Prediction Models and Scores in Pulmonary Hypertension: A Review

Abstract

Background:: Pulmonary arterial hypertension (PAH) is a serious disease with increased morbidity and mortality. The need for an individualized patient treatment approach necessitates the use of risk assessment in PAH patients. That may include a range of hemodynamic, clinical, imaging and biochemical parameters derived from clinical studies and registry data. Objective:: In the current systematic review, we summarize the available data on risk prognostic models and scores in PAH and we explore the possible concordance amongst different risk stratification tools in PAH. Methods:: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines aided the performance of this systematic review. Eligible studies were identified through a literature search in the electronic databases PubMed, Science Direct, Google Scholar and Cochrane with the use of various combinations of MeSH and non-MeSH terms, with a focus on PAH. Results:: Overall, 25 studies were included in the systematic review; out of them, 9 were studies deriving prognostic equations and risk scores and 16 were validating studies of an existing score. The majority of risk stratification scores use hemodynamic data for the assessment of prognosis, while others also include clinical and demographic variables in their equations. The risk discrimination in the overall PAH population was adequate, especially in differentiating the low versus high-risk patients, but their discrimination ability in the intermediate groups remained lower. Current ESC/ERS proposed risk stratification score utilizes a limited number of parameters with prognostic significance, whose prognostic ability has been validated in European patient populations. Conclusion:: Despite improvement in risk estimation of prognostic tools of the disease, PAH morbidity and mortality remain high, necessitating the need for the risk scores to undergo periodic re-evaluation and refinements to incorporate new data into predictors of disease progression and mortality and, thereby, maintain their clinical utility.