6967 Simple Prediction Model for Vitamin D Deficiency in Women with Osteoporosis or Risk Factors for Osteoporosis
Authors: Mullikapipat Tidaporn , Dumrongwongsuwinai Natee , Vallibhakara Orawin , Rattanasiri Sasivimol , Arj-ong Vallibhakara Sakda , Wajanavisit Wiwat , Ongphiphadhanakul Boonsong , Nimitphong Hataikarn 
Published: October 2024

Disclosure: T. Mullikapipat: None. N. Dumrongwongsuwinai: None. O. Vallibhakara: None. S. Rattanasiri: None. S. Vallibhakara: None. W. Wajanavisit: None. B. Ongphiphadhanakul: None. H. Nimitphong: None.

Background: People at high risk of vitamin D deficiency, such as those at risk of osteoporosis, should have their 25-hydroxyvitamin D [25(OH)D] levels assessed. The availability and cost of 25(OH)D measurement in Thailand are both restricted. The objective of this study was to create a model for predicting vitamin D deficiency in women with osteoporosis or risk factors for osteoporosis. Methods: This was a cross-sectional study of women with or at risk of osteoporosis who visited Ramathibodi Hospital's outpatient clinics (medicine, obstetrics and gynecology, and orthopedic) between August 2016 and August 2019. All participants were assessed their 25(OH)D using a chemiluminescent assay (Liaison, DiaSorin Inc.; Stillwater, MN). Each participant completed a questionnaire to determine vitamin D intake through supplementation and sun exposure, as well as the existence of factors impacting vitamin D status. Vitamin D deficiency was defined as 25(OH)D levels less than 30 ng/mL. Logistic regression analyses were used to examine the predictors of vitamin D deficiency. In the vitamin D deficiency prediction model, odds ratios (OR) were converted into simple scores. The sensitivity, specificity, and positive and negative predictive values of the different cutoffs in the total risk score were calculated and used to establish the optimum cutoff for those at high risk of vitamin D deficiency. Internal validation on this model was carried out using a bootstrap resampling method of 1000. Results: Sixty percent of participants had vitamin D deficiency. The final model (n=488) for the prediction of vitamin D deficiency consisted of BMI ≥ 25 kg/m2 [OR 1.15, 95% confidence interval(CI) 0.99-2.30), no exercise (OR 1.59, 95% CI 1.02-2.49), exercise 1-2 times/week (OR 1.40, 95% CI 0.79-2.46), sunlight exposure &lt; 15 minutes/day (OR 1.70, 95% CI 1.04-2.78), no vitamin D supplementation (OR 8.76, 95% CI 5.02-15.28), and vitamin D supplementation ≤ 20,000 IU/day (OR 2.31, 95% CI 1.34-3.96). The area under the curve was 0.747. At a cutoff of 6.6 in total risk score (range 4-13.6), the model predicted vitamin D deficiency with a sensitivity of 71.9 and a specificity of 65.3. The internal validation by Bootstrap revealed a ROC of 0.752 (95%CI 0.751-0.754). Conclusions: In women at risk of osteoporosis, a simple questionnaire can uncover variables related to vitamin D deficiency. The risk score might be used in clinical practice to identify individuals who could benefit from vitamin D supplementation without requiring 25(OH)D levels to be measured. However, external validation in a different cohort is required before implementation.

Presentation: 6/2/2024