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Predicting cerebral infarction in patients with atrial fibrillation using machine learning: The Fushimi AF registry
Hidehisa Nishi1,2, Naoya Oishi3, Hisashi Ogawa4
1Department of Neurosurgery, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.
Insights
Machine learning models show improved prediction of cerebral infarction in atrial fibrillation (AF) patients compared to traditional CHADS2 and CHA2DS2-VASc scores. This AI approach offers better risk assessment for non-valvular AF, enhancing clinical decision-making.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Current risk scores like CHADS2 and CHA2DS2-VASc have limited accuracy in predicting ischemic events in atrial fibrillation (AF) patients.
- Accurate prediction of cerebral infarction is crucial for effective anticoagulation management in AF.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for predicting cerebral infarction in non-valvular AF patients.
- To compare the predictive performance of the ML model against established CHADS2 and CHA2DS2-VASc scores.
Main Methods:
- A prospective cohort study enrolled 4396 AF patients, with data split into derivation (1005) and validation (752) cohorts after exclusions.
- A gradient boosting tree-based machine learning model was constructed using the derivation cohort to predict cerebral infarction.
- Model performance was evaluated in the validation cohort using the Hanley and McNeil method for receiver operating characteristic area under the curve (AUC).
Main Results:
- The machine learning model achieved a higher AUC (0.72) compared to CHADS2 (0.61) and CHA2DS2-VASc (0.62) scores in the validation cohort.
- The ML model demonstrated superior discrimination for predicting cerebral infarction in the studied non-valvular AF population.
Conclusions:
- Machine learning algorithms offer enhanced predictive capabilities for cerebral infarction in non-valvular AF patients.
- The developed ML model presents a promising alternative to existing scores for improving ischemic risk stratification in AF.
Abstract:
The CHADS2 and CHA2DS2-VASc scores are widely used to assess ischemic risk in the patients with atrial fibrillation (AF). However, the discrimination performance of these scores is limited. Using the data from a community-based prospective cohort study, we sought to construct a machine learning-based prediction model for cerebral infarction in patients with AF, and to compare its performance with the existing scores. All consecutive patients with AF treated at 81 study institutions from March 2011 to May 2017 were enrolled (n = 4396). The whole dataset was divided into a derivation cohort (n = 1005) and validation cohort (n = 752) after excluding the patients with valvular AF and anticoagulation therapy. Using the derivation cohort dataset, a machine learning model based on gradient boosting tree algorithm (ML) was built to predict cerebral infarction. In the validation cohort, the receiver operating characteristic area under the curve of the ML model was higher than those of the existing models according to the Hanley and McNeil method: ML, 0.72 (95%CI, 0.66-0.79); CHADS2, 0.61 (95%CI, 0.53-0.69); CHA2DS2-VASc, 0.62 (95%CI, 0.54-0.70). As a conclusion, machine learning algorithm have the potential to perform better than the CHADS2 and CHA2DS2-VASc scores for predicting cerebral infarction in patients with non-valvular AF.
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