RT Journal Article
SR Electronic
T1 Risk prediction score for clinical outcome in atrial fibrillation and stable coronary artery disease
JF Open Heart
JO Open Heart
FD British Cardiovascular Society
SP e002292
DO 10.1136/openhrt-2023-002292
VO 10
IS 1
A1 Masanobu Ishii
A1 Koichi Kaikita
A1 Satoshi Yasuda
A1 Masaharu Akao
A1 Junya Ako
A1 Tetsuya Matoba
A1 Masato Nakamura
A1 Katsumi Miyauchi
A1 Nobuhisa Hagiwara
A1 Kazuo Kimura
A1 Atsushi Hirayama
A1 Eiichiro Nishihara
A1 Shinichiro Nakamura
A1 Kunihiko Matsui
A1 Hisao Ogawa
A1 Kenichi Tsujita
A1 ,
YR 2023
UL http://openheart.bmj.com/content/10/1/e002292.abstract
AB Objective Antithrombotic therapy is essential for patients with atrial fibrillation (AF) and stable coronary artery disease (CAD) because of the high risk of thrombosis, whereas a combination of antiplatelets and anticoagulants is associated with a high risk of bleeding. We sought to develop and validate a machine-learning-based model to predict future adverse events.Methods Data from 2215 patients with AF and stable CAD enrolled in the Atrial Fibrillation and Ischaemic Events With Rivaroxaban in Patients With Stable Coronary Artery Disease trial were randomly assigned to the development and validation cohorts. Using the random survival forest (RSF) and Cox regression models, risk scores were developed for net adverse clinical events (NACE) defined as all-cause death, myocardial infarction, stroke or major bleeding.Results Using variables selected by the Boruta algorithm, RSF and Cox models demonstrated acceptable discrimination and calibration in the validation cohort. Using the variables weighted by HR (age, sex, body mass index, systolic blood pressure, alcohol consumption, creatinine clearance, heart failure, diabetes, antiplatelet use and AF type), an integer-based risk score for NACE was developed and classified patients into three risk groups: low (0–4 points), intermediate (5–8) and high (≥9). In both cohorts, the integer-based risk score performed well, with acceptable discrimination (area under the curve 0.70 and 0.66, respectively) and calibration (p>0.40 for both). Decision curve analysis showed the superior net benefits of the risk score.Conclusions This risk score can predict the risk of NACE in patients with AF and stable CAD.Trial registration numbers UMIN000016612, NCT02642419.Data are available on reasonable request. The data underlying this article will be shared on reasonable request to the corresponding author.