Key result
Machine learning models improved one-year stroke prediction with an AUC of 0.653 (95% CI 0.576-0.730) compared to CHADS2 (AUC 0.587) and CHA2DS2-VASc (AUC 0.535) scores.
Why the study?
Clinical risk scores predicting outcomes in AF have modest predictive value, while machine learning may better predict adverse outcomes in recently diagnosed AF.
Do machine learning models improve the prediction of adverse outcomes compared to traditional clinical risk scores in patients with non-valvular atrial fibrillation?
Cohort (n=26,183)
Do machine learning models improve the prediction of adverse outcomes compared to traditional clinical risk scores in patients with non-valvular atrial fibrillation?
Effect estimate: AUC 0.653 (95% CI 0.576-0.730)
Machine learning models significantly improve the prediction of stroke, major bleeding, and mortality in patients with non-valvular atrial fibrillation compared to traditional clinical risk scores.
ML may refine AF risk stratification; hypothesis-generating and requires prospective validation before practice change.
Clinical risk scores that predict outcomes in patients with atrial fibrillation (AF) have modest predictive value. Machine learning (ML) may achieve greater results when predicting adverse outcomes in patients with recently diagnosed AF. Several ML models were tested and compared with current clinical risk scores on a cohort of 26,183 patients (mean age 70.13 (standard deviation 10.13); 44.8% female) with non-valvular AF. Inputted into the ML models were 23 demographic variables alongside comorbidities and current treatments. For one-year stroke prediction, ML achieved an area under the curve (AUC) of 0.653 (95% confidence interval 0.576–0.730), compared to the CHADS2 and CHA2DS2-VASc scores performance of 0.587 (95% CI 0.559–0.615) and 0.535 (95% CI 0.521–0.550), respectively. Using ML for one-year major bleed prediction increased the AUC from 0.537 (95% CI 0.518–0.557) generated by the HAS-BLED score to 0.677 (95% CI 0.619–0.724). ML was able to predict one-year and three-year all-cause mortality with an AUC of 0.734 (95% CI 0.696–0.771) and 0.742 (95% CI 0.718–0.766). In this study a significant improvement in performance was observed when transitioning from clinical risk scores to machine learning-based approaches across all applications tested. Obtaining precise prediction tools is desirable for increased interventions to reduce event rates. Trial Registry https://www.clinicaltrials.gov ; Unique identifier: NCT01468701, NCT01671007, NCT01937377.
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Joddrell et al. (2024) conducted a cohort in Non-valvular atrial fibrillation (n=26,183). Machine learning models vs. Clinical risk scores (CHADS2, CHA2DS2-VASc, HAS-BLED) was evaluated on One-year stroke prediction (AUC 0.653, 95% CI 0.576-0.730). Machine learning models improved one-year stroke prediction with an AUC of 0.653 (95% CI 0.576-0.730) compared to CHADS2 (AUC 0.587) and CHA2DS2-VASc (AUC 0.535) scores.
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