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

dataset

posted on 2024-02-05, 12:40 authored by Jamie Margetta, Alicia Sale

These are peer-reviewed supplementary tables for the article 'Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records' published in the Journal of Comparative Effectiveness Research.

Table 1: ICD10 codes for atrial fibrillation
Table 2: ICD10 codes for atrial flutter
Table 3: Inpatient and outpatient atrial fibrillation ablation procedure codes
Table 4: Illustrative screenshot of validation and natural language processing (NLP) table links
Table 5: 60-day de novo sensitivity analysis

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 R ? de-identified EHR dataset, Optum R ? 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.