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KMID : 1100120200270020111
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대한골대사학회지
2020 Volume.27 No. 2 p.111 ~ p.117
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Practical Vitamin D Supplementation Using Machine Learning
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Fugere Tyler
Chen Zhongning Jim
Makhoul Issam
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Abstract
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Background: Patients with breast cancer are at increased risk of developing osteoporosis. Maintaining normal levels of vitamin D may decrease the risk of osteoporosis, and vitamin D levels must be corrected in patients who develop osteoporosis before beginning bone modifying agents. Therefore, it is important to correct insufficient vitamin D levels in a timely manner. In clinical practice, current guidelines for replacement regimens often fail to rapidly correct vitamin D levels. The goal of this study was to review data in order to predict what replacement regimen(s) were most effective at repleting vitamin D levels.
Methods: For this retrospective cohort study, data was collected from medical records of 2,164 female patients with breast cancer with Institutional Review Board approval. Total level change per week was the primary outcome and was compared for the most commonly used vitamin D replacement regimens adjusted for age, race, body mass index, creatinine clearance, endocrine therapy, and initial level.
Results: Higher weekly doses of vitamin D supplementation had a more significant impact on the rate of correction compared to lower daily doses. Generalized linear model was used to develop an online calculator that predicts time to vitamin D level correction adjusted for significant patient characteristics for 5 common replacement regimens as well as no intervention.
Conclusions: When choosing a vitamin D replacement regimen for patients with vitamin D deficiency, we recommend clinicians use the online calculator to ensure that the chosen regimen will enable the patient to reach vitamin D sufficiency in a timely manner.
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KEYWORD
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Breast neoplasms, Osteoporosis, Vitamin D
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