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. 2024 Mar;13(3):e230053.
doi: 10.57264/cer-2023-0053. Epub 2024 Jan 23.

Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records

Jamie Margetta  1 , Alicia Sale  1
Affiliations

Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records

Jamie Margetta et al. J Comp Eff Res. 2024 Mar.

Abstract

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.

Keywords: catheter ablation; cryoballoon; electronic health records; natural language processing; pulmonary vein isolation.

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Conflict of interest statement

Competing interests disclosure

J Margetta and A Sale are both employees and stockholders of Medtronic. The authors have no other competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript apart from those disclosed.

Figures

Figure 1.
Figure 1.. Index procedures screening and inclusion.
AF: Atrial fibrillation; EHR: Electronic health record; NLP: Natural language processing; PVI: Pulmonary vein isolation.
Figure 2.
Figure 2.. Frequency of note terms and supraventricular tachycardia add on codes across index procedures.
SVT: Supraventricular tachycardia.
Figure 3.
Figure 3.. Multiple procedures screening and inclusion.
SVT: Supraventricular tachycardia.
Figure 4.
Figure 4.. Frequency of note terms and supraventricular tachycardia add on codes across multiple procedures.
SVT: Supraventricular tachycardia.
See this image and copyright information in PMC

References

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    1. Coding and Payment Guide. Cardiac catheter ablation. Economics, reimbursement, and evidence (2021). https://asiapac.medtronic.com/content/dam/medtronic-com/us-en/hcp/reimbu...

    2. • Cardiac ablation codes were sourced from here.

    1. Hachem AH, Marine JE, Tahboub HA et al. Radiofrequency ablation versus cryoablation in the treatment of paroxysmal atrial fibrillation: a meta-analysis. Cardiol. Res. Pract. 2018, 6276241 (2018). - PMC - PubMed
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    2. • Similar analysis in arial fibrillation therapy area.

    1. Laaksonen N, Varjonen JM, Blomster M et al. Assessing an electronic health record research platform for identification of clinical trial participants. Contemp. Clin. Trials Commun. 21, 100692 (2020). - PMC - PubMed