DISCUSSION
The HCM-AF-Risk Model is the first machine learning-based method for the identification of AF cases and clinical features associated with higher/lower risk of AF in HCM, using electronic health record data. In our model, individual patient data is represented as an N-dimensional vector, and the model output is a probability score for AF (AF risk) in HCM. We identified 18 clinical variables that are highly associated (positively/negatively) with AF in HCM patients. In addition to age, NYHA class, LA size and LV fibrosis that have been previously associated with AF in HCM, we found additional clinical features such as LV diastolic dysfunction/lower LV-systolic strain that are positively associated with AF, and greater exercise capacity that is negatively associated with AF in HCM.
HCM-AF-Risk Model : Employing a statistical machine learning method is advantageous as it allows automatic quantification of the likelihood of an event (AF, in this case) based on the combination of eature values - as obtained from the patient’s electronic health records - and their level of association with the event. Moreover, unlike traditional rule based models, machine learning methods are robust in the face of new data, as these methods can tune and update their parameters, which govern the classification algorithm based on the added data. Thus, machine learning methods are well-suited for use in the clinical setting we additional patients’ data is frequently accumulated.
In contrast to the majority of current ‘black-box’ machine learning methods that are based on artificial neural networks [29, 37, 38] and whose output decision typically cannot be explained, our method is based on modeling a clear probabilisitic decision process, that can be tracked back and used to justifiy the decision – we believe that this is a critical aspect when using machine learning for supporting clinical decisions. Our HCM-AF-Risk Model addresses data imbalance, and utilizes a set of 18 clinical variables to identify AF cases, and clinical features associated with higher/lower risk for AF in HCM patients.
We note that heart failure, along with VT/VF and stroke, were not included in the list of clinical variables considered by our method. This is because our goal is to identify demographic, clinical, and imaging features that predict adverse outcomes (AF in this case) in HCM patients, and using such adverse outcomes as predictors defeats this purpose. However, as several previous studies including the Framingham Heart Study,[8] ARIC [10] and CHARGE-AF [9] have shown heart failure to be a predictor of risk for AF, we assessed the performance of our model while including heart failure. Inclusion of heart failure did not increase AUC or sensitivity, but led to a slight increase in specificity of our model, from 0.72 to 0.73.
Clinical predictors of AF in HCM using the HCM-AF-Risk Model:Left atrial diameter is the strongest predictor of AF in our study. The association between LA size and AF has been extensively documented in the general population[39-43] and HCM patients.[2, 44-48] The association between LA enlargement and AF has been attributed to stretch-induced LA structural and electrophysiologic remodeling.[49] In the case of HCM, since most causal HCM mutations are expressed in both atrial and ventricular myocytes, atrial myopathy and LV diastolic dysfunction could underlie the high prevalence of AF in HCM.
Our HCM-AF-Risk Model indicates an association between diastolic dysfunction and AF in HCM. We found that higher values for E/A, E/e′[50] and lower (worse) global diastolic strain rate reflecting greater degree of diastolic dysfunction are associated with higher risk for AF in HCM. Similar results have been reported in studies conducted in non-HCM patients.[39, 51, 52] The mechanism whereby diastolic dysfunction has been proposed to predispose to AF is by increasing LA preload (stretch), afterload and wall stress (dilation), which lead to ion channel remodeling, fibrosis and increase susceptibility for reentrant arrhythmias such as atrial fibrillation/flutter. [51]
Left ventricular fibrosis (LV-LGE) and worse LV global longitudinal peak systolic strain rate, which reflect greater degree of LV myopathy are associated with AF in our model. Several previous studies have detected an association between LV fibrosis and AF in HCM.[53-55] A recent CMR study in HCM patients reported greater amounts of LA fibrosis in HCM patients with PAF, as well as a positive association between atrial and ventricular fibrosis (LGE).[56] Since fibrosis slows conduction and predisposes to reentry, LA fibrosis would be expected to increase risk for AF.
Lower exercise capacity, lower chronotropic response/heart rate recovery, abnormal BP response to exercise and lower diastolic BP at peak exercise are associated with higher risk for AF in our study. Similar results of exercise intolerance in HCM patients with PAF have been reported in a previous study of 265 HCM patients during sinus rhythm[57] – here, the authors did not observe an association between lower exercise capacity and diastolic dysfunction or LA volume. Additionally, ECHO[58] and CMR[5, 56] studies in HCM patients have revealed greater impairment of LA function and greater degree of LA fibrosis in HCM patients with PAF, suggesting that PAF is a marker of LA myopathy.
One mechanism underlying reduced exercise capacity in HCM patients (with PAF), even during sinus rhythm[57] could be impairment of LV hemodynamics in the setting of LA myopathy, since the LA modulates LV performance by its reservoir function during ventricular systole, conduit function during early ventricular diastole and booster pump function durimg late ventricular diastole. A second possibility is higher pulmonary capillary wedge pressure (PCWP) in HCM patients with AF, based on results of a study in 123 patients who underwent simultaneous left and right heart catheterization, where PCWP was higher than LV end-diastolic pressure (LVEDP) among AF patients and lower than LVEDP among patients in sinus rhythm.[59] Other contributors to lower exercise capacity in HCM patients with AF include sympathovagal imbalance[60] leading to systemic vasodilation, chronotropic incompetence induced by atrial remodeling/medications, and greater degree of LV myopathy.
Comparison of predictors for atrial fibrillation and ventricular arrhythmias identified by the HCM-AF-Risk and HCM-VAr-Risk Models: In an earlier study[14] we developed a machine-learning based model for predicting lethal ventricular arrhythmias (VT/VF) in HCM patients. We identified 5 predictors (exercise time, METs, E/e′ ratio, LV global longitudinal peak systolic strain rate and LV global longitudinal early diastolic strain rate) that are common in the two models and13 variables associated with AF, but not VT/VF (Supplementary Table 3).
Higher age is associated with increased risk for AF, but lower risk for VT/VF, which has been confirmed by other studies.[2, 61] HCM type (non-obstructive), family history of HCM or sudden cardiac death and non-sustained VT are associated with VT/VF but not AF, which may reflect differences in arrhythmic substrate in the LV and LA in HCM. Notably, LV hypertrophy (max IVS thickness, IVS/PW ratio) is associated with VT/VF but not AF – higher risk for VT/VF but not AF could be attributed to greater degree of myocardial ischemia,[62] interstitial fibrosis[63] and myocyte disarray [64] in the hypertrophied LV.[65] The association of replacement fibrosis (LV-LGE) with AF but not VT/VF could reflect the impact of greater degree of diastolic dysfunction induced by LV fibrosis resulting in LA dilatation/remodeling and AF. Taken together, our results suggest distinct pathophysiologic mechanisms underlying atrial and ventricular arrhythmias in HCM (Supplementary Table 3).