Clinical Phenotyping for Prediction of Retention in HIV Care

保持护理对艾滋病毒的治疗和预防至关重要，但在美国，只有一半的艾滋病毒携带者 保留在医疗护理中。改善留存对结束美国艾滋病毒流行至关重要，但有效 保留干预措施是高度资源密集型的。随着艾滋病毒护理资源的减少和不断增加 由于艾滋病毒的流行，需要更好的方法来协助艾滋病毒护理小组识别最脆弱的患者 到失去跟进(LTFU)，在LTFU发生之前谁将从保留资源中获益最多。预测性的 基于电子健康数据的LTFU模型有可能满足这一需求，因为它量化了特定的 患者未来脱离护理的风险基于他/她的独特特征，并且可以自动进行 以实时生成风险预测。 使用来自城市HIV诊所的数据，我们开发了一个机器学习模型来预测HIV的LTFU 使用自然语言处理(NLP)在电子医疗中对提供者备注的非结构化文本的护理 记录(电子病历)。NLP模型在检测有LTFU风险的患者时表现出良好的性能 阳性预测值(PPV)为0.86，并识别出与LTFU相关的单词模式，例如 “虐待”和“污名”，从而证明了良好的面子有效性。 虽然我们的初步数据显示了基于NLP的机器学习模型预测未来的潜力 在护理保留方面，需要解决几个关键问题，然后才能将该模型部署到患者护理中。 首先，PWH是一个明显的异质性种群，可能存在以下亚群 患者(例如，与男性发生性关系的年轻黑人男性，负有育儿责任的顺性女性， 注射毒品的人和不稳定的住房等)，这些人在预测 LTFU。临床表型是一种分析方法，可以将患者聚集在不同的人群中。 根据个人资料的相似性划分为不同的子组。在部署单一型号之前， 因此，更好地理解我们的NLP模型在不同临床表型上的表现是至关重要的 艾滋病病毒感染者的比例。其次，目前还不知道该模型在预期的、现实的环境中会有怎样的表现。 最后，与提供商直觉相比，机器学习模型在以下方面的表现也不清楚 患者脱离护理的风险。这项提案试图通过两个具体的 目标。在目标1中，我们将确定LTFU的NLP预测模型在不同临床中的性能 艾滋病病毒携带者的表型。在目标2中，我们将前瞻性地验证模型并将结果与 护理团队对HIV感染者LTFU风险的直觉。 随着我们朝着结束艾滋病毒流行的方向迈进，该项目的结果将提供有关以下方面的重要信息 使用NLP和临床表型来预测失去HIV治疗后的随访，并将使未来的R01成为可能 申请多地点实施该模式，并结合个别定制的保留干预措施。

Retention in care is essential to HIV treatment and prevention, yet only half of people with HIV in the U.S. are retained in medical care. Improving retention is critical for ending the HIV epidemic in the U.S., but effective retention interventions are highly resource intensive. With diminishing resources for HIV care and increasing prevalence of HIV, better approaches are needed to assist HIV care teams to identify patients most vulnerable to loss to follow-up (LTFU) who would most benefit from retention resources before LTFU occurs. A predictive model of LTFU based on electronic health data has the potential to address this need, as it quantifies a specific patient’s risk of future disengagement from care based on his/her unique characteristics and can be automated to generate risk prediction in real time. Using data from an urban HIV clinic, we have developed a machine learning model to predict LTFU from HIV care using natural language processing (NLP) of unstructured text of provider notes in the electronic medical record (EMR). The NLP model demonstrated good performance in detecting patients at risk for LTFU with a positive predictive value (PPV) of 0.86, and identified word patterns associated with LTFU, such as “substance abuse” and “stigma,” thereby demonstrating good face validity. While our preliminary data reveal the potential of NLP-based machine learning models to predict future retention in care, several key issues need to be addressed before the model can be deployed for patient care. First, PWH are a markedly heterogeneous population, and it is possible that there may exist sub-groups of patients (e.g., young Black men who have sex with men, cisgender women with childcare responsibilities, people who inject drugs and are unstably housed, etc) that differ drastically in the factors that are predictive of LTFU. Clinical phenotyping is an analytic method that can cluster patients within a heterogeneous population into different sub-groups based on profile similarities. Before a single model is deployed with a “one-size-fits- all” manner, it is crucial to better understand the performance of our NLP model on different clinical phenotypes of patients with HIV. Second, it is not known how the model would perform in a prospective, real-life setting. Finally, it is also unclear how a machine learning model would perform compared to provider intuition regarding patients’ risk for disengagement from care. This proposal seeks to address these issues through 2 specific aims. In Aim 1, we will determine the performance of the NLP predictive model of LTFU for different clinical phenotypes of people with HIV. In Aim 2, we will prospectively validate the model and compare results with care team intuition regarding risk for LTFU among people with HIV. As we move toward ending the HIV epidemic, results from this project will provide crucial information regarding the use of NLP and clinical phenotyping to predict loss to follow-up from HIV care and will enable a future R01 application for a multi-site implementation of the model paired with individually tailored retention interventions.