INTRODUCTION
Hypertrophic Cardiomyopathy (HCM) is characterized by myocyte
hypertrophy, myocyte disarray, interstitial/replacement fibrosis, and is
associated with a high risk for atrial and ventricular arrhythmias. A
large proportion (~25-30%) of HCM patients develop
atrial fibrillation (AF) during their lifetime.[1] Notably, HCM
patients with AF are at increased risk for stroke even in the setting of
low CHA2DS2-VASc scores.[1-3]
Furthermore, stroke can be the first manifestation of AF in HCM. Hence
there is a need to understand AF pathophysiology and assess AF risk in
HCM patients.[4]
Several small studies have reported an association between age,[5]
NYHA class,[6] left atrial (LA) size/function,[5-7] EKG-P-wave
dispersion,[7] N-terminal proB-type natriuretic peptide (NT-proBNP)
levels,[7] fibrosis in the left ventricle (LV)[6] and AF in HCM
patients. Clinical risk scores for AF prediction have been developed and
validated using general populations from the Framingham Heart Study
(FHS),[8] Cardiovascular Heart Study (CHS),[9] Atherosclerosis
Risk in Communities Study (ARIC),[10] Multi-Ethnic Study of
Atherosclerosis Study (MESA)[11], Reykjavik Study (AGES)[12] and
Rotterdam Study (RS).[13] But it is unknown whether these models are
effective in assessing AF risk in HCM, given the differences in cardiac
physiology between HCM patients and the general population.
We hypothesized that a machine learning-based model developed using HCM
patients would perform better than existing models (that were developed
using clinical data from the general population) to assess risk factors
for AF in HCM. In order to achieve this goal, as a first step, we
retrospectively identify AF cases and clinical features associated with
higher/lower risk of AF, using electronic health record (EHR) data of
HCM patients who underwent deep clinical phenotyping by multi-modality
imaging. Similar to other machine learning methods and our work in
ventricular arrhythmias, [14] our method has the advantage of
allowing for thorough, unbiased scanning of the data along several
dimensions, which permits derivation of a classification algorithm that
stratifies HCM cases based on their respective likelihood to present
with AF. Importantly, this method is flexible, can be easily updated as
additional clinical data becomes available, and provides insights into
the pathophysiology of AF in HCM patients. The next step is prospective
testing of our model to predict AF development in HCM.