The Achilles heel of mitral valve repair
Mitral valve repair is the ideal surgical option for patients with
mitral valve regurgitation. When performed by expert surgeons, it
provides excellent long term outcomes with lower mortality and reduced
operative risk, compared with mitral valve replacement [1][2].
One notable exception is in ischemic mitral regurgitation where
recurrent regurgitation post repair is a significant limitation of
surgery as it is experienced by a large percentage of patients and has
significant clinical impact. For example, in the Cardiothoracic Surgical
Trials Network (CTSN) randomized controlled trial of repair versus
replacement for ischemic mitral regurgitation (MR), at 2 years, 35% of
patients experienced recurrent moderate or greater MR as compared to 2%
for mitral replacement [3, 4]. In fact, long term follow-up
demonstrates that nearly 30% of patients undergoing repair for ischemic
MR will have recurrent regurgitation [5] which is associated with
a three times greater risk of death at 10 years and increased risk of
hospital readmission for CHF and reoperation [5]. Patients with
recurrent MR post repair also have less cardiac reverse remodelling with
smaller improvements in body-surface area indexed left ventricular
end-systolic volumes, compared to those without recurrent MR [4]. At
the patient level, those with recurrent MR have a lower quality of life
scores as measured by the Minnesota Living with Heart Failure
questionnaire compared with those who do not [4]. Overall, patients
with recurrent MR after mitral valve repair for ischemic regurgitation
unfortunately do not benefit from the surgical undertaking. Thus,
identifying differences between those with and without a durable repair
is crucial to improve the outcomes of this surgery.
In this issue of the Journal, Kachroo et al. address this issue
by using the STS database to identify features that would predict an
unsuccessful repair for ischemic mitral regurgitation. The authors
applied Machine Learning (ML) methods to data from 173 patients to
predict recurrent MR (> Grade 2) or death versus
non-recurrence at one year. They trained three different ML algorithms
(Support Vector Machine, Logistic Regression and Deep Neural network)
and included 53 preoperative predictors relating to demographics,
comorbidities, coronary artery disease architecture and bypass targeted
vessels. They found that all three models performed well with AUC values
between 0.72 -0.75. Important predictors for recurrent significant
regurgitation identified by the models included the necessity of bypass
to the ramus or the second obtuse marginal or the second diagonal
coronary arteries, the presence of peripheral vascular disease, and the
use of beta blockers. Through this paper, the authors have successfully
sowed the seeds for the use of ML with structured, nationally collected
STS data. The combination of the use of the STS database with ML
methods will also allowed for effective use of data for modelling and
identification of predictors of recurrent MR using a data-driven
methodology.