The article introduces a method called linear mixed-effects models to analyze data from cardiothoracic surgery outcomes. By using this method, researchers can better understand how patients' heart valve replacements are working over time. The study used data from patients who had surgery between 1986 and 2017 to show how this model can give more accurate results than traditional linear regression models. Linear mixed-effects models help researchers see how patients' heart function changes after surgery, which can lead to better treatment decisions in the future.