Using Artificial Intelligence to Evaluate Frailty and Atrial Fibrillation
Authors: Amina H Khalpey, PhD, Zeke Mendoza, Brynne Rozell BS, Zain Khalpey, MD, PhD
Atrial fibrillation (AF) is a common complication following cardiac surgery, with an incidence ranging from 10-30%. AF is associated with increased morbidity, including stroke, heart failure, and prolonged hospitalization, as well as increased mortality. Frailty is a state of vulnerability characterized by decreased reserve and resistance to stressors, leading to an increased risk of adverse health outcomes. Identifying frail patients at risk for developing AF following cardiac surgery is important in order to implement preventive measures and improve outcomes.
CT Scans Help Doctors Evaluate Frailty
One potential way to identify frail patients at risk for developing AF is through the use of machine learning algorithms applied to clinical data, such as computed tomography (CT) images and measures of physical function. CT images can provide valuable information about a patient’s anatomy and physiology, including the thickness of the pectoralis muscles and the quadriceps. Pectoralis muscle and quadricep strength and thickness have been found to be a predictor of frailty and adverse outcomes in older adults. In addition, measures of physical function, such as the 5-Meter Walk test, 6-Minute Walk test, Sit-to-Stand test, and Hand-Grip Strength test, can provide insight into a patient’s functional status and their ability to perform activities of daily living.
Utilizing Artificial Intelligence to Predict The Risk of Atrial Fibrillation
There are several machine learning algorithms that could be used to analyze CT images and physical function data in order to predict the risk of AF in frail patients following cardiac surgery. One example is the use of a decision tree algorithm, which involves dividing a dataset into smaller and smaller subsets based on the values of certain variables. The decision tree algorithm can identify patterns and relationships in the data that may not be immediately apparent, and can be used to make predictions about the likelihood of AF based on the thickness of the pectoralis/quadricep muscles and physical function measures. Another example is the use of a support vector machine (SVM) algorithm, which involves finding the hyperplane in a high-dimensional space that maximally separates different classes of data. SVMs have been shown to be effective at predicting AF in patients with heart disease, and could potentially be used to identify frail patients at risk for AF following cardiac surgery. A third example is the use of a random forest algorithm, which involves creating a large number of decision trees and then averaging the predictions made by each tree. Random forest algorithms are particularly useful for dealing with high-dimensional data and have been shown to be effective at predicting AF in patients with heart disease.
Applying Machine Learning Algorithms to CT Scans Will Improve Patient Outcomes
Overall, the use of machine learning algorithms applied to CT images and physical function data could be a useful tool for identifying frail patients at risk for developing AF following cardiac surgery. Further research is needed to determine the optimal machine learning algorithms and clinical data to use for this purpose, as well as to validate the accuracy of these predictions in clinical practice.