Kidwai-Khan Farah, Rentsch Christopher T, Pulk Rebecca, Alcorn Charles, Brandt Cynthia A, Justice Amy C
VA Connecticut Healthcare System, West Haven, CT, United States.
Yale School of Medicine, New Haven, CT, United States.
Front Big Data. 2022 Nov 15;5:1059088. doi: 10.3389/fdata.2022.1059088. eCollection 2022.
A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data.
This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction.
The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration.
Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset.
越来越多的医疗服务提供者在电子健康记录（EHR）软件界面的指导下做出复杂的治疗决策。许多界面成功整合了多种数据来源（如实验室检查、药房信息、诊断结果），不过纳入遗传数据的相对较少。
本研究利用信息学方法和预测模型来创建并验证算法，以便在医疗现场近乎实时地做出明智的药物基因组学决策。所提出的框架整合了与患者当前用药相关的电子健康记录和遗传数据，包括基于预测模型的决策支持机制。我们创建了一个包含电子健康记录的原型，并链接了来自美国最大的综合医疗系统退伍军人事务部（VA）的遗传数据。电子健康记录数据包括2600名艾滋病毒感染者及其匹配的未感染对照的诊断结果、药物配给和门诊就诊信息，并与原型遗传数据（DNA序列中单个或多个位置的变异）相关联。然后，我们将患者所服用的药物与临床药物基因组学实施联盟（CPIC）A级药物 - 基因相互作用图谱（即至少有一项用药行动的充分证据）中定义的药物进行匹配，这些图谱可预测不良事件。CPIC是一个由美国国立卫生研究院资助的专家小组，他们制定基于证据的药物基因组学指南。可预防不良事件（PAE）可定义为原本可以预防的干预措施导致的有害结果。在本研究中，我们关注的是由药物 - 基因相互作用导致的潜在可预防不良事件。
最终模型显示，纳入遗传数据时，AUC评分为0.972，F1评分为0.97；而未整合遗传数据时，AUC分别为0.766，F1分别为0.73。
该队列中超过98%的人在其一生中至少服用过一种有CPIC A级指南的药物。我们比较了五种建模方法（随机森林、支持向量机（SVM）、极端梯度提升（XGBoost）、K近邻（KNN）和决策树）检测可预防不良事件的机器学习模型的预测能力。我们发现，当将遗传数据添加到框架中时，XGBoost在原型中表现最佳，并且改进了对可预防不良事件的预测。我们比较了测试数据集中各模型的曲线下面积（AUC）。
