Machine learning to differentiate pulmonary hypertension due to left heart disease from pulmonary arterial hypertension.

Background and aims

Pulmonary hypertension due to left heart disease (PH-LHD) is the most frequent form of PH. As differential diagnosis with pulmonary arterial hypertension (PAH) has therapeutic implications, it is important to accurately and noninvasively differentiate PH-LHD from PAH before referral to PH centres. The aim was to develop and validate a machine learning (ML) model to improve prediction of PH-LHD in a population of PAH and PH-LHD patients.

Methods

Noninvasive PH-LHD predictors from 172 PAH and 172 PH-LHD patients from the PH centre database at the University Hospitals of Leuven (Leuven, Belgium) were used to develop an ML model. The Jacobs score was used as performance benchmark. The dataset was split into a training and test set (70:30) and the best model was selected after 10-fold cross-validation on the training dataset (n=240). The final model was externally validated using 165 patients (91 PAH, 74 PH-LHD) from Erasme Hospital (Brussels, Belgium).

Results

In the internal test dataset (n=104), a random forest-based model correctly diagnosed 70% of PH-LHD patients (sensitivity: n=35/50), with 100% positive predicted value, 78% negative predicted value and 100% specificity. The model outperformed the Jacobs score, which identified 18% (n=9/50) of the patients with PH-LHD without false positives. In external validation, the model had 64% sensitivity at 100% specificity, while the Jacobs score had a sensitivity of 3% for no false positives.

Conclusions

ML significantly improves the sensitivity of PH-LHD prediction at 100% specificity. Such a model may substantially reduce the number of patients referred for invasive diagnostics without missing PAH diagnoses.

Informations de copyright

Copyright ©The authors 2023.

Déclaration de conflit d'intérêts

Conflict of interest: M. De Vos received funding from the AI in Flanders project. M. Topalovic is CEO and co-founder of Artiq but received no payments related to the manuscript. G. Claessen received a KOOR grant from his institution. C. Belge reports personal fees from Actelion/Janssen and MSD/Bayer, outside the submitted work. J-L. Vachiery received grants from Actelion/Janssen. W. Janssens is supported as senior clinical researcher of the Flemish Research Foundation, and received grants from AstraZeneca and Chiesi, and obtained fees from AstraZeneca, Chiesi and GlaxoSmithKline. He is chairman of Board of Flemish Society for TBC prevention and board member of Artiq. M. Delcroix received funding from Actelion/Janssen and consulting fees from MSD, Acceleron, Actelion/Janssen, AOP, Ferrer and Gossamer BIO. She also participates on a data safety monitoring or advisory board for Actelion/Janssen. K. Swinnen, K. Verstraete, C. Baratto, L. Hardy and R. Quarck have nothing to disclose.