Pulmonary hypertension (PH), a life-threatening condition, is commonly seen in patients with interstitial lung disease (ILD), significantly increasing their risk of morbidity and mortality. Accurate and timely detection remains a clinical challenge, prompting the need for non-invasive diagnostic tools and prediction models to improve outcomes.
A recent narrative review highlights the limitations and potential of current non-invasive methods. Doppler echocardiography, specifically estimated right ventricular systolic pressure (RVSP), remains the most reliable tool, though it faces challenges in patients with advanced lung disease due to poor imaging quality. The review also examined composite scores derived from imaging, pulmonary function tests, and cardiopulmonary exercise tests, finding that most lack validation in diverse patient populations.
Of the tools assessed, only two predictive models showed consistent accuracy in identifying PH. One utilizes a stepwise echocardiographic approach, while the other relies on functional parameters. However, these approaches face methodological challenges that limit their generalizability.
The study emphasizes the potential role of AI in refining PH diagnostics. By integrating multimodal data, AI could enable earlier detection and personalized treatment strategies, a prospect that demands further research.
As PH remains a serious complication in ILD, this review underscores the importance of improving non-invasive screening to facilitate timely referrals to specialized centers. Advances in this field could revolutionize care for patients with ILD, offering hope for improved outcomes.
Reference: Arvanitaki A et al. Noninvasive diagnostic modalities and prediction models for detecting pulmonary hypertension associated with interstitial lung disease: a narrative review. Eur Respir Rev. 2024;33(174):240092.
Anaya Malik | AMJ
