Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans

Abstract

Objective: Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques.

Methods: The Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway. In addition to the AAA diameter, FMD was systemically measured in these patients. A benchmark machine learning technique (non-linear Kernel support vector regression) was applied to predict future AAA growth in individual patients, using their baseline FMD and AAA diameter as input variables.

Results: Prospective growth data were recorded at 12 months (360 ± 49 days) in 94 patients. Of these, growth data were further recorded at 24 months (718 ± 81 days) in 79 patients. The average growth in AAA diameter was 3.4% at 12 months, and 2.8% per year at 24 months. The algorithm predicted the individual's AAA diameter to within 2 mm error in 85% and 71% of patients at 12 and 24 months.

Conclusions: The data highlight the utility of FMD as a biomarker for AAA and the value of machine learning techniques for AAA research in the new era of precision medicine.

Keywords: Abdominal aortic aneurysm; Aneurysm progression; Biomarker; Flow mediated dilatation; Machine learning.

Figure 1

Receiver operating curve (ROC) demonstrating the ability of the logistic regression model to discern future growth at predefined growth rate thresholds. ROC curves were first plotted using two variables (baseline FMD and AAA diameter) to analyse the performance of the generalised linear logistic regression model. The ROC curves are plotted with the threshold of “stable/no growth“ (A) (defined as growth ≤ 0mm/year) or “fast growth” (B) (defined as upper tertile of growth within the group, during the respective period) at both 12 and 24 months (blue and red line respectively).

Figure 2

Applying machine learning techniques for the prediction of AAA growth in individual patients. For the prediction of AAA diameter in individual patients at 12 (A) and 24 (B) months from baseline, non-linear kernel support vector regression (SVM) was applied using two features (FMD, AAA diameter), and hyperparameter optimisation using nested fivefold cross validations. The SVM method is a non-linear regression which can potentially improve the accuracy of predicting AAA diameter by considering non-linear functions of the input features. A 2 mm error margin was allowed because this is accepted technical variability between ultrasound diameter measurements in AAAs. The algorithm predicted the individual's AAA diameter to within a 2 mm error in 85% and 71% of patients at 12 and 24 months, respectively (with root mean square error of 1.7 and 2.4, respectively). Note. The figure includes only data points that are within the 2 mm error tolerance. Black cross, actual AAA diameter measured at 12 and 24 months; blue and red circles, machine predicted diameter at 12 (blue) and 24 (red) months.