Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study

Bisson, Arnaud ORCID: 0000-0002-3449-1800, Lemrini, Yassine, El-Bouri, Wahbi ORCID: 0000-0002-2732-5927, Bodin, Alexandre, Angoulvant, Denis, Lip, Gregory YH ORCID: 0000-0002-7566-1626 and Fauchier, Laurent (2022) Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study. CLINICAL RESEARCH IN CARDIOLOGY, 112 (6). pp. 815-823.

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Abstract

<h4>Background</h4>Targeting ischemic strokes patients at risk of incident atrial fibrillation (AF) for prolonged cardiac monitoring and oral anticoagulation remains a challenge. Clinical risk scores have been developed to predict post-stroke AF with suboptimal performances. Machine learning (ML) models are developing in the field of AF prediction and may be used to discriminate post-stroke patients at risk of new onset AF. This study aimed to evaluate ML models for the prediction of AF and to compare predictive ability to usual clinical scores.<h4>Methods</h4>Based on a French nationwide cohort of 240,459 ischemic stroke patients without AF at baseline from 2009 to 2012, ML models were trained on a train set and the best model was selected to be evaluate on the test set. Discrimination of the best model was evaluated using the C index. We finally compared our best model with previously described clinical scores.<h4>Results</h4>During a mean follow-up of 7.9 ± 11.5 months, 14,095 patients (mean age 77.6 ± 10.6; 50.3% female) developed incident AF. After training, the best ML model selected was a deep neural network with a C index of 0.77 (95% CI 0.76-0.78) on the test set. Compared to traditional clinical scores, the selected model was statistically significantly superior to the CHA₂DS₂-VASc score, Framingham risk score, HAVOC score and C₂HEST score (P < 0.0001). The ability to predict AF was improved as shown by net reclassification index increase (P < 0.0001) and decision curve analysis.<h4>Conclusions</h4>ML algorithms predict incident AF post-stroke with a better ability than previously developed clinical scores. AF: atrial fibrillation; DNN: deep neural network; IS: ischemic stroke; KNN: K-nearest neighbors; LR: logistic regression; RFC: random forest classifier; XGBoost: extreme gradient boosting.

Item Type:	Article
Uncontrolled Keywords:	Atrial fibrillation, Ischemic stroke, Machine learning, Prediction
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	02 Mar 2023 10:15
Last Modified:	17 Dec 2023 02:30
DOI:	10.1007/s00392-022-02140-w
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3168681