European journal of clinical investigation · 2024
Machine learning for stroke in heart failure with reduced ejection fraction but without atrial fibrillation: A post-hoc analysis of the WARCEF trial.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
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Ishiguchi Hironori, Chen Yang, Huang Bi, Gue Ying, Correa Elon, Homma Shunichi, Thompson John L P, Qian Min, Lip Gregory Y H, Abdul-Rahim Azmil H
Аннотация
BACKGROUND: The prediction of ischaemic stroke in patients with heart failure with reduced ejection fraction (HFrEF) but without atrial fibrillation (AF) remains challenging. Our aim was to evaluate the performance of machine learning (ML) in identifying the development of ischaemic stroke in this population.
METHODS: We performed a post-hoc analysis of the WARCEF trial, only including patients without a history of AF. We evaluated the performance of 9 ML models for identifying incident stroke using metrics including area under the curve (AUC) and decision curve analysis. The importance of each feature used in the model was ranked by SAPley Additive exPlanations (SHAP) values.
RESULTS: We included 2213 patients with HFrEF but without AF (mean age 58 ± 11 years; 80% male). Of these, 74 (3.3%) had an ischaemic stroke in sinus rhythm during a mean follow-up of 3.3 ± 1.8 years. Out of the 29 patient-demographics variables, 12 were selected for the ML training. Almost all ML models demonstrated high AUC values, outperforming the CHA2DS2-VASc score (AUC: 0.643, 95% confidence interval [CI]: 0.512-0.767). The Support Vector Machine (SVM) and XGBoost models achieved the highest AUCs, with 0.874 (95% CI: 0.769-0.959) and 0.873 (95% CI: 0.783-0.953), respectively. The SVM and LightGBM consistently provided significant net clinical benefits. Key features consistently identified across these models were creatinine clearance (CrCl), blood urea nitrogen (BUN) and warfarin use.
CONCLUSIONS: Machine-learning models can be useful in identifying incident ischaemic strokes in patients with HFrEF but without AF. CrCl, BUN and warfarin use were the key features.
METHODS: We performed a post-hoc analysis of the WARCEF trial, only including patients without a history of AF. We evaluated the performance of 9 ML models for identifying incident stroke using metrics including area under the curve (AUC) and decision curve analysis. The importance of each feature used in the model was ranked by SAPley Additive exPlanations (SHAP) values.
RESULTS: We included 2213 patients with HFrEF but without AF (mean age 58 ± 11 years; 80% male). Of these, 74 (3.3%) had an ischaemic stroke in sinus rhythm during a mean follow-up of 3.3 ± 1.8 years. Out of the 29 patient-demographics variables, 12 were selected for the ML training. Almost all ML models demonstrated high AUC values, outperforming the CHA2DS2-VASc score (AUC: 0.643, 95% confidence interval [CI]: 0.512-0.767). The Support Vector Machine (SVM) and XGBoost models achieved the highest AUCs, with 0.874 (95% CI: 0.769-0.959) and 0.873 (95% CI: 0.783-0.953), respectively. The SVM and LightGBM consistently provided significant net clinical benefits. Key features consistently identified across these models were creatinine clearance (CrCl), blood urea nitrogen (BUN) and warfarin use.
CONCLUSIONS: Machine-learning models can be useful in identifying incident ischaemic strokes in patients with HFrEF but without AF. CrCl, BUN and warfarin use were the key features.