Natural language processing to identify reasons for sex disparity in statin prescriptions
Background: Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | American Journal of Preventive Cardiology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666667723000375 |
| _version_ | 1797798588568305664 |
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| author | Celeste Witting Zahra Azizi Sofia Elena Gomez Alban Zammit Ashish Sarraju Summer Ngo Tina Hernandez-Boussard Fatima Rodriguez |
| author_facet | Celeste Witting Zahra Azizi Sofia Elena Gomez Alban Zammit Ashish Sarraju Summer Ngo Tina Hernandez-Boussard Fatima Rodriguez |
| author_sort | Celeste Witting |
| collection | DOAJ |
| description | Background: Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing (NLP) to elucidate factors contributing to this disparity. Methods: Our cohort included adult patients with two or more encounters between 2014 and 2021 with an ASCVD diagnosis within a multisite electronic health record (EHR) in Northern California. After reviewing structured EHR prescription data, we used a benchmark deep learning NLP approach, Clinical Bidirectional Encoder Representations from Transformers (BERT), to identify and interpret discussions of statin prescriptions documented in clinical notes. Clinical BERT was evaluated against expert clinician review in 20% test sets. Results: There were 88,913 patients with ASCVD (mean age 67.8±13.1 years) and 35,901 (40.4%) were women. Women with ASCVD were less likely to be prescribed statins compared with men (56.6% vs 67.6%, p <0.001), and, when prescribed, less likely to be prescribed guideline-directed high-intensity dosing (41.4% vs 49.8%, p <0.001). These disparities were more pronounced among younger patients, patients with private insurance, and those for whom English is their preferred language. Among those not prescribed statins, women were less likely than men to have statins mentioned in their clinical notes (16.9% vs 19.1%, p <0.001). Women were less likely than men to have statin use reported in clinical notes despite absence of recorded prescription (32.8% vs 42.6%, p <0.001). Women were slightly more likely than men to have statin intolerance documented in structured data or clinical notes (6.0% vs 5.3%, p=0.003). Conclusions: Women with ASCVD were less likely to be prescribed guideline-directed statins compared with men. NLP identified additional sex-based statin disparities and reasons for statin non-prescription in clinical notes of patients with ASCVD. |
| first_indexed | 2024-03-13T04:06:04Z |
| format | Article |
| id | doaj.art-2fd435e87333436293c2375d42b6d5b4 |
| institution | Directory Open Access Journal |
| issn | 2666-6677 |
| language | English |
| last_indexed | 2024-03-13T04:06:04Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | American Journal of Preventive Cardiology |
| spelling | doaj.art-2fd435e87333436293c2375d42b6d5b42023-06-21T07:00:50ZengElsevierAmerican Journal of Preventive Cardiology2666-66772023-06-0114100496Natural language processing to identify reasons for sex disparity in statin prescriptionsCeleste Witting0Zahra Azizi1Sofia Elena Gomez2Alban Zammit3Ashish Sarraju4Summer Ngo5Tina Hernandez-Boussard6Fatima Rodriguez7Stanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Center for Academic Medicine, Mail Code 5687, 453 Quarry Road, Palo Alto, Stanford, CA, USAStanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Center for Academic Medicine, Mail Code 5687, 453 Quarry Road, Palo Alto, Stanford, CA, USA; Center for Digital Health, Stanford University, Stanford, CA, USAStanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Center for Academic Medicine, Mail Code 5687, 453 Quarry Road, Palo Alto, Stanford, CA, USAInstitute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USADepartment of Cardiovascular Medicine, Heart, Vascular & Thoracic Institute, Cleveland Clinic, Cleveland, OH, USAStanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Center for Academic Medicine, Mail Code 5687, 453 Quarry Road, Palo Alto, Stanford, CA, USADepartment of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA; Department of Surgery, Stanford University School of Medicine, Stanford, CA, USAStanford University Division of Cardiovascular Medicine and Cardiovascular Institute, Department of Medicine, Stanford University, Center for Academic Medicine, Mail Code 5687, 453 Quarry Road, Palo Alto, Stanford, CA, USA; Corresponding author.Background: Statins are the cornerstone of treatment of patients with atherosclerotic cardiovascular disease (ASCVD). Despite this, multiple studies have shown that women with ASCVD are less likely to be prescribed statins than men. The objective of this study was to use Natural Language Processing (NLP) to elucidate factors contributing to this disparity. Methods: Our cohort included adult patients with two or more encounters between 2014 and 2021 with an ASCVD diagnosis within a multisite electronic health record (EHR) in Northern California. After reviewing structured EHR prescription data, we used a benchmark deep learning NLP approach, Clinical Bidirectional Encoder Representations from Transformers (BERT), to identify and interpret discussions of statin prescriptions documented in clinical notes. Clinical BERT was evaluated against expert clinician review in 20% test sets. Results: There were 88,913 patients with ASCVD (mean age 67.8±13.1 years) and 35,901 (40.4%) were women. Women with ASCVD were less likely to be prescribed statins compared with men (56.6% vs 67.6%, p <0.001), and, when prescribed, less likely to be prescribed guideline-directed high-intensity dosing (41.4% vs 49.8%, p <0.001). These disparities were more pronounced among younger patients, patients with private insurance, and those for whom English is their preferred language. Among those not prescribed statins, women were less likely than men to have statins mentioned in their clinical notes (16.9% vs 19.1%, p <0.001). Women were less likely than men to have statin use reported in clinical notes despite absence of recorded prescription (32.8% vs 42.6%, p <0.001). Women were slightly more likely than men to have statin intolerance documented in structured data or clinical notes (6.0% vs 5.3%, p=0.003). Conclusions: Women with ASCVD were less likely to be prescribed guideline-directed statins compared with men. NLP identified additional sex-based statin disparities and reasons for statin non-prescription in clinical notes of patients with ASCVD.http://www.sciencedirect.com/science/article/pii/S2666667723000375StatinsSexAtherosclerotic cardiovascular disease |
| spellingShingle | Celeste Witting Zahra Azizi Sofia Elena Gomez Alban Zammit Ashish Sarraju Summer Ngo Tina Hernandez-Boussard Fatima Rodriguez Natural language processing to identify reasons for sex disparity in statin prescriptions American Journal of Preventive Cardiology Statins Sex Atherosclerotic cardiovascular disease |
| title | Natural language processing to identify reasons for sex disparity in statin prescriptions |
| title_full | Natural language processing to identify reasons for sex disparity in statin prescriptions |
| title_fullStr | Natural language processing to identify reasons for sex disparity in statin prescriptions |
| title_full_unstemmed | Natural language processing to identify reasons for sex disparity in statin prescriptions |
| title_short | Natural language processing to identify reasons for sex disparity in statin prescriptions |
| title_sort | natural language processing to identify reasons for sex disparity in statin prescriptions |
| topic | Statins Sex Atherosclerotic cardiovascular disease |
| url | http://www.sciencedirect.com/science/article/pii/S2666667723000375 |
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