95% CI 0.63-0.66) and for only HIV care appointments had an AUC of 0.63 (95% CI 0.59-0.67). The alternate model we created for people with HIV attending HIV care appointments had an AUC of 0.78 (95% CI 0.75-0.82), a significant improvement over the Epic model restricted to HIV care appointments (P<.001). Features identified as important in the alternate model included lead time, appointment length, HIV viral load >200 copies per mL, lower CD4 T cell counts (both 50 to <200 cells/mm3 and 200 to <350 cells/mm3), and female sex. For both models among people with HIV, performance was significantly lower than reported by Epic. The improvement in the performance of the alternate model over the proprietary Epic model demonstrates that, among people with HIV, the inclusion of demographic information may enhance the prediction of appointment attendance. The alternate model further reveals that the prediction of appointment attendance in people with HIV can be improved by using HIV clinical information such as CD4 count and HIV viral load test results as features in the model.
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DOI：
10.1097/qai.0000000000000572
发表时间：
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期刊：
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