impact of 25-hydroxyvitamin d on the prognosis of acute ischemic stroke: machine learning approach
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ID: 141661
2020
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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.
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kim2020frontiersimpact
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| Authors | ;Chulho Kim;Chulho Kim;Sang-Hwa Lee;Jae-Sung Lim;Yerim Kim;Min Uk Jang;Mi Sun Oh;San Jung;Ju-Hun Lee;Kyung-Ho Yu;Byung-Chul Lee |
| Journal | journal of photochemistry and photobiology a: chemistry |
| Year | 2020 |
| DOI |
10.3389/fneur.2020.00037
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