Score-based prediction model for severe vitamin D deficiency in patients with critical illness: development and validation

 

by Yu-Ting Kuo, Li-Kuo Kuo, Chung-Wei Chen, Kuo-Ching Yuan, Chun-Hsien Fu, Ching-Tang Chiu, Yu-Chang Yeh, Jen-Hao Liu and Ming-Chieh Shih 

 

Critical Care volume 26, Article number: 394 (2022)

 

Background

Severe vitamin D deficiency (SVDD) dramatically increases the risks of mortality, infections, and many other diseases. Studies have reported higher prevalence of vitamin D deficiency in patients with critical illness than general population. This multicenter retrospective cohort study develops and validates a score-based model for predicting SVDD in patients with critical illness.

Methods

A total of 662 patients with critical illness were enrolled between October 2017 and July 2020. SVDD was defined as a serum 25(OH)D level of < 12 ng/mL (or 30 nmol/L). The data were divided into a derivation cohort and a validation cohort on the basis of date of enrollment. Multivariable logistic regression (MLR) was performed on the derivation cohort to generate a predictive model for SVDD. Additionally, a score-based calculator (the SVDD score) was designed on the basis of the MLR model. The model’s performance and calibration were tested using the validation cohort.

Results

The prevalence of SVDD was 16.3% and 21.7% in the derivation and validation cohorts, respectively. The MLR model consisted of eight predictors that were then included in the SVDD score. The SVDD score had an area under the receiver operating characteristic curve of 0.848 [95% confidence interval (CI) 0.781–0.914] and an area under the precision recall curve of 0.619 (95% CI 0.577–0.669) in the validation cohort.

Conclusions

This study developed a simple score-based model for predicting SVDD in patients with critical illness.