Machine Learning–Based Clinical Prediction Models for Acute Ischemic Stroke Based on Serum Xanthine Oxidase Levels

Abstract

Background and objectiveEarly prediction of the onset, progression and prognosis of acute ischemic stroke (AIS) is helpful for treatment decision-making and proactive management. Although several biomarkers have been found to predict the progression and prognosis of AIS, these biomarkers have not been widely used in routine clinical practice. Xanthine oxidase (XO) is a form of xanthine oxidoreductase (XOR), which is widespread in various organs of the human body and plays an important role in redox reactions and ischemia‒reperfusion injury. Our previous studies have shown that serum XO levels on admission have certain clinical predictive value for AIS. The purpose of this study was to utilize serum XO levels and clinical data to establish machine learning models for predicting the onset, progression and prognosis of AIS. Materials and MethodsWe enrolled 328 consecutive patients with AIS and 107 healthy controls from October 2020 to September 2021. Serum XO levels and stroke-related clinical data were collected. We established five machine learning models—the logistic regression (LR), support vector machine (SVM), decision tree, random forest, and K-nearest neighbor (KNN) models—to predict the onset, progression and prognosis of AIS. The area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, negative predictive value and positive predictive value were used to evaluate the predictive performance of each model. ResultsAmong the five machine learning models predicting AIS onset, the AUROC values of four prediction models were over 0.7, while that of the KNN model was lower (AUROC=0.6708, 95% CI 0.576-0.765). The LR model showed the best AUROC value (AUROC=0.9586, 95% CI 0.927-0.991). Although the five machine learning models showed relatively poor predictive value for the progression of AIS (all AUROCs < 0.7), the LR model still showed the highest AUROC value (AUROC=0.6543, 95% CI 0.453-0.856). We compared the value of five machine learning models in predicting the prognosis of AIS, and the LR model showed the best predictive value (AUROC=0.8124, 95% CI 0.715-0.910). ConclusionThe tested machine learning models based on serum levels of XO could predict the onset and prognosis of AIS. Among the five machine learning models, we found that the LR model showed the best predictive performance. Machine learning algorithms improve accuracy in the early diagnosis of AIS and can be used to make treatment decisions.