Development and validation of prediction model for vitamin D deficiency in Chinese college students (a dynamic online nomogram predicting vitamin D deficiency for Chinese college students)

Abstract

Abstract Objective This study aims to develop a model for predicting vitamin D deficiency in Chinese college students using easily accessible clinical characteristics. Methods Data were derived from a cross-section study of the Vitamin D status in Chinese college students in September, 2020. Totally 1,667 freshmen from 26 provinces, autonomous districts or municipalities were analyzed. A LASSO regression model was used to select predictors and the significant factors were used to construct the logistic regression model expression and the nomogram. The prediction model was subjected to100 bootstrap resamples for internal validation to assess its predictive accuracy. Calibration and discrimination were used to assess the performance of the model. A dynamic online nomogram was conducted to make the model easy to use. The clinical use was evaluated by a decision curve analysis. Results Gender, region of original residence, milk and yogurt intake, puffed foods intake, outdoor activity duration, UV protection index and “taken calcium or vitamin D supplements within 3 months” were identified as significant predictors of vitamin D deficiency among Chinese college students. The model demonstrated good calibration with a 100 bootstraps analysis. The C-index was 0.677 and the bias-adjusted C-index was 0.668 in internal validation with 100 bootstrap resamples. The decision curve analysis showed a threshold probability between 0.5 and 0.8, using the model added more benefit than considering all patients are deficient or not deficient. Conclusions The performance of this vitamin D deficiency prediction model is commendable, and the dynamic online nomogram was proved to be a user-friendly screening tool for identifying high-risk subjects among Chinese college students. However, external validation is imperative to ensure the model’s generalizability.

Keywords

• Vitamin D
• Vitamin D deficiency
• Prediction model
• Dynamic nomogram
• Lasso regression
• Chinese