BACKGROUND

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 comparative data of CB and RF in the United States (US) as current healthcare codes are agnostic of energy modality. An alternative method is to analyze patients’ Electronic Health Records (EHR) using Optum’s EHR database.

OBJECTIVES:

To determine the feasibility of using patients’ EHRs with Optum’s Natural Language Processing (NLP) cardiac ablation table to distinguish CB versus RF ablation procedures and determine their respective frequencies.

DATA SOURCE

Optum® de-identified Electronic Health Record dataset

METHODS:

This was a retrospective analysis. Data validation was undergone to define cardiac ablation specific patients in the NLP table followed by a frequency analysis to assess index ablation procedures and their associated note terms. Possible note terms were (1) cryoablation (2) radio frequency ablation, (3) ablation or (4) missing. The note term ablation was categorized as ‘non-specific’ as it did not distinguish energy modality.

RESULTS:

Of the 24,833 patients with validated AF Cardiac ablation procedures, 11,349 (46%) had meaningful note terms (CB, RF, or both) associated with their index ablation procedure. Of this 11,349 (27%) had a radiofrequency note term, 3,643 (15%) had a cryoablation note term, and 927 (4%) had both. An additional 9,428 (38%) of patients had a non-specific note term, and 4,056 (16%) had missing note terms.

CONCLUSIONS:

NLP has large potential to evaluate the frequency of cardiac catheter ablation types, however, for this to be a reliable RWD in the future, mandatory data entry by providers and standardized electronic health reporting must occur.