Use of artificial intelligence to predict survival in pulmonary hypertension

Abstract

BackgroundSurvival in pulmonary hypertension depends on right ventricular adaptation to pressure overload, but the three-dimensional (3D) patterns of function that are most discriminative of subsequent outcome are not known. The aim of this study was to compare the prognostic accuracy of artificial intelligence combined with 3D models of the right ventricle against conventional prognostic markers in predicting survival in patients with pulmonary hypertension. MethodsPatients with suspected pulmonary hypertension were prospectively enrolled and underwent cardiac MRI, right heart catheterisation, and 6-min walk testing, and received standard medical care and follow-up. From cardiac MRI, 3D right ventricular function models were developed and the patterns of function predictive of survival learnt by artificial intelligence. All-cause mortality prediction was compared between artificial intelligence and standard clinical markers using Cox proportional hazards. Findings256 patients had confirmed pulmonary hypertension, investigations within 3 months, and follow-up for a median of 4·0 years [IQR 2·0–5·7]. Artificial intelligence identified specific regional and directional patterns of function, which predicted survival and was a stronger predictor (median area under the curve 0·72, IQR 0·69–0·76; p<0·0001), better separator of high-risk and low-risk individuals by median survival time (median difference 4·1 years, IQR 3·6–4·6), and had a less variable threshold (5·2%, p<0·0001) than all other markers. Only artificial intelligence (hazard ratio 1·819, 95% CI 1·069–3·095; p=0·03) and 6-min walk testing (1·970, 1·171–3·314; p=0·01) were significant survival predictors when age and subtype of pulmonary hypertension were controlled for. Artificial intelligence identified separate patterns of dysfunction in pulmonary hypertension subtypes, which accurately predicted survival. InterpretationThe right ventricle in pulmonary hypertension demonstrates prognostic regional and directional adaptations that can be revealed by artificial intelligence methods, thereby improving risk-stratification and mechanistic insights. The variety of pulmonary hypertension subtypes and treatment regimens included might limit applicability to subgroups, but demonstrates efficacy across a spectrum of disease and treatment. Artificial intelligence methods could have important additional benefit in analysing complex cardiovascular datasets. FundingMedical Research Council UK, National Institute for Health Research Biomedical Research Centre, Imperial College Healthcare NHS Trust and Imperial College London, British Heart Foundation (project grant PG/12/27/29489, special grant SP/10/10/28431), Wellcome Trust GlaxoSmithKline Fellowship Grant.