Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients

Abstract

Background: Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response.

Methods and results: The Swedish Heart Failure Registry is a nationwide registry collecting detailed demographic, clinical, laboratory, and medication data and linked to databases with outcome information. We applied random forest modeling to identify predictors of 1-year survival. Cluster analysis was performed and validated using serial bootstrapping. Association between clusters and survival was assessed with Cox proportional hazards modeling and interaction testing was performed to assess for heterogeneity in response to HF pharmacotherapy across propensity-matched clusters. Our study included 44 886 HF patients enrolled in the Swedish Heart Failure Registry between 2000 and 2012. Random forest modeling demonstrated excellent calibration and discrimination for survival (C-statistic=0.83) whereas left ventricular ejection fraction did not (C-statistic=0.52): there were no meaningful differences per strata of left ventricular ejection fraction (1-year survival: 80%, 81%, 83%, and 84%). Cluster analysis using the 8 highest predictive variables identified 4 clinically relevant subgroups of HF with marked differences in 1-year survival. There were significant interactions between propensity-matched clusters (across age, sex, and left ventricular ejection fraction and the following medications: diuretics, angiotensin-converting enzyme inhibitors, β-blockers, and nitrates, P<0.001, all).

Conclusions: Machine learning algorithms accurately predicted outcomes in a large data set of HF patients. Cluster analysis identified 4 distinct phenotypes that differed significantly in outcomes and in response to therapeutics. Use of these novel analytic approaches has the potential to enhance effectiveness of current therapies and transform future HF clinical trials.

Keywords: heart failure; machine learning; outcomes research.

© 2018 The Authors and Qure.ai. Published on behalf of the American Heart Association, Inc., by Wiley.

Figures

Figure 1

Survival curves according to cluster and ejection fraction groups. Survival curves per (A) cluster and (B) left ventricular ejection fraction groups.

Figure 2

Receiver operating characteristic curves for prediction of all‐cause mortality at 1 year.

Figure 3

Online tool for prediction of outcomes and assignment of patient into cluster (http://hfcalculator.qure.ai).

Figure 4

Heterogeneity in response to heart failure therapies per patient clusters that are propensity matched for age, sex, and left ventricular ejection fraction. ACE indicates angiotensin‐converting enzyme; ARB, angiotensin receptor blocker; CI, confidence interval.

Figure 5

Interaction between heart failure therapies and clusters that are propensity matched for age, sex, and left ventricular ejection fraction. ACE indicates angiotensin‐converting enzyme; ARB, angiotensin receptor blocker; CI, confidence interval.

Figure 6

Current and future paradigm for prognostication and testing of therapeutics in patients with heart failure using machine learning. AA indicates Aldosterone Antagonist; ACE‐I, angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; ARNI, Angiotensin Receptor‐Neprilysin Inhibitor; AUC, area under the curve; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.