Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic
1Department of Health Economics & Outcomes Research, Medtronic, Mounds View, MN 55112, USA.
Insights
Natural language processing shows potential for analyzing catheter ablation types in electronic health records. However, standardized data entry is needed for reliable real-world insights into cryoballoon versus radiofrequency ablation procedures.
Area of Science:
- Cardiology
- Medical Informatics
- Health Data Science
Background:
- Catheter ablation using cryoballoon (CB) or radiofrequency (RF) energy treats symptomatic atrial fibrillation (AF).
- Limited real-world data exists in the US differentiating CB and RF ablation due to healthcare code limitations.
- Electronic Health Records (EHRs) offer a potential alternative data source.
Purpose of the Study:
- To assess the feasibility of using Natural Language Processing (NLP) on EHR data to differentiate between CB and RF ablation procedures.
- To determine the potential of NLP in analyzing real-world data for cardiac ablation modalities.
Main Methods:
- Retrospective analysis of de-identified EHR data from Optum®.
- Utilized the Optum® Cardiac Ablation NLP Table and medical codes for validation.
- Applied frequency analysis to note terms associated with ablation procedures, categorizing them into ablation, cryoablation, radiofrequency ablation, or both.
Main Results:
- Of 40,810 validated cardiac ablations, 3,777 index procedures (9%) had matching NLP note terms.
- Cryoablation accounted for 27% and radiofrequency ablation for 49% of index procedures classified by NLP.
- In multiple procedures analysis, 27% were cryoablation and 47% were radiofrequency ablation.
Conclusions:
- NLP demonstrates potential for evaluating the frequency of different cardiac ablation types.
- Reliable real-world data requires mandatory provider data entry and standardized EHR reporting for NLP analysis.
- Further development in data standardization is crucial for leveraging EHRs in ablation research.
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.
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