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Published on: July 29, 2011
Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning
Ree Lu1, Heidi S Lumish1, Kohei Hasegawa2
1Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
Insights
A new machine learning (ML) model accurately predicts new-onset atrial fibrillation (AF) in hypertrophic cardiomyopathy (HCM) patients. This ML model significantly outperforms the traditional HCM-AF score, offering improved prediction for AF in HCM.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Atrial fibrillation (AF) is a common complication in hypertrophic cardiomyopathy (HCM), increasing symptoms and thromboembolic risk.
- Existing tools like the HCM-AF score have limited accuracy in predicting new-onset AF in HCM patients.
- There is a critical need for more precise predictive models for AF in the HCM population.
Purpose of the Study:
- To develop and validate a novel machine learning (ML) model for predicting new-onset AF in patients with HCM.
- To compare the predictive performance of the ML model against the conventional HCM-AF score.
Main Methods:
- A prospective, multicentre cohort study included 1069 HCM patients without prior AF.
- A logistic regression model with Lasso regularization was developed using clinical variables from a training set and validated on an independent test set.
- Performance was assessed by comparing the area under the receiver-operating characteristic curve (AUC) with the HCM-AF score.
Main Results:
- The ML model achieved an AUC of 0.84 in the test set, significantly outperforming the HCM-AF score (AUC 0.64).
- The ML model demonstrated superior sensitivity (0.82 vs. 0.67) and specificity (0.76 vs. 0.57) compared to the HCM-AF score.
- Key predictors identified included left atrial volume/diameter, LV outflow tract gradient, late gadolinium enhancement, peak heart rate, age, genotype, diabetes, and ESRD.
Conclusions:
- The developed ML model significantly improves the prediction of new-onset AF in HCM patients.
- This ML approach offers a more accurate and robust tool compared to the current HCM-AF score.
- The findings support the clinical utility of ML in identifying high-risk HCM patients for AF development.
Aims:
Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), leading to increased symptom burden and risk of thromboembolism. The HCM-AF score was developed to predict new-onset AF in patients with HCM, though sensitivity and specificity of this conventional tool are limited. Thus, there is a need for more accurate tools to predict new-onset AF in HCM. The objective of the present study was to develop a better model to predict new-onset AF in patients with HCM using machine learning (ML).
Methods And Results:
In this prospective, multicentre cohort study, we enrolled 1069 patients with HCM without a prior history of AF. We built a ML model (logistic regression with Lasso regularization) using clinical variables. We developed the ML model using the cohort from one institution (training set) and applied it to an independent cohort from a separate institution (test set). We used the HCM-AF score as a reference model. We compared the area under the receiver-operating characteristic curve (AUC) between the ML model and the reference model using the DeLong's test. Median follow-up time was 2.1 years, with 128 (12%) patients developing new-onset AF. Using the ML model developed in the training set to predict new-onset AF, the AUC in the test set was 0.84 (95% confidence interval [CI] 0.77-0.91). The ML model outperformed the reference model (AUC 0.64; 95% CI 0.54-0.73; DeLong's p < 0.001). The ML model had higher sensitivity (0.82; 95% CI 0.65-0.93) than that of the reference model (0.67; 95% CI 0.52-0.88). The ML model also had higher specificity (0.76; 95% CI 0.71-0.81) than that of the reference model (0.57; 95% CI 0.41-0.70). Among the most important clinical variables included in the ML-based model were left atrial volume and diameter, left ventricular outflow tract gradient with exercise stress and at rest, late gadolinium enhancement on cardiac magnetic resonance imaging, peak heart rate during exercise stress, age at diagnosis, positive genotype, diabetes mellitus, and end-stage renal disease.
Conclusion:
Our ML model showed superior performance compared to the conventional HCM-AF score for the prediction of new-onset AF in patients with HCM.
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