Raymond Benza and his team have published a state of the art algorithm for application in PAH. See below for a brief experpt and full PDF of the document.
Aim 2: Derive an expert-knowledge model for diagnosis and treatment of PAH.
2.1: Expert knowledge derived from existing documented best practices
Guidelines rom the European Respiratory Society have been identified to incorporate in the myPHORA application. Organization of information from these guidelines into a logical-dependency look-up table has been completed, with the table functionalized on the myPHORA clinician application (Figure 8A &B). As seen in Figure 8A (red arrow) this patient did not reach the tretment goal of attaining low risk status. The clinician is alerted to this and then directed through a second series of questions to determine if the patient has PAH as opposed to PVOD (Yellow arrow), which is an important distiction because the latter has no current treatment options except lung transplant. Once this is confirmed, the clinician is then asked whether the patient is treatment naïve or or already on therapy, as the tretment choices vary according to this status. In this case the patient is treatment naïve (Yellow arrow) and the software releases a series of treatment recommendations (both drug and follow-up times) based on the risk status for a treatment naïve patient (blue arrow). The clinician can also see where these treatment recommendations arise form by hitting the “i” bubble (Figure 8B, solid green line). In this case the patient has reached the treatment goal of low risk status and no new recommendations are given, indicated to remain on current therapy.