Impact of blood pressure on coronary perfusion and valvular hemodynamics after aortic valve replacement.

OBJECTIVE: Our objective was to evaluate the impact of various blood pressures (BPs) on coronary perfusion and valvular hemodynamics following aortic valve replacement (AVR). BACKGROUND: Lower systolic and diastolic (SBP/DBP) pressures from the recommended optimal target range of SBP < 120-130 mmHg and DBP < 80 mmHg after AVR have been independently associated with increased cardiovascular and all-cause mortality. METHODS: The hemodynamic assessment of a 26 mm SAPIEN 3 transcatheter aortic valve (TAV), 29 mm Evolut R TAV, and 25 mm Magna Ease surgical aortic valve (SAV) was performed in a pulsed left heart simulator with varying SBP, DBP, and heart rate (HR) conditions (60 and 120 bpm) at 5 L/min cardiac output (CO). Average coronary flow (CF), effective orifice areas (EOAs), and valvulo-arterial impedance (Zva) were calculated. RESULTS: At HR of 60 bpm, at SBP < 120 mmHg and DBP < 60 mmHg, CF decreased below the physiological lower limit with several different valves. Zva and EOA were found to increase and decrease respectively with increasing SBP and DBP. The same results were found with an HR of 120 bpm. The trends of CF variation with BP were similar in all valves however the drop below the lower physiological CF limit was valve dependent. CONCLUSION: In a controlled in vitro system, with different aortic valve prostheses in place, CF decreased below the physiologic minimum when SBP and DBP were in the range targeted by blood pressure guidelines. Combined with recent observations from patients treated with AVR, these findings underscore the need for additional studies to identify the optimal BP in patients treated with AVR for AS.

A Preliminary Study on the Usage of a Data-Driven Probabilistic Approach to Predict Valve Performance Under Different Physiological Conditions.

Predicting potential complications after aortic valve replacement (AVR) is a crucial task that would help pre-planning procedures. The goal of this work is to generate data-driven models based on logistic regression, where the probability of developing transvalvular pressure gradient (DP) that exceeds 20 mmHg under different physiological conditions can be estimated without running extensive experimental or computational methods. The hemodynamic assessment of a 26 mm SAPIEN 3 transcatheter aortic valve and a 25 mm Magna Ease surgical aortic valve was performed under pulsatile conditions of a large range of systolic blood pressures (SBP; 100-180 mmHg), diastolic blood pressures (DBP; 40-100 mmHg), and heart rates of 60, 90 and 120 bpm. Logistic regression modeling was used to generate a predictive model for the probability of having a DP > 20 mmHg for both valves under different conditions. Experiments on different pressure conditions were conducted to compare the probabilities of the generated model and those obtained experimentally. To test the accuracy of the predictive model, the receiver operation characteristics curves were generated, and the areas under the curve (AUC) were calculated. The probabilistic predictive model of DP > 20 mmHg was generated with parameters specific to each valve. The AUC obtained for the SAPIEN 3 DP model was 0.9465 and that for Magna Ease was 0.9054 indicating a high model accuracy. Agreement between the DP probabilities obtained between experiments and predictive model was found. This model is a first step towards developing a larger statistical and data-driven model that can inform on certain valves reliability during AVR pre-procedural planning.