Article Text
Abstract
Objective Electrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is difficult. The objective was to develop sex-specific prediction models for successful electrical cardioversion and assess the potential of machine learning methods in comparison with traditional logistic regression.
Methods In a retrospective cohort study, we examined several candidate predictors, including comorbidities, biochemistry, echocardiographic data, and medication. The outcome was successful cardioversion, defined as normal sinus rhythm immediately after the electrical cardioversion and no documented recurrence of AF within 3 months after. We used random forest and logistic regression models for sex-specific prediction.
Results The cohort comprised 332 female and 790 male patients with persistent AF who underwent electrical cardioversion. Cardioversion was successful in 44.9% of the women and 49.9% of the men. The prediction errors of the models were high for both women (41.0% for machine learning and 48.8% for logistic regression) and men (46.0% for machine learning and 44.8% for logistic regression). Discrimination was modest for both machine learning (0.59 for women and 0.56 for men) and logistic regression models (0.60 for women and 0.59 for men), although the models were well calibrated.
Conclusions Sex-specific machine learning and logistic regression models showed modest predictive performance for successful electrical cardioversion. Identifying patients who will benefit from cardioversion remains challenging in clinical practice. The high recurrence rate calls for thoroughly informed shared decision-making for electrical cardioversion.
- atrial fibrillation
- gender
- statistics
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Footnotes
Contributors NV, ASF, LF, AEA and DSM participated in the original planning, conduct and design of the study; collected the data. NV, MF-G and LT performed the statistical analyses. NV, LT, MF-G and LF drafted the manuscript and ASF, AEA, GYHL and DSM provided manuscript editing and comments and suggestions.
Funding An unrestricted grant from Bristol-Myers Squibb (BMS) and Pfizer supported this study.
Disclaimer The sponsor had no role in the study design, in the collection and interpretation of the data, in the writing of this report, or in the decision to submit the article for publication.
Competing interests AEA: has been on the speaker bureaus for Astra Zenica, Bayer, BMS, Boehringer Ingelheim and Pfizer. GYHL: consultant for Bayer/Janssen, BMS/Pfizer, Medtronic, Boehringer Ingelheim, Novartis, Verseon and Daiichi-Sankyo. Speaker for Bayer, BMS/Pfizer, Medtronic, Boehringer Ingelheim and Daiichi-Sankyo. No fees are directly received personally. LT: is supported by a grant from AHA (18SFRN34150007). LF: has been an advisory board member for BMS, MSD and Pfizer in relation to non-interventional studies and has been on the speaker bureaus for Bayer, BMS, Boehringer Ingelheim, MSD and Pfizer. DSM: has been on the speaker bureaus for Bayer, BMS, Boehringer Ingelheim, MSD and Pfizer.
Patient consent for publication Not required.
Ethics approval The Danish Data Protection Agency (1-16-02-427-15) and the Medicines Authority (3-3013-1165/1) approved this study. Approval from an Ethics Committee was not required according to Danish law.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement No data are available. Data cannot be made available as access to patient records and public sharing of data are not legal, cf. Danish law.