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

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

ML algorithms predict incident AF post-stroke with a better ability than previously developed clinical scores.

Graphic Abstract

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

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Data availability

Data will not be available according to the French legislation.

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Author notes

  1. Denis Angoulvant, Gregory Y. H. Lip and Laurent Fauchier are joint senior authors.

Authors and Affiliations

  1. Service de Cardiologie, Centre Hospitalier Régional Universitaire et Faculté de Médecine de Tours, 2 Boulevard Tonnellé, 37000, Tours, France

    Arnaud Bisson, Yassine Lemrini, Alexandre Bodin, Denis Angoulvant & Laurent Fauchier

  2. Transplantation Immunité Inflammation, EA4245, Université de Tours, Tours, France

    Arnaud Bisson & Denis Angoulvant

  3. Service de Cardiologie, Centre Hospitalier Régional d’Orléans, Orléans, France

    Arnaud Bisson

  4. Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool Heart and Chest Hospital, Liverpool, UK

    Wahbi El-Bouri & Gregory Y. H. Lip

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  1. Arnaud Bisson
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Correspondence to Arnaud Bisson.

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Conflict of interest

A.Bisson has been a consultant or speaker for Astra-Zeneca, Bayer, BMS/Pfizer, Medtronic, Vitorpharma and Alnylam. D. Angoulvant has been a consultant or speaker for Amgen, Astra-Zeneca, Bayer, BMS/Pfizer, MSD, Novartis, Novo Nordisk, Sanofi, Servier. GYH.Lip has been a consultant for Bayer/Janssen, BMS/Pfizer, Medtronic, Boehringer Ingelheim, Novartis, Verseon, and Daiichi-Sankyo and a speaker for Bayer, BMS/Pfizer, Medtronic, Boehringer Ingelheim, and Daiichi-Sankyo; no fees are directly received personally. L. Fauchier has been a consultant or speaker for Bayer, BMS/Pfizer, Boehringer Ingelheim, Medtronic, and Novartis.

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Bisson, A., Lemrini, Y., El-Bouri, W. et al. Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study. Clin Res Cardiol 112, 815–823 (2023). https://doi.org/10.1007/s00392-022-02140-w

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  • DOI: https://doi.org/10.1007/s00392-022-02140-w

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