English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background Administrative data have limited sensitivity for case finding of atrial fibrillation/atrial flutter (AF/AFL). Linkage with clinical repositories of interpreted ECGs may enhance diagnostic yield of AF/AFL. Methods and Results We retrieved 369 ECGs from the institutional Marquette Universal System for Electrocardiography (MUSE) repository as validation samples, with rhythm coded as AF (n=49), AFL (n=50), or other competing rhythm diagnoses (n=270). With blinded, duplicate review of ECGs as the reference comparison, we compared multiple MUSE coding definitions for identifying AF/AFL. We tested the agreement between MUSE diagnosis and reference comparison, and calculated the sensitivity and specificity. Using a data set linking clinical registries, administrative data, and the MUSE repository (n=11 662), we assessed the incremental diagnostic yield of AF/AFL by incorporating ECG data to administrative data‐based algorithms. The agreement between MUSE diagnosis and reference comparison depended on the coding definitions applied, with the Cohen κ ranging from 0.57 to 0.75. Sensitivity ranged from 60.6% to 79.1%, and specificity ranged from 93.2% to 98.0%. A coding definition with AF/AFL appearing in the first 3 ECG statements had the highest sensitivity (79.1%), with little loss of specificity (94.5%). Compared with the algorithms with only administrative data, incorporating ECG data increased the diagnostic yield of preexisting AF/AFL by 14.5% and incident AF/AFL by 7.5% to 16.1%. Conclusions Routine ECG interpretation using MUSE coding is highly specific and moderately sensitive for AF/AFL detection. Inclusion of MUSE ECG data in AF/AFL case identification algorithms can identify cases missed using administrative data‐based algorithms alone.

Use of a Clinical Electrocardiographic Database to Enhance Atrial Fibrillation/Atrial Flutter Identification Algorithms Based on Administrative Data