Simulation as a preoperative planning approach in advanced heart failure patients. A retrospective clinical analysis

Abstract

Background: Modelling and simulation may become clinically applicable tools for detailed evaluation of the cardiovascular system and clinical decision-making to guide therapeutic intervention. Models based on pressure-volume relationship and zero-dimensional representation of the cardiovascular system may be a suitable choice given their simplicity and versatility. This approach has great potential for application in heart failure where the impact of left ventricular assist devices has played a significant role as a bridge to transplant and more recently as a long-term solution for non eligible candidates.

Results: We sought to investigate the value of simulation in the context of three heart failure patients with a view to predict or guide further management. CARDIOSIM^© was the software used for this purpose. The study was based on retrospective analysis of haemodynamic data previously discussed at a multidisciplinary meeting. The outcome of the simulations addressed the value of a more quantitative approach in the clinical decision process.

Conclusions: Although previous experience, co-morbidities and the risk of potentially fatal complications play a role in clinical decision-making, patient-specific modelling may become a daily approach for selection and optimisation of device-based treatment for heart failure patients. Willingness to adopt this integrated approach may be the key to further progress.

Keywords: CARDIOSIM©; Heart failure; Modelling; Simulation; Ventricular assist device.

Fig. 1

Electrical analogue model of the cardiovascular system. The systemic arterial section consists of three RLC elements representing the aortic (R_AT, L_AT and C_AT), thoracic (R_TT, L_TT and C_TT) and abdominal (R_ABT, L_ABT and C_ABT) tract respectively. Ras is the variable systemic peripheral resistance. The systemic venous section consists of two variable resistances (Rvs1 and Rvs2) and a compliance (Cvs). The main (small) pulmonary section is reproduced by a RLC element: Rpam, Lpam and Cpam (Rpas, Lpas and Cpas). The arteriole (capillary) bed behaviour is reproduced by a single resistance Rpar (Rpc). The pulmonary venous section consists of a compliance (Cvp) and a resistance (Rvp). Pt is the mean intrathoracic pressure

Fig. 2

Electrical analogue model of the Berlin Heart INCOR pump. Plv ad Pas are the left ventricular and systemic arterial pressures respectively. Input (output) pump cannula is modelled with a resistance Rvpi (Rvpo), a compliance Cvpi (Cvpo) and an inertance Lvpi (Lvpo). Qvad is the pump flow and Qvpi (Qvpo) is the input (output) cannula flow

Fig. 3

Screen output obtained using our simulator. The first step consists of simulating the “Admission” conditions of the second patient. Subsequently, LVAD assistance is applied. The upper window shows the simulated starting ventricular loop (A) and the ventricular loop obtained during LVAD assistance (B) in the left ventricular pressure–volume plane. The mean values (calculated during the cardiac cycle in the presence of LVAD assistance) of pressure, flow and HR are reported in the right column. Mean systolic and diastolic values are reported for the systemic arterial pressure (Pas ≡ BP). Pla is the mean left atrial pressure (Pla ≡ PCWP). Pra is the mean right atrial pressure. Mean pulmonary arterial pressure (Ppam ≡ PA), systemic venous pressure (Pvs) and pulmonary venous pressure (Pvp) are also shown. In the bottom column, mean left/right atrial input flow (Qlia/Qria), left/right ventricular input flow (Qli/Qri) and right ventricular output flow (Qro) assume the same value. The sum of the mean left ventricular output flow (Qlo) and the Qvad (LVAD flow) equals the flow into the circulatory network (Qlo + Qvad = Qlia = Qria = Qro = Qri = Qli). Finally, the left lower box reports the end-systolic volume (Ves ≡ ESV), the end-diastolic volume (Ved ≡ EDV), the stroke volume (SV) and the ejection fraction (EF) for both ventricles