Assessing risk in pulmonary arterial hypertension: what we know, what we don't

Abstract

Despite improvement in short-term survival, pulmonary arterial hypertension (PAH) remains an incurable disease with an unacceptable median survival of 7 years [1]. In the USA, the survival rates for PAH patients awaiting transplant continue to mirror the 2.5-year survival rate in the pretreatment era of the disease [2]. Despite treatment, PAH patients continue to experience disease progression and increased rates of hospitalisations due to right heart failure. Importantly, right heart failure hospitalisations in PAH occur at rates that are comparable to patients with left heart disease, particularly in those aged greater than 65 [3] More so, morbid events in PAH, notably hospitalisations, herald disease progression and early mortality [4,5]. Hence, along with advancing PAH treatment options, appropriate and accurate risk prediction is essential to halt disease progression and make individualised treatment decisions.In a progressive disease like PAH, early and accurate risk prediction allows for the identification of patients who are more likely to progress rapidly, "rapid progressors". Risk stratification is especially important in settings where clinical PAH experience is not available and could facilitate early referral to a PAH centre. A risk stratification algorithm could also offer a more individualised treatment strategy for PAH patients; by identifying risk stratum, guiding clinical decision making and informing treatment options and goals. Risk prediction modelling can help physicians allocate treatment resources in settings where they are scarce. They can also be used to inform patients of their prognosis thereby allowing them to make informed decisions about treatment options. If widely adopted, appropriate risk prediction provides an opportunity to learn about various risk phenotypes in PAH, enhance consistency of treatment approaches across practitioners and assist in the timely referral for lung transplantation. Lastly, risk model-derived equations can enhance clinical study design both by selecting the appropriate study cohort and serving as a study end-point.Statistical models are often used to predict the probability that an individual with a given set of risk factors will experience a health outcome, usually termed an "event" [6]. In PAH, it is widely agreed that a

Right atrial to left atrial volume index ratio is associated with increased mortality in patients with pulmonary hypertension

“…Despite increases in the general awareness and detection of PH, survival and outcomes remain poor across the spectrum of PH groups . Furthermore, risk assessment remains a challenge .…”

“…Despite increases in the general awareness and detection of PH, 12 survival and outcomes remain poor across the spectrum of PH groups. [13][14][15][16] Furthermore, risk assessment remains a challenge. 12 Robust risk assessment models allow clinicians to determine prognosis, monitor disease progression, and treatment response to therapy.…”

Risk Assessment in Pulmonary Arterial Hypertension Patients: The Long and Short of it

“…It is widely agreed and strongly encouraged that risk assessment in PAH be performed, utilizing the full spectrum of available risk-related markers. 55,69 The ideal risk assessment tool, particularly in a progressive and uniformly fatal disease like PAH, must hold to certain tenets. It has to be easy to use; applicable, with good predictive ability at any time in a disease course, whether that patient is newly or previously diagnosed; be equally predictive whether used at baseline or at follow-up; be applicable to all Group 1 PAH etiological subgroups; be informed by the most recent data available even if testing was not contemporaneous; retain utility when some data points or risk parameters are missing; and be dynamic (ie, capable of capturing change over time and changes in overall risk, which are reflective of changes in prognosis).…”

“…To be statistically sound, the risk assessment tool must have good discrimination and calibration; be derived from sizable well-defined cohorts; be composed of risk parameters (and corresponding cut points) that are evidence-based rather than expert opinion-derived; incorporate "weighting" of the various parameters; and be validated internally and externally. 69 The National Institutes of Health (NIH) registry equation, 6 reappraised in later work from the Pulmonary Hypertension Connection (PHC) registry, 70 the Scottish Composite Score, 28 as well as those from the larger French Pulmonary Arterial Hypertension Network (FPHN ItinérAIR-HTAP) registry 71,72 and REVEAL (USA) 8,20,70,73 have collectively informed current treatment guidelines by facilitating the creation of the current European Society of Cardiology (ESC)/ European Respiratory Society (ERS) risk table as well as the recommendations regarding periodic assessment of patient FC, BNP level, 6MWD, and right heart catheterization to stratify risk. 4 Data from these registries were used to derive equations and tools that predict survival for patients with PAH.…”

Retrospective Validation of the REVEAL 2.0 Risk Score With the Australian and New Zealand Pulmonary Hypertension Registry Cohort

“…14,15 Risk models differ significantly in terms of included parameters, and consensus on the optimal combination of predictive variables has not been reached. [15][16][17] External validation in populations separate from the derivation cohort is necessary to determine a model's broad applicability. 18,19 However, this process is challenging in rare diseases such as PAH because of differences in registry cohort enrollment criteria, time periods, followup patterns, and treatment.…”