Mathematical modelling to identify patients who should not undergo left ventricle remodelling surgery

Area of Science:

• Cardiovascular Surgery
• Mathematical Modeling
• Cardiac Physiology

Background:

• Left Ventricle Volume Reduction Surgery (LVVRS) is a procedure aimed at improving cardiac function.
• The outcomes of LVVRS can vary significantly depending on the underlying cardiac condition.
• Predictive modeling is crucial for patient selection and optimizing surgical outcomes.

Purpose of the Study:

• To develop a mathematical model to predict cardiac output after LVVRS.
• To differentiate surgical candidacy for LVVRS in patients with dilated cardiomyopathy (DCM) versus left ventricular aneurysm (LVA).
• To assess the impact of LVVRS on cardiac output in different cardiac pathologies.

Main Methods:

• Development of a mathematical model simulating cardiac output.
• Inclusion of patient parameters such as left ventricular diastolic volume (LVDV), ejection fraction (EF), and heart rate.
• Evaluation of two distinct clinical scenarios: DCM and LVA.

Main Results:

• In DCM, LVVRS led to a decrease in cardiac output (0.7 l/min) with unchanged EF.
• In LVA, LVVRS resulted in a significant increase in EF (to 56%) and doubled cardiac output.
• The model demonstrated that LVVRS is potentially hazardous in DCM but beneficial in LVA.

Conclusions:

• LVVRS carries significant risks for patients with DCM, as supported by registry data.
• LVVRS can lead to marked improvements in cardiac output for patients with LVA.
• Preoperative modeling of LV resection extent can predict postoperative cardiac output, aiding surgical decision-making.