Stanford University School of Medicine, Stanford, CA.
Department of Medicine (Biomedical Informatics), Stanford University School of Medicine, Stanford, CA.
JCO Clin Cancer Inform. 2021 Oct;5:1106-1126. doi: 10.1200/CCI.21.00116.
Acute care use (ACU) is a major driver of oncologic costs and is penalized by a Centers for Medicare & Medicaid Services quality measure, OP-35. Targeted interventions reduce preventable ACU; however, identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data in the electronic health record (EHR). We aimed to predict risk of preventable ACU after starting chemotherapy using machine learning (ML) algorithms trained on comprehensive EHR data.
Chemotherapy patients treated at an academic institution and affiliated community care sites between January 2013 and July 2019 who met inclusion criteria for OP-35 were identified. Preventable ACU was defined using OP-35 criteria. Structured EHR data generated before chemotherapy treatment were obtained. ML models were trained to predict risk for ACU after starting chemotherapy using 80% of the cohort. The remaining 20% were used to test model performance by the area under the receiver operator curve.
Eight thousand four hundred thirty-nine patients were included, of whom 35% had preventable ACU within 180 days of starting chemotherapy. Our primary model classified patients at risk for preventable ACU with an area under the receiver operator curve of 0.783 (95% CI, 0.761 to 0.806). Performance was better for identifying admissions than emergency department visits. Key variables included prior hospitalizations, cancer stage, race, laboratory values, and a diagnosis of depression. Analyses showed limited benefit from including patient-reported outcome data and indicated inequities in outcomes and risk modeling for Black and Medicaid patients.
Dense EHR data can identify patients at risk for ACU using ML with promising accuracy. These models have potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted, preventative interventions.
急性护理使用（ACU）是肿瘤学成本的主要驱动因素，并且受到医疗保险和医疗补助服务中心质量措施 OP-35 的惩罚。有针对性的干预措施可以减少可预防的 ACU；然而，确定哪些患者可能受益仍然具有挑战性。先前的预测模型已经利用了电子健康记录（EHR）中有限的数据集。我们旨在使用基于全面 EHR 数据训练的机器学习（ML）算法预测开始化疗后可预防 ACU 的风险。
在学术机构和附属社区护理站点接受治疗的符合 OP-35 纳入标准的化疗患者于 2013 年 1 月至 2019 年 7 月之间被确定。可预防的 ACU 使用 OP-35 标准定义。获得化疗前生成的结构化 EHR 数据。使用 80%的队列训练 ML 模型来预测开始化疗后 ACU 的风险。其余 20%用于通过接收者操作特征曲线下的面积来测试模型性能。
共纳入 8439 名患者，其中 35%的患者在开始化疗后 180 天内发生可预防的 ACU。我们的主要模型对可预防 ACU 风险的分类，接收者操作特征曲线下的面积为 0.783（95%CI，0.761 至 0.806）。对住院和急诊就诊的识别效果更好。关键变量包括先前的住院治疗、癌症分期、种族、实验室值和抑郁症诊断。分析表明，纳入患者报告的结果数据的益处有限，并表明黑人患者和医疗补助患者的结果和风险建模存在不平等。
使用 ML 可以从密集的 EHR 数据中识别出有 ACU 风险的患者，并且具有很高的准确性。这些模型有可能通过允许有针对性的预防干预措施来改善癌症护理结果、患者体验和成本。
