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Long-term PM 2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

Authors
 In-Soo Kim  ;  Pil-Sung Yang  ;  Eunsun Jang  ;  Hyunjean Jung  ;  Seng Chan You  ;  Hee Tae Yu  ;  Tae-Hoon Kim  ;  Jae-Sun Uhm  ;  Hui-Nam Pak  ;  Moon-Hyoung Lee  ;  Jong-Youn Kim  ;  Boyoung Joung 
Citation
 SCIENTIFIC REPORTS, Vol.10(1) : 16324, 2020-10 
Journal Title
 SCIENTIFIC REPORTS 
Issue Date
2020-10
Abstract
Clinical impact of fine particulate matter (PM2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837-0.853]) than existing traditional regression models using CHA2DS2-VASc (0.654 [0.646-0.661]), CHADS2 (0.652 [0.646-0.657]), or HATCH (0.669 [0.661-0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM2.5 as a highly important variable, we applied PM2.5 for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM2.5 (c-indices: boosted ensemble ML, 0.954 [0.949-0.959]; PM-CHA2DS2-VASc, 0.859 [0.848-0.870]; PM-CHADS2, 0.823 [0.810-0.836]; or PM-HATCH score, 0.849 [0.837-0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM2.5 data was found to predict incident AF better than models without PM2.5 or even established risk prediction approaches in the general population exposed to high air pollution levels.
Files in This Item:
T202003960.pdf Download
DOI
10.1038/s41598-020-73537-8
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, In-Soo(김인수) ORCID logo https://orcid.org/0000-0003-2801-5514
Kim, Jong Youn(김종윤) ORCID logo https://orcid.org/0000-0001-7040-8771
Kim, Tae-Hoon(김태훈) ORCID logo https://orcid.org/0000-0003-4200-3456
Pak, Hui Nam(박희남) ORCID logo https://orcid.org/0000-0002-3256-3620
Uhm, Jae Sun(엄재선) ORCID logo https://orcid.org/0000-0002-1611-8172
Yu, Hee Tae(유희태) ORCID logo https://orcid.org/0000-0002-6835-4759
Lee, Moon-Hyoung(이문형) ORCID logo https://orcid.org/0000-0002-7268-0741
Joung, Bo Young(정보영) ORCID logo https://orcid.org/0000-0001-9036-7225
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/180070
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