Patient specific modelling and optimization of the Dialysis therapy planning

Abstract—Health conditions and quality of life of uremic patients undergoing dialysis could be improved by a specific tailoring of the haemodialysis (HD) treatment. This research was aimed at optimising and customising the HD therapy in order to improve the patients’ clinical outcome. Two main goals have been pursued in order to reduce the clinical intra HD patient distress and the medium to long term HD related cardiovascular dysfunctions. To achieve the first goal a patient-specific compartmental model of the transport phenomena taking place in the patient during the therapy has been developed, based on clinical data ad hoc acquired. Three parameters, related to mass transfer at the cell membrane, capillary wall permeability and thickness of the protein layer inside the filters, were used to tune the model. The comparison with the clinical data points out the model ability in describing and predicting the patient-specific response to HD with deviations always lower than 10%. The model allows also the evaluation of the effects of different settings in terms of catabolites removal efficiency. The analysis of the clinical data also has allowed the identification of two indexes correlated with the intra-HD cardiac instabilities: HI allowing the evaluation of the patient’s proneness to hypotension; PRI allowing the study of plasmarefilling dynamics. To achieve the second goal, a hybrid model of the cardiovascular system has been developed, based on clinical data of 20 patients waiting for the creation of the AVF and then monitored after 10 days, 3 months, and 1 year from the fistula tailoring. Here too, three parameters related to peripheral vascular resistance (ξ), cardiac stiffness (q) and contractility (cc) were used to adapt the model to each patient. The model allowed the investigation of the alterations induced by the presence of the AVF both in the short and the medium-long period in terms of cardiac work, power, q, cc and ξ. Summarizing, the developed models and the calculated indexes help the customization of HD therapy and the reduction of related co-morbidity. Based on these outputs the treatment can be planned with more accuracy, bettering patients’ quality of life and limiting cardiac overload.