Impact of 25-Hydroxyvitamin D on the Prognosis of Acute Ischemic Stroke: Machine Learning Approach

Abstract

Background and Purpose: Vitamin D is a predictor of poor outcome for cardiovascular disease. We evaluated whether serum 25-hydroxyvitamin D level was associated with poor outcome in patients with acute ischemic stroke (AIS) using machine learning approach. Materials and Methods: We studied a total of 328 patients within 7 days of AIS onset. Serum 25-hydroxyvitamin D level was obtained within 24 h of hospital admission. Poor outcome was defined as modified Rankin Scale score of 3-6. Logistic regression and extreme gradient boosting algorithm were used to assess association of 25-hydroxyvitamin D with poor outcome. Prediction performances were compared with area under ROC curve and F1 score. Results: Mean age of patients was 67.6 ± 13.3 years. Of 328 patients, 59.1% were men. Median 25-hydroxyvitamin D level was 10.4 (interquartile range, 7.1-14.8) ng/mL and 47.2% of patients were 25-hydroxyvitamin D-deficient (<10 ng/mL). Serum 25-hydroxyvitamin D deficiency was a predictor for poor outcome in multivariable logistic regression analysis (odds ratio, 3.38; 95% confidence interval, 1.24-9.18, p = 0.017). Stroke severity, age, and 25-hydroxyvitamin D level were also significant predictors in extreme gradient boosting classification algorithm. Performance of extreme gradient boosting algorithm was comparable to those of logistic regression (AUROC, 0.805 vs. 0.746, p = 0.11). Conclusions: 25-hydroxyvitamin D deficiency was highly prevalent in Korea and low 25-hydroxyvitamin D level was associated with poor outcome in patients with AIS. The machine learning approach of extreme gradient boosting was also useful to assess stroke prognosis along with logistic regression analysis.

Keywords: cerebral infarction; machine learning; outcome assessment; stroke; vitamin D.