Machine learning offers the opportunity to gain new insights into clinical cardiovascular disease as shown in the paper in this issue of Heart on novel patient phenogroups in heart failure with preserved ejection fraction (HFpEF).1 Model-based clustering was performed based on echocardiographic, clinical and laboratory variables (including proteomics) in 320 outpatients (mean age 78 years, 56% female) with HFpEF to identify six phenogroups with significant differences between groups in the composite outcome of all-cause mortality or heart failure hospitalisation at up to 2½ years follow-up. The highest event rates occurred in phenogroup 2, characterised by older age, reduced right ventricular function, atrial fibrillation in 85%, hypertension in 83% and chronic obstructive pulmonary disease in 30%. Poor outcomes were also seen in phenogroup 1 which was defined by the presence of hypertension, coronary disease, renal disease, diabetes and anaemia (figure 1).