Early Diagnosis in Pulmonary Arterial Hypertension: The Search for the Holy Grail

Prognostic biomarkers in pediatric pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a progressive life-threatening disease of the pulmonary vasculature. Despite the introduction of targeted therapies, prognosis remains poor. In pediatric PAH, reliable prognostic biomarkers are needed to inform clinicians on disease progression and risk of mortality, in order to be able to assess the need for escalation of medical therapy, consider surgical options such as Pott's shunt and listing for (heart)-lung transplantation. This review provides an overview of prognostic biomarkers that are considered to carry potential for the clinical management of pediatric PAH. These include conventional physiological biomarkers [resting heart rate, heart rate variability (HRV), a child's growth], biomarkers of functional status [World Health Organization functional class, 6-minute walk distance (6MWD), parameters derived from cardiopulmonary exercise testing (CPET), daily physical activity level], electrocardiographic biomarkers, circulating serum biomarkers (natriuretic peptides, uric acid, neurohormones, inflammatory markers, and novel circulating biomarkers), and multiple hemodynamic biomarkers and imaging biomarkers [echocardiography and cardiac magnetic resonance (CMR)]. In recent years, many potential prognostic biomarkers have become available for the management of PAH in children. As the available prognostic biomarkers reflect different aspects of the disease process and functional implications, a multi-marker approach appears the most useful for guiding therapy decisions and improve outcome in pediatric PAH.

Screening strategies for pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is rare and, if untreated, has a median survival of 2-3 years. Pulmonary arterial hypertension may be idiopathic (IPAH) but is frequently associated with other conditions. Despite increased awareness, therapeutic advances, and improved outcomes, the time from symptom onset to diagnosis remains unchanged. The commonest symptoms of PAH (breathlessness and fatigue) are non-specific and clinical signs are usually subtle, frequently preventing early diagnosis where therapies may be more effective. The failure to improve the time to diagnosis largely reflects an inability to identify patients at increased risk of PAH using current approaches. To date, strategies to improve the time to diagnosis have focused on screening patients with a high prevalence [systemic sclerosis (10%), patients with portal hypertension assessed for liver transplantation (2-6%), carriers of mutations of the gene encoding bone morphogenetic protein receptor type II, and first-degree relatives of patients with heritable PAH]. In systemic sclerosis, screening algorithms have demonstrated that patients can be identified earlier, however, current approaches are resource intensive. Until, recently, it has not been considered possible to screen populations for rare conditions such as IPAH (prevalence 5-15/million/year). However, there is interest in the use of artificial intelligence approaches in medicine and the application of diagnostic algorithms to large healthcare data sets, to identify patients at risk of rare conditions. In this article, we review current approaches and challenges in screening for PAH and explore novel population-based approaches to improve detection.