ArticleCritical care (London, England)2022
Score-based prediction model for severe vitamin D deficiency in patients with critical illness: development and validation.
Article in Critical care (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03639584 (Investigating the Prevalence of Deficiency of Vitamin D in Critically Ill Patients and Its Effects on the Prognosis), which is not on this map. Cited by 2 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Investigating the Prevalence of Deficiency of Vitamin D in Critically Ill Patients and Its Effects on the Prognosis
Who cites it
2 citing papers in PubMed.
- 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).Journal of health, population, and nutrition · 2025Article
- Vitamin D, Cellular Senescence and Chronic Kidney Diseases: What Is Missing in the Equation?Nutrients · 2023Review
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundSevere 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.
methodsA 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.
resultsThe 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.
conclusionsThis study developed a simple score-based model for predicting SVDD in patients with critical illness.
trial registrationClinicalTrials.gov protocol registration ID: NCT03639584. Date of registration: May 12, 2022.
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