The advent of implantable monitoring devices promises to provide significant improvements to the treatment of pulmonary arterial hypertension (PAH), a life-threatening disease caused by narrowing of pulmonary arterioles which can lead to right heart failure. Continuous access to daily data, including pressure pulse waves, heart rate and cardiac output, offers the opportunity of developing digital twins DT which can uncover mechanistic changes within the patients’ physiology and provide novel biomarkers to track both disease progression and response to therapy. Taking a minimum viable product approach, we explore a new DT based on a lumped parameter model of the cardiovascular system, taking advantage of its reduced computational cost. The base model follows a standard structure and is composed of both systemic and pulmonary circulation (composed of three RLC units each), four cardiac chambers based on exponential passive behaviour and four cardiac valves which model the pressure-flow relation using the orifice law. To reduce the challenge of identifying all system parameters, we perform a global sensitivity analysis allowing us to narrow the number of parameters from 42 to 7, including pulmonary arterial resistance and compliance, systemic arterial resistance, passive and active ventricular material parameters. For personalising the model to available data, we propose a two-stage approach. First, we perform history matching (HM), an iterative approach of narrowing the parameter space. For each iteration, a sparse set of simulations is performed to train a surrogate model which is then evaluated on many data points to identify implausible parameter regions. New sparse data points are sampled from the remaining space, allowing us to retrain the surrogate model with higher accuracy and further narrow the search. After HM, we use MCMC to obtain an approximate posterior distribution. Future work will focus on how these parameters evolve over time, and whether they can be used to forecast the disease progression or the response to treatment.