Risk prediction for new-onset atrial fibrillation using the Minnesota code electrocardiography classification system

• Main Authors: Yu Igarashi, Kotaro Nochioka, Yasuhiko Sakata, Tokiwa Tamai, Shinya Ohkouchi, Toshiya Irokawa, Hiromasa Ogawa, Hideka Hayashi, Takahide Fujihashi, Shinsuke Yamanaka, Takashi Shiroto, Satoshi Miyata, Jun Hata, Shogo Yamada, Toshiharu Ninomiya, Satoshi Yasuda, Hajime Kurosawa, Hiroaki Shimokawa
• Format: Article
• Language: English
• Published: Elsevier 2021-06-01
• Series: International Journal of Cardiology: Heart & Vasculature
• Subjects: Atrial fibrillation; Epidemiology; Risk factors; Risk score; Minnesota code
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352906721000506

Background: Few risk models are available to predict future onset of atrial fibrillation (AF) in workers. We aimed to develop risk prediction models for new-onset AF, using annual health checkup (HC) data with electrocardiogram findings. Methods and Results: We retrospectively included 56,288 factory or office workers (mean age = 51.5 years, 33.0% women) who underwent a HC at a medical center and fulfilled the following criteria; age ≥ 40 years, no history of AF, and greater than 1 annual follow-up HC in 2013–2016. Using Cox models with the Akaike information criterion, we developed and compared prediction models for new-onset AF with and without the Minnesota code information. We externally validated the discrimination accuracy of the models in a general Japanese population cohort, the Hisayama cohort. During the median 3.0-year follow-up, 209 (0.37%) workers developed AF. Age, sex, waist circumference, blood pressure, LDL cholesterol, and γ-GTP were associated with new-onset of AF. Using the Minnesota code information, the AUC significantly improved from 0.82 to 0.84 in the derivation cohort and numerically improved from 0.78 to 0.79 in the validation cohort, and from 0.77 to 0.79 in the Hisayama cohort. The NRI and IDI significantly improved in all and male subjects in both the derivation and validation cohorts, and in female subjects in both the validation and the Hisayama cohorts. Conclusions: We developed useful risk model with Minnesota code information for predicting new-onset AF from large worker population validated in the original and external cohorts, although study interpretation is limited by small improvement of AUC.