External validation of machine learning prediction model for pulmonary hypertension due to left heart disease

Abstract

A machine learning (ML) model improved the prediction of pulmonary hypertension due to left heart disease (PH-LHD) in suspected pulmonary arterial hypertension (PAH) subjects using non-invasive predictors. (<i>K. Swinnen et al.</i>, ERS 2021) We aimed to externally validate this model to assess its generalizability to different populations. The ML model was developed using non-invasive predictors from 344 subjects from the University Hospitals of Leuven; demographics, medical history, echocardiographic, ECG and lung function parameters, and lab results were extracted from patient files. During internal validation (n=104), the ML model outperformed the Jacobs score (<i>W. Jacobs et al.</i>, ERJ 2015) with sensitivity 70%, specificity 100%, positive predictive value (PPV) 100% and negative predictive value (NPV) 78%. The external validation was performed in a cohort of 165 subjects (91 PAH and 74 PH-LHD) from Erasme Hospital Brussels. The ML model achieved similar performances (sensitivity 64%, specificity 100%, PPV 100% and NPV 78%) during external validation. Of the 74 PH-LHD subjects, 47 were correctly identified non-invasively without false positives <i>versus</i> 2 with the Jacobs score. The cohorts had significant differences in baseline characteristics, including important parameters contributing to the model predictions. Despite differences between populations, the ML model showed good generalizability when tested on subjects from a different centre with only a slight decrease in sensitivity. This model is ready to be tested in clinical practice and could substantially reduce the number of right heart catheterizations in patients with high probability for PH-LHD.