Noninvasive diagnostic modalities and prediction models for detecting pulmonary hypertension associated with interstitial lung disease: a narrative review.

Arvanitaki A; National Pulmonary Hypertension Service, Royal Brompton Hospital, London, UK.;
Diller GP; Gatzoulis MA; McCabe C; Price LC

European respiratory review : an official journal of the European Respiratory Society [Eur Respir Rev] 2024 Oct 09; Vol. 33 (174).
Date of Electronic Publication: 2024 Oct 09 (Print Publication: 2024).

Pulmonary hypertension (PH) is highly prevalent in patients with interstitial lung disease (ILD) and is associated with increased morbidity and mortality. Widely available noninvasive screening tools are warranted to identify patients at risk for PH, especially severe PH, that could be managed at expert centres. This review summarises current evidence on noninvasive diagnostic modalities and prediction models for the timely detection of PH in patients with ILD. It critically evaluates these approaches and discusses future perspectives in the field. A comprehensive literature search was carried out in PubMed and Scopus, identifying 39 articles that fulfilled inclusion criteria. There is currently no single noninvasive test capable of accurately detecting and diagnosing PH in ILD patients. Estimated right ventricular pressure (RVSP) on Doppler echocardiography remains the single most predictive factor of PH, with other indirect echocardiographic markers increasing its diagnostic accuracy. However, RVSP can be difficult to estimate in patients due to suboptimal views from extensive lung disease. The majority of existing composite scores, including variables obtained from chest computed tomography, pulmonary function tests and cardiopulmonary exercise tests, were derived from retrospective studies, whilst lacking validation in external cohorts. Only two available scores, one based on a stepwise echocardiographic approach and the other on functional parameters, predicted the presence of PH with sufficient accuracy and used a validation cohort. Although several methodological limitations prohibit their generalisability, their use may help physicians to detect PH earlier. Further research on the potential of artificial intelligence may guide a more tailored approach, for timely PH diagnosis.
Competing Interests: Conflict of interest: A. Arvanitaki, M.A. Gatzoulis and C. McCabe declare no conflicts of interest relevant to this work. G.P. Diller has received honoraria and travel grants from Janssen Global. L.C. Price has received consultancy fees from Janssen and educational support and conference support from Janssen and Ferrer. S.J. Wort has received consultancy fees from Janssen, Acceleron, MSD, Ferrer and Bayer, honoraria from Janssen, Acceleron, MSD, Ferrer and Bayer, as well as travel and research grants from Janssen and Ferrer.