Sarraju Ashish, Coquet Jean, Zammit Alban, Chan Antonia, Ngo Summer, Hernandez-Boussard Tina, Rodriguez Fatima
Division of Cardiovascular Medicine and Cardiovascular Institute, Stanford University, Stanford, CA USA.
Department of Medicine, Stanford University, Stanford, CA USA.
Commun Med (Lond). 2022 Jul 15;2:88. doi: 10.1038/s43856-022-00157-w. eCollection 2022.
Statins conclusively decrease mortality in atherosclerotic cardiovascular disease (ASCVD), the leading cause of death worldwide, and are strongly recommended by guidelines. However, real-world statin utilization and persistence are low, resulting in excess mortality. Identifying reasons for statin nonuse at scale across health systems is crucial to developing targeted interventions to improve statin use.
We developed and validated deep learning-based natural language processing (NLP) approaches (Clinical Bidirectional Encoder Representations from Transformers [BERT]) to classify statin nonuse and reasons for statin nonuse using unstructured electronic health records (EHRs) from a diverse healthcare system.
We present data from a cohort of 56,530 ASCVD patients, among whom 21,508 (38%) lack guideline-directed statin prescriptions and statins listed as allergies in structured EHR portions. Of these 21,508 patients without prescriptions, only 3,929 (18%) have any discussion of statin use or nonuse in EHR documentation. The NLP classifiers identify statin nonuse with an area under the curve (AUC) of 0.94 (95% CI 0.93-0.96) and reasons for nonuse with a weighted-average AUC of 0.88 (95% CI 0.86-0.91) when evaluated against manual expert chart review in a held-out test set. Clinical BERT identifies key patient-level reasons (side-effects, patient preference) and clinician-level reasons (guideline-discordant practices) for statin nonuse, including differences by type of ASCVD and patient race/ethnicity.
Our deep learning NLP classifiers can identify crucial gaps in statin nonuse and reasons for nonuse in high-risk populations to support education, clinical decision support, and potential pathways for health systems to address ASCVD treatment gaps.
他汀类药物能显著降低动脉粥样硬化性心血管疾病（ASCVD）的死亡率，ASCVD是全球主要死因，且指南强烈推荐使用。然而，现实中他汀类药物的使用率和持续用药率较低，导致死亡率过高。确定整个卫生系统中他汀类药物未使用的原因对于制定有针对性的干预措施以改善他汀类药物的使用至关重要。
我们开发并验证了基于深度学习的自然语言处理（NLP）方法（临床双向编码器表征从变换器[BERT]），以使用来自多样化医疗系统的非结构化电子健康记录（EHR）对他汀类药物未使用情况及未使用原因进行分类。
我们展示了56530例ASCVD患者队列的数据，其中21508例（38%）缺乏指南指导的他汀类药物处方，且在结构化EHR部分中他汀类药物被列为过敏。在这21508例无处方患者中，只有3929例（18%）在EHR文档中有关于他汀类药物使用或未使用的任何讨论。当在一个预留测试集中与人工专家图表审查进行对比评估时，NLP分类器识别他汀类药物未使用情况的曲线下面积（AUC）为0.94（95%CI 0.93 - 0.96），识别未使用原因的加权平均AUC为0.88（95%CI 0.86 - 0.91）。临床BERT识别出他汀类药物未使用的关键患者层面原因（副作用, 患者偏好）和临床医生层面原因（与指南不一致的做法），包括ASCVD类型和患者种族/族裔的差异。
我们的深度学习NLP分类器可以识别高危人群中他汀类药物未使用情况及未使用原因的关键差距，以支持教育、临床决策支持以及卫生系统解决ASCVD治疗差距的潜在途径。
