  Heart failure (HF) is a leading cause of morbidity, mortality, and health
care costs. Prolonged conduction through the myocardium can occur with HF, and
a device-driven approach, termed cardiac resynchronization therapy (CRT), can
improve left ventricular (LV) myocardial conduction patterns. While a
functional benefit of CRT has been demonstrated, a large proportion of HF
patients (30-50%) receiving CRT do not show sufficient improvement. Moreover,
identifying HF patients that would benefit from CRT prospectively remains a
clinical challenge. Accordingly, strategies to effectively predict those HF
patients that would derive a functional benefit from CRT holds great medical
and socio-economic importance. Thus, we used machine learning methods of
classifying HF patients, namely Cluster Analysis, Decision Trees, and
Artificial neural networks, to develop predictive models of individual outcomes
following CRT. Clinical, functional, and biomarker data were collected in HF
patients before and following CRT. A prospective 6-month endpoint of a
reduction in LV volume was defined as a CRT response. Using this approach (418
responders, 412 non-responders), each with 56 parameters, we could classify HF
patients based on their response to CRT with more than 95% success. We have
demonstrated that using machine learning approaches can identify HF patients
with a high probability of a positive CRT response (95% accuracy), and of equal
importance, identify those HF patients that would not derive a functional
benefit from CRT. Developing this approach into a clinical algorithm to assist
in clinical decision-making regarding the use of CRT in HF patients would
potentially improve outcomes and reduce health care costs.
