S-EPMC9287295 - Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease - OmicsDI

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Dataset Information

Using deep learning-based natural language processing to identify reasons for statin nonuse in patients with atherosclerotic cardiovascular disease

ABSTRACT:

Background

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.

Methods

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.

Results

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.

Conclusions

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. Plain language summary Cardiovascular disease (CVD) is the leading cause of death worldwide. Statins are cholesterol-lowering drugs that are recommended by major guidelines to reduce the risk of heart attacks and death. However, despite being effective and generally well-tolerated, statins are concerningly underused, and reasons for such statin nonuse are not well-understood. Using artificial intelligence (AI) approaches that can analyze complex language data including written medical terminology, we identified statin nonuse and reasons for statin nonuse from clinical notes from electronic health records of CVD patients at a large health system. We found that nearly 2 in 5 patients lacked guideline-directed statin prescriptions. We then identified the key reasons for statin nonuse including patient (side-effects, personal preference) and clinician reasons (not practicing according to guidelines). Our AI approaches may help target interventions to address statin nonuse and thus, improve CVD treatment. Sarraju, Coquet et al. utilise a natural language processing approach to identify reasons for statin nonuse in the health records of patients with atherosclerotic cardiovascular disease (ASCVD). Their approach identifies patient-level and clinician-level reasons for statin nonuse, with differences by ASCVD type and patient race/ethnicity.

SUBMITTER: Sarraju A

PROVIDER: S-EPMC9287295 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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