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Time for precision medicine in systemic sclerosis-associated pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is both a frequent and dismal complication of systemic sclerosis (SSc) [1]. A recent meta-analysis established that the pooled prevalence of PAH in SSc was 6.4% (95% CI 5–8.3%) and overall incidence 18.2 cases per 1000 person-years (95% CI 12–27.4) [2]. PAH is also one of the first causes of death in SSc, with a standardised mortality ratio of 5.27 (95% CI 2.98–9.31) [3] and a median survival of 3 years [1], which has not improved meaningfully for many years. Very consistently, prognosis of SSc-associated PAH (SSc-PAH) is worse than idiopathic PAH [4]. There are many explanations for this poor prognosis [5], including the severity and multisystemic condition of SSc itself [6, 7], heart involvement, frequent pulmonary veno-occlusive disease and late diagnosis (72–85% of patients have New York Heart Association (NYHA) functional class III or IV symptoms at diagnosis [8], leading to a worse response to treatment and poor outcome [9]. Integration of omics with clinical data through machine learning is the future of personalised and precision medicine in systemic sclerosis-associated pulmonary arterial hypertension https://bit.ly/3jsyBoV