Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records
Aim: Catheter ablation is used to treat symptomatic atrial fibrillation (AF) and is performed using either cryoballoon (CB) or radiofrequency (RF) ablation. There is limited real world data of CB and RF in the US as healthcare codes are agnostic of energy modality. An alternative method is to analyz...
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| Format: | Article |
| Language: | English |
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Becaris Publishing Limited
2024-01-01
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| Series: | Journal of Comparative Effectiveness Research |
| Subjects: |
| _version_ | 1797258468105650176 |
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| author | Jamie Margetta Alicia Sale |
| author_facet | Jamie Margetta Alicia Sale |
| author_sort | Jamie Margetta |
| collection | DOAJ |
| description | Aim: Catheter ablation is used to treat symptomatic atrial fibrillation (AF) and is performed using either cryoballoon (CB) or radiofrequency (RF) ablation. There is limited real world data of CB and RF in the US as healthcare codes are agnostic of energy modality. An alternative method is to analyze patients’ electronic health records (EHRs) using Optum’s EHR database. Objective: To determine the feasibility of using patients’ EHRs with natural language processing (NLP) to distinguish CB versus RF ablation procedures. Data Source: Optum de-identified EHR dataset, Optum Cardiac Ablation NLP Table. Methods: This was a retrospective analysis of existing de-identified EHR data. Medical codes were used to create an ablation validation table. Frequency analysis was used to assess ablation procedures and their associated note terms. Two cohorts were created (1) index procedures, (2) multiple procedures. Possible note term
combinations included (1) cryoablation (2) radiofrequency (3) ablation, or (4) both. Results: Of the 40,810 validated cardiac ablations, 3777 (9%) index ablation procedures had available and matching NLP note terms. Of these, 22% (n = 844) were classified as ablation, 27% (n = 1016) as cryoablation, 49% (n = 1855) as radiofrequency ablation, and 1.6% (n = 62) as both. In the multiple procedures analysis, 5691 (14%) procedures had matching note terms. 24% (n = 1362) were classified as ablation, 27% as cryoablation, 47% as radiofrequency ablation, and 2%as both. Conclusion: NLP has potential to evaluate the frequency of cardiac ablation by type, however, for this to be a reliable real-world data source, mandatory data entry by providers and standardized electronic health reporting must occur. |
| first_indexed | 2024-04-24T22:54:00Z |
| format | Article |
| id | doaj.art-ab8252759a8349bcb1316faabc2f4664 |
| institution | Directory Open Access Journal |
| issn | 2042-6313 |
| language | English |
| last_indexed | 2024-04-24T22:54:00Z |
| publishDate | 2024-01-01 |
| publisher | Becaris Publishing Limited |
| record_format | Article |
| series | Journal of Comparative Effectiveness Research |
| spelling | doaj.art-ab8252759a8349bcb1316faabc2f46642024-03-18T09:50:38ZengBecaris Publishing LimitedJournal of Comparative Effectiveness Research2042-63132024-01-0113310.57264/cer-2023-0053Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health recordsJamie Margetta0https://orcid.org/0009-0003-7584-9404Alicia Sale1https://orcid.org/0009-0003-3394-5246Department of Health Economics & Outcomes Research, Medtronic, Mounds View, MN 55112, USADepartment of Health Economics & Outcomes Research, Medtronic, Mounds View, MN 55112, USAAim: Catheter ablation is used to treat symptomatic atrial fibrillation (AF) and is performed using either cryoballoon (CB) or radiofrequency (RF) ablation. There is limited real world data of CB and RF in the US as healthcare codes are agnostic of energy modality. An alternative method is to analyze patients’ electronic health records (EHRs) using Optum’s EHR database. Objective: To determine the feasibility of using patients’ EHRs with natural language processing (NLP) to distinguish CB versus RF ablation procedures. Data Source: Optum de-identified EHR dataset, Optum Cardiac Ablation NLP Table. Methods: This was a retrospective analysis of existing de-identified EHR data. Medical codes were used to create an ablation validation table. Frequency analysis was used to assess ablation procedures and their associated note terms. Two cohorts were created (1) index procedures, (2) multiple procedures. Possible note term combinations included (1) cryoablation (2) radiofrequency (3) ablation, or (4) both. Results: Of the 40,810 validated cardiac ablations, 3777 (9%) index ablation procedures had available and matching NLP note terms. Of these, 22% (n = 844) were classified as ablation, 27% (n = 1016) as cryoablation, 49% (n = 1855) as radiofrequency ablation, and 1.6% (n = 62) as both. In the multiple procedures analysis, 5691 (14%) procedures had matching note terms. 24% (n = 1362) were classified as ablation, 27% as cryoablation, 47% as radiofrequency ablation, and 2%as both. Conclusion: NLP has potential to evaluate the frequency of cardiac ablation by type, however, for this to be a reliable real-world data source, mandatory data entry by providers and standardized electronic health reporting must occur.catheter ablationcryoballoonelectronic health recordsnatural language processingpulmonary vein isolation |
| spellingShingle | Jamie Margetta Alicia Sale Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records Journal of Comparative Effectiveness Research catheter ablation cryoballoon electronic health records natural language processing pulmonary vein isolation |
| title | Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records |
| title_full | Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records |
| title_fullStr | Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records |
| title_full_unstemmed | Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records |
| title_short | Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records |
| title_sort | distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records |
| topic | catheter ablation cryoballoon electronic health records natural language processing pulmonary vein isolation |
| work_keys_str_mv | AT jamiemargetta distinguishingcardiaccatheterablationenergymodalitiesbyapplyingnaturallanguageprocessingtoelectronichealthrecords AT aliciasale distinguishingcardiaccatheterablationenergymodalitiesbyapplyingnaturallanguageprocessingtoelectronichealthrecords |