Front Neurol. 2020 Jan 31;11:37. doi: 10.3389/fneur.2020.00037. eCollection 2020.
Kim C1,2, Lee SH1, Lim JS3, Kim Y4, Jang MU5, Oh MS3, Jung S6, Lee JH4, Yu KH3, Lee BC3.
Machine learning was used to find the most relevant variable - NIHSS (initial indication of stroke severity)
NIHSS = (National Institute of Health Stroke Scale
Best model adjustment found by Machine Learning was NIHSS * (1+ Vitamin D)
Plain English – If you have a "bad" stroke you especially need to add Vitamin D
Note by founder of VitaminDWiki
This could have been found 30 years ago by Machine Learning.
I had proposed giving a talk at the Vitamin D Workshop in 2019 on what modern ML can do
but it was rejected
Poor Ischemic stroke outcome (END) 2.6 X worse if low vitamin D – March 2019
Stroke mortality 3X worse among seniors with less than 26 ng of vitamin D – June 2014
Stroke incidence not associated with low Vitamin D (but stroke outcome is) – Aug 2019
- Stroke risks increased if low Vitamin D: Death 3.6 X, recurrence 5.5 X – Meta-analysis Nov 2019
- Ischemic Stroke risk reduced by 2.5 if have good level of vitamin D – meta-analysis Feb 2018
- Vitamin D associated with 50 percent less ischemic stroke – meta-analysis Aug 2012
- Cerebrovascular disease 40 percent less likely if high level of vitamin D – meta-analysis Sept 2012
- 50 percent fewer strokes with vitamin D, even though ignored dose size – meta-analysis March 2012
- Improved recovery from ischemic stroke with Vitamin D (300,000 IU injection) – RCT June 2018
- Ischaemic stroke – Vitamin D doubled survival (Injection and 60,000 IU per month) – RCT Aug 2016
- Model 1 was adjusted for age and sex.
- Model 2 included variables in model 1 plus NIHSS and NIHSS*25-hydroxyvitamin D deficiency interaction term.
- Model 3 included variables in model 2 plus stroke subtype (TOAST classification) and intravenous thrombolysis.
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.