Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number16324
JournalScientific reports
Volume10
Issue number1
DOIs
Publication statusPublished - 2020 Dec 1

Bibliographical note

Funding Information:
This study was supported by research grants from the Korean Healthcare Technology R&D project funded by the Ministry of Health & Welfare (HI15C1200, HC19C0130), and a faculty research grant of Department of Internal Medicine, Yonsei University College of Medicine for 2019-8020-80717-4323130, a research grant from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (NRF-2017R1A2B3003303).

Publisher Copyright:
© 2020, The Author(s).

All Science Journal Classification (ASJC) codes

  • General

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